CN110837925B - Urban waterlogging prediction method and device - Google Patents
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Abstract
The application provides a city waterlogging prediction method and device. The method comprises the following steps: carrying out hydrologic analysis on the city to be predicted to determine a first mapping relation between the water depositable quantity and the corresponding water depositable depth of each low-lying point in the city to be predicted and the percentage of the respective area of each low-lying point to the sum of the areas of all the low-lying points; carrying out big data analysis on historical water circulation monitoring data of the city to be predicted so as to determine a second mapping relation between regional waterlogging water accumulation amount and corresponding regional precipitation amount of the city to be predicted; according to the second mapping relation, predicting regional waterlogging water accumulation of rainfall on the scene; and distributing the regional waterlogging and water accumulation amount of the rainfall on the ground to each low-lying point according to the respective corresponding percentage of each low-lying point, and determining the respective water accumulation depth of each low-lying point in the rainfall on the ground according to the respective corresponding first mapping relation of each low-lying point. The monitoring of precipitation is realized to a certain extent, and the possibility of inducing urban waterlogging is predicted according to the precipitation amount.
Description
Technical Field
The application relates to the field of urban risk early warning, in particular to a method and a device for predicting urban waterlogging.
Background
In recent years, with the rapid development of urban cities, the scale of cities is gradually increased, but water safety problems are also caused, for example, in the case of extremely severe weather, and when the emergency capability of an urban emergency system is insufficient to cope with the extremely severe weather, the life and production of residents are seriously affected and disturbed, and even huge loss is caused to property of people.
At present, no quick and effective method is available for predicting urban inland inundation, and at present, a method for analyzing and early warning urban inland inundation is generally adopted, wherein theoretical model simulation is applied in combination with weather forecast or prediction is carried out according to experience. The method mainly utilizes an SWMM model (storm watermanagementmodel storm flood management model is a dynamic rainfall-runoff simulation model and is mainly used for simulating a single rainfall event or long-term water quantity and water quality simulation of a city) to analyze and early warn, or utilizes GIS to divide a non-structural irregular network, and performs risk analysis and early warn on urban waterlogging disasters by generalizing terrain and ground objects and combining with the simulation of an urban drainage system. The mode of predicting the pure mechanism model is limited by the long operation time of the model, inaccurate mastering of urban basic background data, limited by the self capability of weather professionals and other reasons, so that whether the urban waterlogging occurs or not is difficult to be predicted scientifically, accurately and effectively; the method for carrying out the speculation by the meteorological professional according to the experience knowledge is less persuasive, and the speculation result is less likely to ensure the accuracy.
Disclosure of Invention
The application provides a method and a device for predicting urban waterlogging, and mainly aims to monitor rainfall to a certain extent and predict the possibility of inducing urban waterlogging according to the rainfall.
One aspect of the application provides a city waterlogging prediction method, comprising the following steps:
carrying out hydrologic analysis on underlying surface topographic data of a city to be predicted so as to determine a first mapping relation between the water depositable quantity and the corresponding water depositable depth of each low-lying point in the city to be predicted and the percentage of the respective area of each low-lying point to the sum of the areas of all the low-lying points;
performing big data analysis on the historical water circulation monitoring data of the city to be predicted so as to determine a second mapping relation between the regional waterlogging water accumulation amount of the city to be predicted and the corresponding regional precipitation amount;
according to the second mapping relation, predicting regional waterlogging water accumulation of rainfall on the field;
and distributing the regional waterlogging and water accumulation amount of the rainfall to each low-lying point according to the respective corresponding percentage of each low-lying point, and determining the respective water accumulation depth of each low-lying point in the rainfall according to the respective corresponding first mapping relation of each low-lying point.
Optionally, the step of performing big data analysis on the historical water circulation monitoring data of the city to be predicted to determine a second mapping relationship between the regional waterlogging water volume of the city to be predicted and the corresponding regional precipitation volume includes:
constructing an estimation function of the amount of precipitation which can be carried by the corresponding region of the city to be predicted:
B(m)＝E(m)+F(m)+V 3 (m)+V 4 (m)+V 5 (m)+V 6 (m)
wherein B (m) represents the area capable of bearing the precipitation function, and m represents the precipitation field; e (m) represents a zone total evaporation capacity function; f (m) represents a total soil infiltration function of the region; v (V) 3 (m) represents a regional depression water yield function; v (V) 4 (m) represents a regional water body water storage function; v (V) 5 (m) represents a regional pipe space water storage function; v (V) 6 (m) represents an off-zone displacement function;
weighting coefficients are given to each independent variable of the estimation function of the area bearing precipitation:
B(m)＝θ 0 +θ 1 E(m)+θ 2 F(m)+θ 3 V 3 (m)+θ 4 V 4 (m)+θ 5 V 5 (m)
+θ 6 V 6 (m)
wherein θ 0 ，θ 1 ，θ 2 …θ 6 The weight coefficient corresponding to each independent variable of the function B (m);
matching an estimated function of the region carrying precipitation given with a weight coefficient with a function library in a big data regression analysis statistical model, and carrying out regression analysis to determine an approximate function B' (m) of the region carrying precipitation;
constructing an estimation function of the regional waterlogging water accumulation according to the approximation function B' (m):
W(m)＝P(m)-B'(m)
wherein W (m) represents a regional waterlogging water yield function, and P (m) represents a regional precipitation function;
giving weight coefficients to each independent variable of the estimation function of the regional waterlogging water accumulation;
W(m)＝δ 0 +δ 1 P(m)-δ 2 B(m)
wherein delta 0 ，δ 1 ，δ 2 The corresponding weight coefficient of each independent variable of the bit function W (m);
matching the estimation function of the regional waterlogging water yield given with the weight coefficient with a function library in a big data regression analysis statistical model, and carrying out regression analysis to determine an approximate function W' (m) of the regional waterlogging water yield: w '(m) =f (P (m)), the approximation function W' (m) is a second mapping relationship between the regional waterlogging water accumulation amount and the regional precipitation amount.
Optionally, the method further comprises:
performing hydrologic analysis on each low-lying point to determine a warning line of each low-lying point;
judging whether the ponding elevation of each low-lying point in the field rainfall is higher than the corresponding warning line;
screening out low-lying points with accumulated water elevations higher than corresponding warning lines in the field rainfall, and marking the screened low-lying points as low-lying points to be early-warned;
and carrying out early warning on the low-lying point to be early-warned.
Optionally, the step of pre-warning the low-lying point to be pre-warned includes:
performing hydrologic analysis on the low-lying point to be pre-warned to determine the geographic position of the low-lying point to be pre-warned;
marking in a GIS map according to the geographic position of the low-lying point to be pre-warned, and outputting the corresponding position coordinates and pre-warning information of the low-lying point to be pre-warned.
Optionally, the step of pre-warning the low-lying point to be pre-warned includes:
determining corresponding early warning levels of the low-lying points to be early warned according to differences between the ponding depths of the low-lying points to be early warned in the rainfall and corresponding warning lines;
and outputting early warning information which accords with the corresponding early warning level to the low-lying point to be early warned.
In another aspect of the present application, there is provided an urban waterlogging prediction apparatus, comprising:
the first analysis module is used for carrying out hydrologic analysis on the underlying surface topographic data of the city to be predicted so as to determine a first mapping relation between the water depositable quantity and the corresponding water depositable depth of each low-lying point in the city to be predicted and the percentage of the respective area of each low-lying point to the sum of the areas of all the low-lying points;
the second analysis module is used for carrying out big data analysis on the historical water circulation monitoring data of the city to be predicted so as to determine a second mapping relation between the regional waterlogging water accumulation amount of the city to be predicted and the corresponding regional precipitation amount;
the prediction module is used for predicting regional waterlogging water accumulation of the rainfall according to the second mapping relation;
the distribution determining module is used for distributing the regional waterlogging and water accumulation amount of the rainfall on the field to each low-lying point according to the corresponding percentage of each low-lying point, and determining the water accumulation depth of each low-lying point in the rainfall on the field according to the corresponding first mapping relation of each low-lying point.
Optionally, the second analysis module includes:
the first construction unit is used for constructing an estimation function of the loadable precipitation of the corresponding region of the city to be predicted:
B(m)＝E(m)+F(m)+V 3 (m)+V 4 (m)+V 5 (m)+V 6 (m)
wherein B (m) represents the area capable of bearing the precipitation function, and m represents the precipitation field; e (m) represents a zone total evaporation capacity function; f (m) represents a total soil infiltration function of the region; v (V) 3 (m) represents a regional depression water yield function; v (V) 4 (m) represents a regional water body water storage function; v (V) 5 (m) represents a regional pipe space water storage function; v (V) 6 (m) represents an off-zone displacement function;
a first assignment unit, configured to assign weight coefficients to each argument of an estimation function of the area loadable precipitation:
B(m)＝θ 0 +θ 1 E(m)+θ 2 F(m)+θ 3 V 3 (m)+θ 4 V 4 (m)+θ 5 V 5 (m)
+θ 6 V 6 (m)
wherein θ 0 ，θ 1 ，θ 2 …θ 6 The weight coefficient corresponding to each independent variable of the function B (m);
the first analysis unit is used for matching the estimation function of the regional loadable precipitation given with the weight coefficient with a function library in the big data regression analysis statistical model, and carrying out regression analysis to determine an approximate function B' (m) of the regional loadable precipitation;
a second construction unit, configured to construct an estimation function of the area waterlogging water accumulation according to the approximation function B' (m):
W(m)＝P(m)-B'(m)
wherein W (m) represents a regional waterlogging water yield function, and P (m) represents a regional precipitation function;
the second assignment unit is used for assigning weight coefficients to each independent variable of the estimation function of the regional waterlogging water accumulation;
W(m)＝δ 0 +δ 1 P(m)-δ 2 B(m)
wherein delta 0 ，δ 1 ，δ 2 The corresponding weight coefficient of each independent variable of the bit function W (m);
the second analysis unit is used for matching the estimation function of the regional waterlogging water yield given with the weight coefficient with a function library in the big data regression analysis statistical model, and carrying out regression analysis to determine an approximate function W' (m) of the regional waterlogging water yield: w '(m) =f (P (m)), the approximation function W' (m) is a second mapping relationship between the regional waterlogging water accumulation amount and the regional precipitation amount.
Optionally, the apparatus further comprises:
the first determining module is used for carrying out hydrologic analysis on each low-lying point so as to determine the warning line of each low-lying point;
the judging module is used for judging whether the ponding elevation of each low-lying point in the field rainfall is higher than the corresponding warning line;
the screening module is used for screening out low-lying points with the accumulated water elevation higher than the corresponding warning line in the field rainfall, and marking the screened low-lying points as low-lying points to be early-warned;
and the early warning module is used for carrying out early warning on the low-lying points to be early-warned.
Optionally, the early warning module includes:
the first determining unit is used for carrying out hydrologic analysis on the low-lying point to be pre-warned so as to determine the geographic position of the low-lying point to be pre-warned;
the first output unit is used for marking in the GIS map according to the geographic position of the low-lying point to be pre-warned and outputting the corresponding position coordinates and pre-warning information of the low-lying point to be pre-warned.
Optionally, the early warning module includes:
the second determining unit is used for determining corresponding early warning levels of the low-lying points to be early warned according to differences between the ponding depths of the low-lying points to be early warned in the rainfall and the corresponding warning lines;
and the second output unit is used for outputting early warning information which accords with the corresponding early warning level to the low-lying point to be early warned.
Compared with the prior art, the application has the following advantages:
determining a first mapping relation between the water depositable quantity and the corresponding water depositable depth of each low-lying point in the city to be predicted and the percentage of the area of each low-lying point to the sum of the areas of all the low-lying points; determining a second mapping relation between the regional waterlogging water accumulation amount of the city to be predicted and the corresponding regional precipitation amount through historical water circulation monitoring data of the city to be predicted; when the field rainfall occurs, matching is carried out according to a second mapping relation obtained by big data analysis, and the waterlogging water accumulation amount of the corresponding area of the field rainfall is obtained; according to the percentage of the water accumulation amount of each low-lying point to the total water accumulation amount of all the low-lying points, the regional waterlogging water accumulation amount of the rainfall is distributed to each low-lying point, and then the water accumulation depth of each low-lying point is determined according to a first mapping relation, so that monitoring of the rainfall is realized, and the possibility of inducing urban waterlogging is predicted according to the water accumulation amount. Compared with the related art, the method for predicting whether the city is waterlogged or not by adopting SWMM model or GIS analysis or the method for predicting by weather professionals according to experience knowledge can be used for predicting whether the city is waterlogged or not more effectively.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
Drawings
FIG. 1 is a flow chart of a city waterlogging prediction method provided by an embodiment of the application;
fig. 2 is a schematic diagram of an urban waterlogging prediction device according to an embodiment of the present application.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description.
Referring to fig. 1, there is shown a city waterlogging prediction method of the present application, comprising:
s101, hydrologic analysis is carried out on underlying surface topographic data of a city to be predicted so as to determine a first mapping relation between water depositable quantity and corresponding water depositable depth of each low-lying point in the city to be predicted and the percentage of the area of each low-lying point to the sum of the areas of all the low-lying points.
Each city has own underlying surface topography data, hydrologic analysis is carried out on the underlying surface topography data, and easily ponding and potential ponding areas in the city to be predicted, namely low-lying points, are searched and identified. The process of searching and identifying low-lying points is a process of solving the optimal solution of the intelligent computing search problem. For example, a tabu search algorithm is adopted for search and identification, and the specific steps are as follows:
s1011, dividing the urban DEM elevation topography data to be predicted into blocks, randomly selecting elevation data of a point in the divided data as an initial solution x, and setting a tabu table as empty.
The DEM elevation data is a small square divided according to longitude and latitude coordinates, and corresponds to altitude data of coordinate points. The DEM elevation data is subjected to blocking processing according to longitude and latitude coordinates, and the number and the size of the blocking can be adjusted according to the accuracy and the requirement of the DEM elevation data. After the DEM elevation data are segmented, parallel calculation is carried out on each block of DEM elevation data by using a tabu search solving algorithm.
S1012, judging whether points of the terrain elevation data in the city to be predicted meet the termination condition; if yes, go to step S1013; if not, step S1014 is performed.
S1013, the identified depression point is output, and the search is ended.
S1014, solving all neighborhood solutions by using the partitioned neighborhood of the current solution of the terrain elevation data of the city to be predicted, namely solving the minimum value of the elevation data, and determining the candidate solution in the candidate solution set.
S1015, judging whether a candidate solution of the topographic elevation data of the city to be predicted meets a scofflaw; if yes, go to step S1016; if not, step S1017 is performed.
S1016, updating the solution meeting the scofflaw into a tabu list; and step S1012 is performed again until all the low-lying points are identified.
For example, replacing x with a candidate solution satisfying the scofflaw, that is, the determined optimal solution y of the topography elevation of the low-lying point, to be a new current solution, that is, x=y, then replacing the topography elevation of the point which enters the tabu table earliest in the tabu table with the topography elevation of the low-lying point corresponding to y as the point of the tabu object, and judging again whether the point of the topography elevation data of the city to be predicted satisfies the termination condition.
S1017, judging tabu attributes in the candidate solutions, selecting an optimal solution in a candidate solution set corresponding to the topographic elevation of the low-lying point as a new current solution, and replacing the topographic elevation corresponding to the new current solution as an object to replace a topographic elevation object which enters the tabu table earliest; and step S1012 is performed again until all the low-lying points are identified.
When all the low-lying points of the city to be predicted are identified, corresponding hydrological data of each low-lying point, such as geographic positions of each low-lying point, corresponding coordinate information, water depositable quantity, water depositable depth and the like, are acquired.
And determining a first mapping relationship between the water depositable quantity and the corresponding water accumulation depth of each low-lying point, for example, a relationship curve v=g (h) between the water depositable quantity and the water accumulation depth, according to the corresponding hydrological data of each low-lying point, and determining the percentage of the respective area of each low-lying point to the sum of the areas of all the low-lying points.
S102, carrying out big data analysis on the historical water circulation monitoring data of the city to be predicted so as to determine a second mapping relation between the regional waterlogging water accumulation amount and the corresponding regional precipitation amount of the city to be predicted.
By performing big data analysis on historical water circulation monitoring data of the city to be predicted, for example, regression analysis can be selected as the basis of a big data statistical analysis model of the city to be predicted, and a determination model of the influence relationship between a dependent variable (regional waterlogging and water yield) and an independent variable (regional precipitation) is established through regression analysis, so that a second mapping relationship between the regional waterlogging and water yield of the city to be predicted and the corresponding regional precipitation, for example, an exponential curve, a quadratic curve, a logarithmic curve and the like obtained through regression analysis, is determined.
S103, predicting regional waterlogging water accumulation of field rainfall according to the second mapping relation.
After the second mapping relation between the regional waterlogging water accumulation amount and the regional precipitation amount is determined, the regional waterlogging water accumulation amount corresponding to the rainfall on the scene can be predicted and obtained according to the surface rainfall of the rainfall on the scene (namely the regional precipitation amount of the rainfall on the scene) and the mapping relation. The calculation of the surface rainfall related to the field rainfall can be confirmed according to a method for obtaining a least square fitting quadric surface in the prior art, and corresponding correction is carried out according to the time section of the field rainfall so as to correct errors.
S104, distributing the regional waterlogging water accumulation amount of the rainfall to each low-lying point according to the corresponding percentage of each low-lying point, and determining the respective water accumulation depth of each low-lying point in the rainfall according to the corresponding first mapping relation of each low-lying point.
After predicting and obtaining the regional waterlogging and water accumulating amount of the rainfall, distributing the regional waterlogging and water accumulating amount of the rainfall to each low-lying point according to the percentage of the respective area of each low-lying point to the sum of the areas of all the low-lying points determined in the step S101, and predicting the water accumulating amount possibly needed to be carried by each low-lying point in the rainfall; and then determining the water accumulation depth of each low-lying point in the rainfall according to the relation curve between the water accumulation amount and the water accumulation depth of each low-lying point determined in the step S101 and the water accumulation amount required to be carried in the rainfall.
It should be noted that, the steps S103 to S104 are dynamic processes, and each time the rainfall is performed dynamically in real time according to the rainfall time, so as to ensure the accuracy of prediction.
In a modern city, according to the hydrologic theory system, when rainfall occurs, the direction of rainfall in the area mainly includes the following items: regional evaporation, regional soil infiltration, regional depression ponding, regional water (river, lake, pond and the like) water storage, regional pipeline space storage and regional external drainage. The total amount of rainfall in the area is called area-supportable precipitation, and according to the water balance principle, it can be known that when the area precipitation exceeds the area-supportable precipitation, water accumulation is generated at low-lying points, and when the water accumulation depth exceeds a certain limit, waterlogging is caused. Constructing a water balance model of the city:
W＝P-B
wherein W represents the waterlogging water accumulation amount of the area, P represents the precipitation amount of the area, and B represents the bearing precipitation amount of the area;
when P-B is less than or equal to 0, namely W is less than or equal to 0, the waterlogging does not occur in the area;
on the contrary, when P-B > 0, i.e. W > 0, the value of W is the specific value of the water accumulation amount of the regional waterlogging.
Wherein the zone loadable precipitation B can be represented by the following model:
B＝E+F+V 3 +V 4 +V 5 +V 6
wherein E represents the total evaporation capacity of the area; f represents the total soil infiltration amount of the region; v (V) 3 Representing the water accumulation in the area depression; v (V) 4 Representing the water storage capacity of the regional water body; v (V) 5 Representing regional pipe space water storage; v (V) 6 Indicating the amount of off-zone drainage.
Therefore, the model can be utilized when large data analysis is performed on the historical water circulation monitoring data of the city to be predicted.
More specifically, in some embodiments, the step of performing big data analysis on the historical water circulation monitoring data of the city to be predicted to determine a second mapping relationship between the regional waterlogging water volume of the city to be predicted and the corresponding regional precipitation volume includes:
s1021, constructing an estimation function of the bearing precipitation of the corresponding region of the city to be predicted:
B(m)＝E(m)+F(m)+V 3 (m)+V 4 (m)+V 5 (m)+V 6 (m)
wherein B (m) represents the area capable of bearing the precipitation function, and m represents the precipitation field; e (m) represents a zone total evaporation capacity function; f (m) represents a total soil infiltration function of the region; v (V) 3 (m) represents a regional depression water yield function; v (V) 4 (m) represents a regional water body water storage function; v (V) 5 (m) represents a regional pipe space water storage function; v (V) 6 (m) represents an off-zone displacement function.
It is known that precipitation times, rainfall, etc. are different from season to season, and the total evaporation capacity of the area, the infiltration capacity of soil, etc. are also different. In order to ensure the validity of the prediction, it is necessary to perform the prediction in segments (time/quarter/month/day, etc.) according to different time/quarter, for example, in this embodiment, each month is divided into upper, middle and lower ten days by month, and the corresponding ten days of the month are counted as the last year rainfall, and each ten days is about 10 days (because each month is not 30 days, the error value of each individual ten days may be dynamically adjusted within one day, for example, 9 days or 11 days). And analyzing big data according to the historical water circulation monitoring data of each ten-day time. In this embodiment, the precipitation m corresponds to the data of the precipitation m counted in the last year of the corresponding to the month to be predicted, and the functional relation expressions of the precipitation directions, such as E (m), F (m), and V, can be obtained by adopting big data regression analysis 3 (m)、V 4 (m)、V 5 (m) and V 6 (m)。
However, the present application is not limited thereto, and the precipitation m may be dynamically adjusted appropriately according to the prediction accuracy, for example, the likelihood of occurrence of waterlogging and the occurrence of waterlogging in a certain season need to be predicted, and the precipitation m corresponds to the data of the precipitation counted from the season to the year, and the functional relation expression of each precipitation is corrected accordingly. Correspondingly, the precipitation m can also correspond to the data of the precipitation accumulated and counted from the past year in other periods.
After the data of the precipitation field times m of the period to be predicted and the functional relation expression of each precipitation direction are determined, the model of the regional loadable precipitation B can be used for constructing an estimation function of the regional precipitation corresponding to the city to be predicted.
S1022, assigning a weight coefficient to each argument of the estimation function of the precipitation amount that the region can carry:
B(m)＝θ 0 +θ 1 E(m)+θ 2 F(m)+θ 3 V 3 (m)+θ 4 V 4 (m)+θ 5 V 5 (m)+θ 6 V 6 (m)
wherein θ 0 ，θ 1 ，θ 2 …θ 6 And (3) corresponding weight coefficients for each independent variable of the function B (m).
In different time sections, the proportion of each precipitation is different, so that corresponding weight coefficients are required to be given to each independent variable of the function B (m) according to the period to be predicted.
S1023, matching the estimation function of the regional loadable precipitation given with the weight coefficient with a function library in a big data regression analysis statistical model, and carrying out regression analysis to determine an approximate function B' (m) of the regional loadable precipitation.
In the big data regression analysis statistical model, the big data platform Hadoop, ambari, HBase, pandas, hypertable and the like have complete function libraries for matching, such as linear functions, quadratic functions, exponential functions, logarithmic functions, S-shaped curves and the like. The big data regression analysis model requires a large amount of data as support, with emphasis on the data and the results produced by the data, and without attention to the specific process and cause of producing the results.
S1024, constructing an estimation function of the regional waterlogging water accumulation according to the approximation function B' (m):
W(m)＝P(m)-B'(m)
wherein W (m) represents a regional waterlogging water yield function, and P (m) represents a regional precipitation function.
After determining the approximate function B' (m) of the region supportable precipitation, an estimated function of the region precipitation may be constructed according to the water balance principle model.
S1025, giving weight coefficients to each independent variable of the estimation function of the regional waterlogging water accumulation;
W(m)＝δ 0 +δ 1 P(m)-δ 2 B(m)
wherein delta 0 ，δ 1 ，δ 2 The respective argument of the bit function W (m) corresponds to the weight coefficient.
The regional precipitation and the regional loadable precipitation may be affected by different time periods of the year corresponding to different periods to be predicted, and the duty ratio may be different, so that the regional precipitation and the regional loadable precipitation need to be corrected by giving corresponding weight coefficients according to the periods to be predicted.
S1026, matching the estimation function of the regional waterlogging water yield given with the weight coefficient with a function library in a big data regression analysis statistical model, and carrying out regression analysis to determine an approximate function W' (m) of the regional waterlogging water yield: w '(m) =f (P (m)), the approximation function W' (m) is a second mapping relationship between the regional waterlogging water accumulation amount and the regional precipitation amount.
The above regression analysis is similar to the regression analysis of the estimating function of the area bearing precipitation, and more specifically, please visit the related art, which is not a technical problem to be solved by the present application, and therefore will not be described in detail.
In some embodiments, the method further comprises:
and S105, performing hydrologic analysis on each low-lying point to determine the warning line of each low-lying point.
S106, judging whether the water accumulation elevation of each low-lying point in the field rainfall is higher than the corresponding warning line.
And S107, screening out low-lying points with the accumulated water elevation higher than the corresponding warning line in the field rainfall, and marking the screened low-lying points as low-lying points to be warned.
S108, early warning is carried out on the low-lying points to be early warned.
While hydrologic analysis is being performed, a tabu search algorithm may still be used, although other algorithms may be used to determine the warning line for each depression. After the respective ponding depths of the low-lying points in the predicted rainfall are determined through the embodiment, whether the low-lying points need to be warned or not is judged, so that people are reminded of the warning.
In some embodiments, the step of pre-warning the low-lying point to be pre-warned includes:
s1081, performing hydrologic analysis on the low-lying point to be pre-warned to determine the geographic position of the low-lying point to be pre-warned.
S1082, marking in the GIS map according to the geographic position of the low-lying point to be pre-warned, and outputting the corresponding position coordinates and pre-warning information of the low-lying point to be pre-warned.
The position coordinate information of the low-lying point to be pre-warned is marked in the GIS map and is output to the risk controllable center, so that the risk control center can start an emergency pre-warning scheme in time.
In some embodiments, the step of pre-warning the low-lying point to be pre-warned includes:
s1083, determining corresponding early warning levels of the low-lying points to be early warned according to differences between the ponding depths of the low-lying points to be early warned in the rainfall and the corresponding warning lines.
Each depression is located at a different location and has a different depth of water accumulation in the field precipitation. Normally, the depth of accumulated water is 0-10 mm, primary early warning is carried out, the accumulated water can be automatically resolved in a short period, and the influence on urban traffic and human life is basically avoided. When the depth of the accumulated water is 10-30 mm, the secondary early warning is carried out, the accumulated water automatically subsides in a certain period, and the influence on urban traffic and human life is small. When the depth of the accumulated water is 30-100 mm, three-level early warning is performed, the accumulated water cannot self-subside within a certain period, and the influence on urban traffic and human life is large. When the depth of the accumulated water is more than 100mm, the accumulated water can not be automatically resolved for a long time, so that huge influence is caused on urban traffic and human life, even accumulated water in local areas can be possibly caused, and the life and property safety of people is seriously threatened.
The early warning level can be conveniently determined according to the difference value between the water accumulation depth in the field rainfall and the corresponding warning line.
S1084, outputting early warning information which accords with the corresponding early warning level to the low-lying point to be early warned. The flexibility and timeliness of early warning are improved.
Compared with the prior art, the application has the following advantages:
determining a first mapping relation between the water depositable quantity of each low-lying point and the corresponding water depositable depth in the city to be predicted, and the percentage of the water depositable quantity of each low-lying point to the total water depositable quantity of all the low-lying points; determining a second mapping relation between the regional waterlogging water accumulation amount of the city to be predicted and the corresponding regional precipitation amount through historical water circulation monitoring data of the city to be predicted; when the field rainfall occurs, matching is carried out according to a second mapping relation obtained by big data analysis, and the waterlogging water accumulation amount of the corresponding area of the field rainfall is obtained; according to the percentage of the water accumulation amount of each low-lying point to the total water accumulation amount of all the low-lying points, the regional waterlogging water accumulation amount of the rainfall is distributed to each low-lying point, and then the water accumulation depth of each low-lying point is determined according to a first mapping relation, so that monitoring of the rainfall is realized, and the possibility of inducing urban waterlogging is predicted according to the water accumulation amount. Compared with the related art, the method for predicting whether the city is waterlogged or not by adopting SWMM model or GIS analysis or the method for predicting by weather professionals according to experience knowledge can be used for predicting whether the city is waterlogged or not more effectively.
Referring to fig. 2, in another aspect of the present application, there is provided an urban waterlogging prediction apparatus, including:
a first analysis module 201, configured to perform hydrologic analysis on underlying surface topography data of a city to be predicted, so as to determine a first mapping relationship between a water depositable amount of each low-lying point in the city to be predicted and a corresponding water depositable depth, and a percentage of the water depositable amount of each low-lying point in a total water depositable amount of all the low-lying points;
a second analysis module 202, configured to perform big data analysis on the historical water circulation monitoring data of the city to be predicted, so as to determine a second mapping relationship between the regional waterlogging water volume of the city to be predicted and the corresponding regional precipitation volume;
the prediction module 203 is configured to predict a regional waterlogging water accumulation amount of the rainfall according to the second mapping relationship;
the allocation determining module 204 is configured to allocate the water logging amount of the rainfall area to each low-lying point according to the respective corresponding percentage of each low-lying point, and determine the water logging depth of each low-lying point in the rainfall area according to the respective corresponding first mapping relationship of each low-lying point.
Optionally, the second analysis module 202 includes:
a first construction unit 2021 is configured to construct an estimation function of the amount of precipitation that can be carried by the corresponding region of the city to be predicted:
B(m)＝E(m)+F(m)+V 3 (m)+V 4 (m)+V 5 (m)+V 6 (m)
wherein B (m) represents the area capable of bearing the precipitation function, and m represents the precipitation field; e (m) represents a zone total evaporation capacity function; f (m) represents a total soil infiltration function of the region; v (V) 3 (m) represents a regional depression water yield function; v (V) 4 (m) represents a regional water body water storage function; v (V) 5 (m) represents a regional pipe space water storage function; v (V) 6 (m) represents an off-zone displacement function;
a first assigning unit 2022, configured to assign weight coefficients to the arguments of the estimation function of the area loadable precipitation:
B(m)＝θ 0 +θ 1 E(m)+θ 2 F(m)+θ 3 V 3 (m)+θ 4 V 4 (m)+θ 5 V 5 (m)+θ 6 V 6 (m)
wherein θ 0 ，θ 1 ，θ 2 …θ 6 The weight coefficient corresponding to each independent variable of the function B (m);
a first analysis unit 2023, configured to match the estimated function of the region supportable precipitation amount given with the weight coefficient with a function library in the big data regression analysis statistical model, and perform regression analysis to determine an approximate function B' (m) of the region supportable precipitation amount;
a second construction unit 2024, configured to construct an estimation function of the regional waterlogging water accumulation according to the approximation function B' (m):
W(m)＝P(m)-B'(m)
wherein W (m) represents a regional waterlogging water yield function, and P (m) represents a regional precipitation function;
a second assigning unit 2025, configured to assign weight coefficients to the independent variables of the estimation function of the regional waterlogging water accumulation;
W(m)＝δ 0 +δ 1 P(m)-δ 2 B(m)
wherein delta 0 ，δ 1 ，δ 2 The corresponding weight coefficient of each independent variable of the bit function W (m);
a second analysis unit 2026, configured to match the estimated function of the regional waterlogging water yield given with the weight coefficient with a function library in the big data regression analysis statistical model, and perform regression analysis to determine an approximate function W' (m) of the regional waterlogging water yield: w '(m) =f (P (m)), the approximation function W' (m) is a second mapping relationship between the regional waterlogging water accumulation amount and the regional precipitation amount.
Optionally, the apparatus further comprises:
a first determining module 205, configured to perform hydrologic analysis on each of the low-lying points, so as to determine a warning line of each of the low-lying points;
a judging module 206, configured to judge whether the water accumulation elevation of each low-lying point in the field precipitation is higher than the corresponding warning line;
the screening module 207 is configured to screen out low-lying points with a height of accumulated water higher than the corresponding warning line in the field rainfall, and mark the screened low-lying points as low-lying points to be pre-warned;
and the early warning module 208 is used for early warning the low-lying point to be early warned.
Optionally, the early warning module 208 includes:
the first determining unit 2081 is configured to perform hydrologic analysis on the low-lying point to be pre-warned, so as to determine a geographic position of the low-lying point to be pre-warned;
the first output unit 2082 is configured to mark in a GIS map according to the geographic position of the low-lying point to be pre-warned, and output the position coordinates and pre-warning information corresponding to the low-lying point to be pre-warned.
Optionally, the early warning module 208 includes:
the second determining unit 2083 is configured to determine, according to a difference between a water accumulation depth of each low-lying point to be pre-warned in the rainfall and a corresponding warning line, a corresponding pre-warning level of each low-lying point to be pre-warned;
the second output unit 2084 is configured to output, to the low-lying point to be pre-warned, pre-warning information that meets the corresponding pre-warning level.
For system embodiments, the description is relatively simple as it is substantially similar to method embodiments, and reference is made to the description of method embodiments for relevant points.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.
The above description of the urban waterlogging prediction method and device provided by the application applies specific examples to illustrate the principle and implementation of the application, and the above examples are only used for helping to understand the method and core ideas of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (8)
1. A method for predicting urban inland inundation, comprising:
carrying out hydrologic analysis on underlying surface topographic data of a city to be predicted so as to determine a first mapping relation between the water depositable quantity and the corresponding water depositable depth of each low-lying point in the city to be predicted and the percentage of the respective area of each low-lying point to the sum of the areas of all the low-lying points;
performing big data analysis on the historical water circulation monitoring data of the city to be predicted so as to determine a second mapping relation between the regional waterlogging water accumulation amount of the city to be predicted and the corresponding regional precipitation amount;
according to the second mapping relation, predicting regional waterlogging water accumulation of rainfall on the field;
according to the respective corresponding percentage of each low-lying point, distributing the regional waterlogging water accumulation amount of the rainfall on the field to each low-lying point, and determining the respective water accumulation depth of each low-lying point in the rainfall on the field according to the respective corresponding first mapping relation of each low-lying point;
the step of analyzing the historical water circulation monitoring data of the city to be predicted to determine a second mapping relationship between the regional waterlogging water accumulation amount and the corresponding regional precipitation amount of the city to be predicted comprises the following steps:
constructing an estimation function of the amount of precipitation which can be carried by the corresponding region of the city to be predicted:
B(m)＝E(m)+F(m)+V 3 (m)+V 4 (m)+V 5 (m)+V 6 (m)
wherein B (m) represents the area capable of bearing the precipitation function, and m represents the precipitation field; e (m) represents a zone total evaporation capacity function; f (m) represents a total soil infiltration function of the region; v (V) 3 (m) represents a regional depression water yield function; v (V) 4 (m) represents a regional water body water storage function; v (V) 5 (m) represents a regional pipe space water storage function; v (V) 6 (m) represents an off-zone displacement function;
weighting coefficients are given to each independent variable of the estimation function of the area bearing precipitation:
B(m)＝θ 0 +θ 1 E(m)+θ 2 F(m)+θ 3 V 3 (m)+θ 4 V 4 (m)+θ 5 V 5 (m)+θ 6 V 6 (m)
wherein θ 0 ，θ 1 ，θ 2 ...θ 6 The weight coefficient corresponding to each independent variable of the function B (m);
matching an estimated function of the region carrying precipitation given with a weight coefficient with a function library in a big data regression analysis statistical model, and carrying out regression analysis to determine an approximate function B' (m) of the region carrying precipitation;
constructing an estimation function of the regional waterlogging water accumulation according to the approximation function B' (m):
W(m)＝P(m)-B′(m)
wherein W (m) represents a regional waterlogging water yield function, and P (m) represents a regional precipitation function;
giving weight coefficients to each independent variable of the estimation function of the regional waterlogging water accumulation;
W(m)＝δ 0 +δ 1 P(m)-δ 2 B(m)
wherein delta 0 ，δ 1 ，δ 2 The corresponding weight coefficient of each independent variable of the bit function W (m);
matching the estimation function of the regional waterlogging water yield given with the weight coefficient with a function library in a big data regression analysis statistical model, and carrying out regression analysis to determine an approximate function W' (m) of the regional waterlogging water yield: w '(m) =f (P (m)), the approximation function W' (m) is a second mapping relationship between the regional waterlogging water accumulation amount and the regional precipitation amount.
2. The method according to claim 1, wherein the method further comprises:
performing hydrologic analysis on each low-lying point to determine a warning line of each low-lying point;
judging whether the ponding elevation of each low-lying point in the field rainfall is higher than the corresponding warning line;
screening out low-lying points with accumulated water elevations higher than corresponding warning lines in the field rainfall, and marking the screened low-lying points as low-lying points to be early-warned;
and carrying out early warning on the low-lying point to be early-warned.
3. The method of claim 2, wherein the step of pre-warning the depression to be pre-warned comprises:
performing hydrologic analysis on the low-lying point to be pre-warned to determine the geographic position of the low-lying point to be pre-warned;
marking in a GIS map according to the geographic position of the low-lying point to be pre-warned, and outputting the corresponding position coordinates and pre-warning information of the low-lying point to be pre-warned.
4. A method according to claim 2 or 3, wherein the step of pre-warning the depression to be pre-warned comprises:
determining corresponding early warning levels of the low-lying points to be early warned according to differences between the ponding depths of the low-lying points to be early warned in the rainfall and corresponding warning lines;
and outputting early warning information which accords with the corresponding early warning level to the low-lying point to be early warned.
5. A city water logging prediction apparatus, comprising:
the first analysis module is used for carrying out hydrologic analysis on the underlying surface topographic data of the city to be predicted so as to determine a first mapping relation between the water depositable quantity and the corresponding water depositable depth of each low-lying point in the city to be predicted and the percentage of the respective area of each low-lying point to the sum of the areas of all the low-lying points;
the second analysis module is used for carrying out big data analysis on the historical water circulation monitoring data of the city to be predicted so as to determine a second mapping relation between the regional waterlogging water accumulation amount of the city to be predicted and the corresponding regional precipitation amount;
the prediction module is used for predicting regional waterlogging water accumulation of the rainfall according to the second mapping relation;
the distribution determining module is used for distributing the regional waterlogging water accumulation amount of the rainfall on each low-lying point according to the corresponding percentage of each low-lying point, and determining the water accumulation depth of each low-lying point in the rainfall on the field according to the corresponding first mapping relation of each low-lying point;
the second analysis module includes:
the first construction unit is used for constructing an estimation function of the loadable precipitation of the corresponding region of the city to be predicted:
B(m)＝E(m)+F(m)+V 3 (m)+V 4 (m)+V 5 (m)+V 6 (m)
wherein B (m) represents the area capable of bearing the precipitation function, and m represents the precipitation field; e (m) represents a zone total evaporation capacity function; f (m) represents a total soil infiltration function of the region; v (V) 3 (m) represents a regional depression water yield function; v (V) 4 (m) represents a regional water body water storage function; v (V) 5 (m) represents a regional pipe space water storage function; v (V) 6 (m) represents an off-zone displacement function;
a first assignment unit, configured to assign weight coefficients to each argument of an estimation function of the area loadable precipitation:
B(m)＝θ 0 +θ 1 E(m)+θ 2 F(m)+θ 3 V 3 (m)+θ 4 V 4 (m)+θ 5 V 5 (m)+θ 6 V 6 (m)
wherein θ 0 ，θ 1 ，θ 2 ...θ 6 The weight coefficient corresponding to each independent variable of the function B (m);
the first analysis unit is used for matching the estimation function of the regional loadable precipitation given with the weight coefficient with a function library in the big data regression analysis statistical model, and carrying out regression analysis to determine an approximate function B' (m) of the regional loadable precipitation;
a second construction unit, configured to construct an estimation function of the area waterlogging water accumulation according to the approximation function B' (m):
W(m)＝P(m)-B′(m)
wherein W (m) represents a regional waterlogging water yield function, and P (m) represents a regional precipitation function;
the second assignment unit is used for assigning weight coefficients to each independent variable of the estimation function of the regional waterlogging water accumulation;
W(m)＝δ 0 +δ 1 P(m)-δ 2 B(m)
wherein delta 0 ，δ 1 ，δ 2 The corresponding weight coefficient of each independent variable of the bit function W (m);
the second analysis unit is used for matching the estimation function of the regional waterlogging water yield given with the weight coefficient with a function library in the big data regression analysis statistical model, and carrying out regression analysis to determine an approximate function W' (m) of the regional waterlogging water yield: w '(m) =f (P (m)), the approximation function W' (m) is a second mapping relationship between the regional waterlogging water accumulation amount and the regional precipitation amount.
6. The apparatus of claim 5, wherein the apparatus further comprises:
the first determining module is used for carrying out hydrologic analysis on each low-lying point so as to determine the warning line of each low-lying point;
the judging module is used for judging whether the ponding elevation of each low-lying point in the field rainfall is higher than the corresponding warning line;
the screening module is used for screening out low-lying points with the accumulated water elevation higher than the corresponding warning line in the field rainfall, and marking the screened low-lying points as low-lying points to be early-warned;
and the early warning module is used for carrying out early warning on the low-lying points to be early-warned.
7. The apparatus of claim 6, wherein the pre-warning module comprises:
the first determining unit is used for carrying out hydrologic analysis on the low-lying point to be pre-warned so as to determine the geographic position of the low-lying point to be pre-warned;
the first output unit is used for marking in the GIS map according to the geographic position of the low-lying point to be pre-warned and outputting the corresponding position coordinates and pre-warning information of the low-lying point to be pre-warned.
8. The apparatus of claim 6 or 7, wherein the pre-warning module comprises:
the second determining unit is used for determining corresponding early warning levels of the low-lying points to be early warned according to differences between the ponding depths of the low-lying points to be early warned in the rainfall and the corresponding warning lines;
and the second output unit is used for outputting early warning information which accords with the corresponding early warning level to the low-lying point to be early warned.
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Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111859707B (en) * | 2020-07-31 | 2023-06-16 | 中国地质大学（武汉） | Pipeline diameter determining method, urban waterlogging early warning method and device |
| CN112767217B (en) * | 2021-01-27 | 2021-11-30 | 武汉数洪云通科技有限公司 | Intelligent community management system based on Internet of things |
| CN113408211B (en) * | 2021-07-12 | 2022-02-11 | 中国水利水电科学研究院 | Urban inland inundation water accumulation point water accumulation process prediction method and system based on machine learning |
| CN113593191A (en) * | 2021-08-10 | 2021-11-02 | 安徽嘉拓信息科技有限公司 | Visual urban waterlogging monitoring and early warning system based on big data |
| CN115293469B (en) * | 2022-10-10 | 2022-12-27 | 山脉科技股份有限公司 | Urban flood control and drainage risk prediction method |
| CN116362419B (en) * | 2023-05-31 | 2023-08-04 | 聊城市科慧市政工程设计院有限公司 | Urban flood control early warning system and method |
| CN117198003A (en) * | 2023-09-19 | 2023-12-08 | 北京建筑大学 | Waterlogging risk early warning method and device, electronic equipment and storage medium |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103944946A (en) * | 2013-01-22 | 2014-07-23 | 中国水利水电科学研究院 | City inland inundation monitoring method and monitoring system based on Internet of Things |
| CN104658285A (en) * | 2015-02-06 | 2015-05-27 | 天津大学 | Intelligent traffic smoothing method in urban inland inundation |
| CN104778648A (en) * | 2015-05-06 | 2015-07-15 | 北京国电经纬工程技术有限公司 | Waterlogging warning system and method |
| CN105550803A (en) * | 2015-12-08 | 2016-05-04 | 哈尔滨航天恒星数据***科技有限公司 | Urban water-logging analysis method and urban water-logging analysis system |
| CN106295123A (en) * | 2016-07-27 | 2017-01-04 | 泰华智慧产业集团股份有限公司 | The computational methods of multi-source non-single depression surface pond state |
| CN106373070A (en) * | 2016-08-29 | 2017-02-01 | 西安理工大学 | Four-prevention method for responding to city rainstorm waterlogging |
| KR101741777B1 (en) * | 2016-01-27 | 2017-05-30 | (주)도명이엔지 | Method for applying damage functions while calculating flood damage of flooded areas by using GIS information |
| CN107543580A (en) * | 2016-09-30 | 2018-01-05 | 河南众联云科工程技术有限公司 | A kind of urban waterlogging monitoring early-warning system |
| JP2018028486A (en) * | 2016-08-18 | 2018-02-22 | 西日本高速道路エンジニアリング四国株式会社 | Method for quantitatively analyzing pothole generation risk in drainable pavement |
| CN108108892A (en) * | 2017-12-19 | 2018-06-01 | 南京信息工程大学 | A kind of flooded evaluation unit division methods of urban storm product based on Thiessen polygon |
| CN108182543A (en) * | 2018-01-17 | 2018-06-19 | 福建四创软件有限公司 | One kind becomes more meticulous grid waterlogging water logging forecasting procedure |
| CN108446464A (en) * | 2018-03-05 | 2018-08-24 | 重庆大学 | A method of utilizing the big drainage system of SWMM model constructions |
| CN109145317A (en) * | 2017-06-14 | 2019-01-04 | 浙江贵仁信息科技股份有限公司 | A kind of vertical coupled method, system and the equipment of earth's surface hydrodynamic model and underground pipe network model |
| CN109191358A (en) * | 2018-10-19 | 2019-01-11 | 中电科新型智慧城市研究院有限公司 | Urban Flood control prevention waterlogging monitoring commander's operating system |
| CN109657841A (en) * | 2018-11-27 | 2019-04-19 | 杭州师范大学 | A kind of urban rainstorm waterlogging depth of accumulated water extracting method |
| CN109726259A (en) * | 2018-12-27 | 2019-05-07 | 中冶京诚工程技术有限公司 | A kind of waste pipe-network design optimization system and method based on GIS technology |
| CN110298076A (en) * | 2019-05-27 | 2019-10-01 | 广州奥格智能科技有限公司 | A kind of urban waterlogging intelligent modeling and analysis method based on GIS and SWMM |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10147057B2 (en) * | 2014-08-13 | 2018-12-04 | Intermap Technologies Inc. | Systems and methods for flood zone modeling |
-
2019
- 2019-11-04 CN CN201911066929.3A patent/CN110837925B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103944946A (en) * | 2013-01-22 | 2014-07-23 | 中国水利水电科学研究院 | City inland inundation monitoring method and monitoring system based on Internet of Things |
| CN104658285A (en) * | 2015-02-06 | 2015-05-27 | 天津大学 | Intelligent traffic smoothing method in urban inland inundation |
| CN104778648A (en) * | 2015-05-06 | 2015-07-15 | 北京国电经纬工程技术有限公司 | Waterlogging warning system and method |
| CN105550803A (en) * | 2015-12-08 | 2016-05-04 | 哈尔滨航天恒星数据***科技有限公司 | Urban water-logging analysis method and urban water-logging analysis system |
| KR101741777B1 (en) * | 2016-01-27 | 2017-05-30 | (주)도명이엔지 | Method for applying damage functions while calculating flood damage of flooded areas by using GIS information |
| CN106295123A (en) * | 2016-07-27 | 2017-01-04 | 泰华智慧产业集团股份有限公司 | The computational methods of multi-source non-single depression surface pond state |
| JP2018028486A (en) * | 2016-08-18 | 2018-02-22 | 西日本高速道路エンジニアリング四国株式会社 | Method for quantitatively analyzing pothole generation risk in drainable pavement |
| CN106373070A (en) * | 2016-08-29 | 2017-02-01 | 西安理工大学 | Four-prevention method for responding to city rainstorm waterlogging |
| CN107543580A (en) * | 2016-09-30 | 2018-01-05 | 河南众联云科工程技术有限公司 | A kind of urban waterlogging monitoring early-warning system |
| CN109145317A (en) * | 2017-06-14 | 2019-01-04 | 浙江贵仁信息科技股份有限公司 | A kind of vertical coupled method, system and the equipment of earth's surface hydrodynamic model and underground pipe network model |
| CN108108892A (en) * | 2017-12-19 | 2018-06-01 | 南京信息工程大学 | A kind of flooded evaluation unit division methods of urban storm product based on Thiessen polygon |
| CN108182543A (en) * | 2018-01-17 | 2018-06-19 | 福建四创软件有限公司 | One kind becomes more meticulous grid waterlogging water logging forecasting procedure |
| CN108446464A (en) * | 2018-03-05 | 2018-08-24 | 重庆大学 | A method of utilizing the big drainage system of SWMM model constructions |
| CN109191358A (en) * | 2018-10-19 | 2019-01-11 | 中电科新型智慧城市研究院有限公司 | Urban Flood control prevention waterlogging monitoring commander's operating system |
| CN109657841A (en) * | 2018-11-27 | 2019-04-19 | 杭州师范大学 | A kind of urban rainstorm waterlogging depth of accumulated water extracting method |
| CN109726259A (en) * | 2018-12-27 | 2019-05-07 | 中冶京诚工程技术有限公司 | A kind of waste pipe-network design optimization system and method based on GIS technology |
| CN110298076A (en) * | 2019-05-27 | 2019-10-01 | 广州奥格智能科技有限公司 | A kind of urban waterlogging intelligent modeling and analysis method based on GIS and SWMM |
Non-Patent Citations (3)
| Title |
|---|
| 刘伟.城市暴雨地面积水量分析研究.《工程科技Ⅱ辑-建筑科学与工程》.2006,全文. * |
| 王斌.面向防汛应急的重庆市洼地积涝预测模型研究.《测绘科学》.2011,第36卷(第1期),113-115. * |
| 王静.城市洪涝仿真模型的改进与应用.《水利学报》.2010,第41卷(第12期),1393-1400. * |
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