CN110837925A - Urban waterlogging prediction method and device - Google Patents
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Abstract
The application provides a method and a device for forecasting urban waterlogging. The method comprises the following steps: hydrologic analysis is carried out on the city to be predicted so as to determine a first mapping relation between the accumulated water quantity and the corresponding accumulated water depth of each low-lying point in the city to be predicted and the percentage of the area of each low-lying point in the sum of the areas of all the low-lying points; performing big data analysis on historical water circulation monitoring data of the city to be predicted to determine a second mapping relation between regional waterlogging and water accumulation of the city to be predicted and corresponding regional precipitation; according to the second mapping relation, forecasting the regional waterlogging accumulated water amount of the field rainfall; distributing the water accumulation amount of the waterlogging in the rainfall field area 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 field according to the corresponding first mapping relation of each low-lying point. The monitoring of rainfall is realized to a certain extent, and the possibility of causing urban waterlogging is predicted according to the rainfall.
Description
Technical Field
The application relates to the field of urban risk early warning, in particular to a method and a device for urban waterlogging prediction.
Background
In recent years, with the rapid development of urbanization, the size of cities gradually begins to expand, but some water safety problems come along, for example, under the condition of extremely severe weather, and the emergency capacity of the urban emergency system is not enough to deal with the extremely severe weather, the life and production of residents can be seriously influenced and disturbed, and even the property of people is greatly lost. More specifically, for example, when sudden rainstorm or heavy rainfall occurs, the drainage capacity of the urban drainage system is not enough to timely drain the rainfall during the rainfall, which may cause serious waterlogging, such as "urban sea-seeing" phenomenon frequently occurring in recent years, which may not only lead to traffic paralysis, but also cause failures of urban water supply, power supply, communication, etc., even submerge commercial areas, production areas, residential areas, etc., so that people's property suffers huge loss, and further, may cause personal injuries and deaths, causing social order problems.
The reason why urban waterlogging frequently occurs in recent years is mainly as follows through comprehensive analysis: due to global warming caused by serious environmental pollution, extreme weather is frequent, and rainstorm or heavy rainfall events are increased; meanwhile, the urbanization progress is accelerated, so that the hardening rate of the earth surface is increased, and the amount of rainwater capable of penetrating into the earth surface is small; in addition, the urbanization construction speed is too fast, and the drainage capacity of the urban drainage system is limited, so that the rainfall cannot be discharged in time. The reasons are the main reasons of frequent phenomenon of 'city seeing sea' in recent years. Generally, the precipitation is increased, the surface runoff in cities is increased, and a city drainage system cannot drain water in time, so that waterlogging is easily generated in low-lying areas.
At present, no quick and effective method is available for urban waterlogging prediction, and a method of applying theoretical model simulation in combination with weather forecast or carrying out prediction according to experience is generally adopted for urban waterlogging analysis and early warning at present. The method mainly utilizes an SWMM (storm water management model), which 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 in a city to carry out analysis and early warning, or divides a structureless irregular network by means of GIS (geographic information system), and carries out risk analysis and early warning on urban waterlogging disasters by generalizing terrain and ground objects and combining the simulation of an urban drainage system. The prediction mode of the pure-mechanism model is limited by long model operation time, inaccurate basic data of the city, the capability of weather professionals and other reasons, so that whether the city is subjected to waterlogging is scientifically, accurately and effectively predicted; the method for the meteorological professional to carry out the conjecture according to the experience knowledge is lack of persuasion, and the conjecture result is unlikely to guarantee the accuracy.
Disclosure of Invention
The application provides a method and a device for forecasting urban waterlogging, which mainly aim to monitor rainfall to a certain extent and forecast the possibility of causing urban waterlogging according to the rainfall.
One aspect of the present application provides a method for urban waterlogging prediction, including:
hydrologic analysis is carried out on underlying surface topographic data of the city to be predicted so as to determine a first mapping relation between the accumulated water quantity and the corresponding accumulated water depth of each low-lying point in the city to be predicted and the percentage of the area of each low-lying point in 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 to determine a second mapping relation between the regional waterlogging accumulated water quantity of the city to be predicted and the corresponding regional precipitation quantity;
forecasting regional waterlogging accumulated water volume of the rainfall field according to the second mapping relation;
distributing the regional waterlogging accumulated water amount of the rainfall field to each low-lying point according to the percentage corresponding to each low-lying point, and determining the accumulated water depth of each low-lying point in the rainfall field according to the first mapping relation corresponding to 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 and water accumulation of the city to be predicted and the corresponding regional precipitation includes:
constructing an estimation function capable of bearing precipitation in a corresponding area of the city to be predicted:
B(m)＝E(m)+F(m)+V3(m)+V4(m)+V5(m)+V6(m)
wherein, B (m) represents the area can bear precipitation function, and m represents the precipitation field; e (m) represents a function of total evaporation in the zone; f (m) represents a function of total soil infiltration of the area; v3(m) represents a regional depression water accumulation function; v4(m) representing a regional water body water storage capacity function; v5(m) represents a regional conduit space impoundment capacity function; v6(m) represents an off-region water displacement function;
and assigning weight coefficients to independent variables of an estimation function of the area capable of bearing precipitation:
B(m)＝θ0+θ1E(m)+θ2f(m)+θ3V3(m)+θ4V4(m)+θ5V5(m)+θ6V6(m)
wherein, theta0，θ1，θ2…θ6The weighting coefficient corresponding to each independent variable of the function B (m);
matching the estimation function which is endowed with the weight coefficient and can bear the precipitation with a function library in a big data regression analysis statistical model, and performing regression analysis to determine an approximate function B' (m) which can bear the precipitation in the region;
according to the approximate function B' (m), constructing an estimation function of the regional waterlogging accumulated water volume:
W(m)＝P(m)-B′(m)
wherein W (m) represents a regional waterlogging water accumulation function, and P (m) represents a regional precipitation function;
assigning a weight coefficient to each independent variable of the estimation function of the regional waterlogging accumulated water volume;
W(m)＝δ0+δ1P(m)-δ2B(m)
wherein, delta0，δ1，δ2The weight coefficient corresponding to each independent variable of the bit function W (m);
matching the estimation function of the regional waterlogging water accumulation amount endowed with the weight coefficient with a function library in a big data regression analysis statistical model, and performing regression analysis to determine an approximate function W' (m) of the regional waterlogging water accumulation amount: w '(m) ═ f (p (m)), and the approximation function W' (m) is a second mapping relationship between the regional waterlogging water accumulation and the regional precipitation.
Optionally, the method further comprises:
hydrologic analysis is carried out on each depression point to determine a warning line of each depression point;
judging whether the elevation of accumulated water of each low-lying point in the rainfall field is higher than the corresponding warning line or not;
screening low-lying points, of which the elevation of accumulated water in the rainfall field is higher than the corresponding warning line, and marking the screened low-lying points as low-lying points to be pre-warned;
and early warning is carried out on the low-lying points to be early warned.
Optionally, the step of early warning the low-lying point to be early-warned comprises:
hydrologic analysis is carried out on the depression to be subjected to early warning so as to determine the geographical position of the depression to be subjected to early warning;
and marking in a GIS map according to the geographical position of the low-lying point to be pre-warned, and outputting the corresponding position coordinate and pre-warning information of the low-lying point to be pre-warned.
Optionally, the step of early warning the low-lying point to be early-warned comprises:
determining the corresponding early warning level of each low-lying point to be early warned according to the difference value between the depth of accumulated water in the rainfall field of each low-lying point to be early warned and the corresponding warning line;
and outputting early warning information according 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 device, including:
the first analysis module is used for carrying out hydrologic analysis on underlying surface topographic data of the city to be predicted so as to determine a first mapping relation between the water accumulation amount and the corresponding water accumulation depth of each low-lying point in the city to be predicted and the percentage of the area of each low-lying point in 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 accumulated water volume and the corresponding regional precipitation of the city to be predicted;
the prediction module is used for predicting the regional waterlogging and water accumulation of the rainfall in the field according to the second mapping relation;
and the distribution determining module is used for distributing the water accumulation amount of the water logging area of the rainfall field to each low-lying point according to the percentage corresponding to each low-lying point, and determining the water accumulation depth of each low-lying point in the rainfall field according to the first mapping relation corresponding to each low-lying point.
Optionally, the second analysis module comprises:
the first construction unit is used for constructing an estimation function of the rainfall capacity which can be borne by the corresponding area of the city to be predicted:
B(m)＝E(m)+F(m)+V3(m)+V4(m)+V5(m)+V6(m)
wherein, B (m) represents the area can bear precipitation function, and m represents the precipitation field; e (m) represents a function of total evaporation in the zone; f (m) represents a function of total soil infiltration of the area; v3(m) represents a regional depression water accumulation function; v4(m) representing a regional water body water storage capacity function; v5(m) represents a regional conduit space impoundment capacity function; v6(m) represents an off-region water displacement function;
a first assignment unit, configured to assign a weight coefficient to each argument of the estimation function that can carry precipitation in the area:
B(m)＝θ0+θ1E(m)+θ2f(m)+θ3V3(m)+θ4V4(m)+θ5V5(m)+θ6V6(m)
wherein, theta0，θ1，θ2…θ6The weighting coefficient corresponding to each independent variable of the function B (m);
the first analysis unit is used for matching the estimation function which is endowed with the weight coefficient and can bear the precipitation with a function library in a big data regression analysis statistical model, and performing regression analysis to determine an approximate function B' (m) which can bear the precipitation in the region;
a second construction unit, configured to construct an estimation function of the regional waterlogging water volume according to the approximation function B' (m):
W(m)＝P(m)-B′(m)
wherein W (m) represents a regional waterlogging water accumulation function, and P (m) represents a regional precipitation function;
the second assignment unit is used for assigning a weight coefficient to each independent variable of the estimation function of the regional waterlogging accumulated water;
W(m)＝δ0+δ1P(m)-δ2B(m)
wherein, delta0，δ1，δ2The weight coefficient corresponding to each independent variable of the bit function W (m);
and the second analysis unit is used for matching the estimation function of the regional waterlogging water volume, which is endowed with the weight coefficient, with a function library in a big data regression analysis statistical model, and performing regression analysis to determine an approximate function W' (m) of the regional waterlogging water volume: w '(m) ═ f (p (m)), and the approximation function W' (m) is a second mapping relationship between the regional waterlogging water accumulation and the regional precipitation.
Optionally, the apparatus further comprises:
the first determination module is used for carrying out hydrological analysis on each low-lying point so as to determine a warning line of each low-lying point;
the judging module is used for judging whether the elevation of accumulated water of each low-lying point in the rainfall field is higher than the corresponding warning line or not;
the screening module is used for screening low-lying points, with the accumulated water elevation higher than the corresponding warning line, in the rainfall field and marking the screened low-lying points as low-lying points to be pre-warned;
and the early warning module is used for early warning the low-lying points to be early warned.
Optionally, the early warning module includes:
the first determining unit is used for carrying out hydrological analysis on the low-lying point to be subjected to early warning so as to determine the geographical position of the low-lying point to be subjected to early warning;
and the first output unit is used for marking in a GIS map according to the geographical position of the low-lying point to be pre-warned and outputting the corresponding position coordinate 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 the corresponding early warning level of each low-lying point to be early warned according to the difference value between the depth of accumulated water in the rainfall field of each low-lying point to be early warned and the corresponding warning line;
and the second output unit is used for outputting the 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 method has the following advantages:
determining a first mapping relation between the accumulated water quantity of each low-lying point in the city to be predicted and the corresponding accumulated water depth, and the percentage of the area of each low-lying point in the sum of the areas of all the low-lying points; determining a second mapping relation between the regional waterlogging accumulated water volume of the city to be predicted and the corresponding regional rainfall volume through historical water circulation monitoring data of the city to be predicted; when the rainfall occurs, matching is carried out according to a second mapping relation obtained by big data analysis, and the corresponding regional waterlogging accumulated water volume of the rainfall is obtained; according to the percentage of the accumulated water quantity of each low-lying point to the sum of the accumulated water quantities of all the low-lying points, distributing the waterlogging accumulated water quantity of the rainfall area to each low-lying point, and then according to the first mapping relation, determining the water accumulation depth of each low-lying point, so that the rainfall is monitored, and the possibility of causing urban waterlogging is predicted according to the water accumulation quantity. Compared with the related technology, the method for predicting whether the urban waterlogging occurs or not can be more effectively predicted by adopting an SWMM model or GIS analysis or a method for predicting whether the urban waterlogging occurs or not by weather professionals according to experience knowledge.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
Fig. 1 is a flowchart of a method for urban waterlogging prediction according to an embodiment of the present 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 to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description.
Referring to fig. 1, a city waterlogging prediction method according to the present application is shown, including:
s101, hydrologic analysis is carried out on underlying surface topographic data of the city to be predicted, so that a first mapping relation between the water accumulation amount and the corresponding water accumulation depth of each low-lying point in the city to be predicted is determined, and the percentage of the area of each low-lying point in the sum of the areas of all the low-lying points is determined.
Each city has respective underlying surface topographic data, hydrologic analysis is carried out on the underlying surface topographic data, and water accumulation areas which are easy to accumulate water and potential water accumulation areas, namely low-lying points, in the city to be predicted are searched and identified. The process of searching for and identifying the low-lying points is a process of solving the optimal solution of the search problem by intelligent computation. For example, a tabu search algorithm is adopted for search identification, and the specific steps are as follows:
s1011, partitioning the elevation terrain data of the city DEM to be predicted, randomly selecting elevation data of one point in the partitioned data as an initial solution x, and setting a taboo table to be empty.
The DEM elevation data is small squares divided according to longitude and latitude coordinates and corresponds to elevation data of coordinate points. And (4) partitioning the DEM elevation data according to the longitude and latitude coordinates, and adjusting the number and size of partitions according to the accuracy and requirements of the DEM elevation data. And after the DEM elevation data are partitioned, performing parallel calculation on each DEM elevation data by using a tabu search solving algorithm.
S1012, judging whether the points of the terrain elevation data in the city to be predicted meet termination conditions; if yes, go to step S1013; if not, step S1014 is executed.
S1013, the identified low-lying point is output, and the search is ended.
S1014, solving all neighborhood solutions by using the block neighborhoods 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 the candidate solution of the terrain elevation data of the city to be predicted meets scofflaw criteria; if yes, go to step S1016; if not, go to step S1017.
S1016, updating the solution meeting the scofflaw criterion into a taboo table; and step S1012 is performed again until all low-lying points are identified.
For example, the candidate solution meeting the scofflaw criterion, namely the determined optimal solution y of the terrain elevation of the low-lying point replaces x to become a new current solution, namely x is equal to y, then the terrain elevation of the point which enters the taboo table earliest in the taboo table is replaced by the point which takes the terrain elevation of the low-lying point corresponding to y as the taboo object, and whether the point of the terrain elevation data of the city to be predicted meets the termination condition is judged again.
S1017, judging a taboo attribute in the candidate solution, selecting an optimal solution in the candidate solution set corresponding to the terrain elevation of the low-lying point as a new current solution, and replacing a terrain elevation object entering a taboo table at the earliest time by using the terrain elevation corresponding to the optimal solution as the object; and step S1012 is performed again until all low-lying points are identified.
When all low-lying points of the city to be predicted are identified, acquiring hydrological data corresponding to each low-lying point, such as the geographic position, corresponding coordinate information, ponding amount, ponding depth and the like of each low-lying point.
And determining a first mapping relation between the water collectable amount and the corresponding water collecting depth of each low-lying point according to the hydrological data corresponding to each low-lying point, for example, a relation curve V between the water collectable amount and the water collecting depth is g (h), and determining the percentage of the area of each low-lying point to the sum of the areas of all the low-lying points.
S102, performing big data analysis on the historical water circulation monitoring data of the city to be predicted to determine a second mapping relation between the regional waterlogging accumulated water volume and the corresponding regional precipitation 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 a basis of a statistical analysis model of the waterlogging big data of the city to be predicted, and a determination model of an influence relation between a dependent variable (regional waterlogging accumulated water quantity) and an independent variable (regional precipitation quantity) is established through the regression analysis, so that a second mapping relation between the regional waterlogging accumulated water quantity of the city to be predicted and the corresponding regional precipitation quantity is determined, for example, an exponential curve, a quadratic curve, a logarithmic curve and the like obtained through the regression analysis.
And S103, predicting the regional waterlogging and water accumulation of the rainfall field according to the second mapping relation.
After the second mapping relationship between the regional waterlogging water accumulation amount and the regional rainfall amount is determined, matching can be performed according to the area rainfall amount of the rainfall (namely the regional rainfall amount of the rainfall) and the mapping relationship, and the corresponding regional waterlogging water accumulation amount of the rainfall can be predicted and obtained. The calculation of the rainfall on the rainfall surface can be confirmed according to the method of obtaining the least square method fitting quadric surface in the prior art, and corresponding correction is carried out according to the time of the rainfall on the rainfall so as to correct errors.
S104, distributing the water accumulation amount of the water logging area of the rainfall field to each low-lying point according to the percentage corresponding to each low-lying point, and determining the water accumulation depth of each low-lying point in the rainfall field according to the first mapping relation corresponding to each low-lying point.
After the regional waterlogging accumulated water volume of the rainfall field is predicted and obtained, distributing the regional waterlogging accumulated water volume of the rainfall field to each low-lying point according to the percentage of the area of each low-lying point determined in the step S101 to the sum of the areas of all the low-lying points, and previewing the accumulated water volume which each low-lying point may need to bear in the rainfall field; and then, determining the accumulated water depth of each low-lying point in the rainfall according to the relation curve between the water accumulatable amount and the accumulated water depth of each low-lying point determined in the step S101 and the accumulated water amount required to be carried in the rainfall.
It should be noted that the above steps S103-S104 are a dynamic process, and during each rainfall event, the real-time dynamic execution is required according to the rainfall time to ensure the accuracy of the prediction.
In a modern city, according to the hydrological theory system, when rainfall occurs, the direction of rainfall in the area mainly includes the following items: regional evaporation, regional soil infiltration, regional water accumulation in hollow lands, regional water storage (riverways, lakes, ponds and the like), regional pipeline space storage and regional discharge. The total amount of the rainfall in the area, which goes to, is called as the area capable of bearing the rainfall, according to the water balance principle, when the area rainfall exceeds the area capable of bearing the rainfall, ponding can be generated at a low-lying point, and when the ponding depth exceeds a certain limit, the waterlogging is caused. Constructing a water balance model of a city:
W＝P-B
wherein W represents the regional waterlogging accumulated water volume, P represents the regional precipitation volume, and B represents the regional rainfall volume capable of bearing precipitation;
when P-B is less than or equal to 0, namely W is less than or equal to 0, indicating that no waterlogging occurs in the area;
on the contrary, when P-B is more than 0, namely W is more than 0, the inland inundation is the occurrence of inland inundation in the area, and the value of W is the specific value of inland inundation water accumulation in the area.
The area capable of carrying precipitation B can be represented by the following model:
B＝E+F+V3+V4+V5+V6
wherein E represents the total evaporation of the zone; f represents the total soil infiltration amount of the area; v3Indicating the water accumulation in the area depression; v4Representing the water storage capacity of the regional water body; v5Representing the water storage capacity of the area pipeline space; v6Indicating the amount of water displaced outside the zone.
Therefore, the model can be used when the big data analysis is carried out 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 and the corresponding regional precipitation of the city to be predicted includes:
s1021, constructing an estimation function capable of bearing precipitation in a corresponding area of the city to be predicted:
B(m)＝E(m)+F(m)+V3(m)+V4(m)+V5(m)+V6(m)
wherein, B (m) represents the area can bear precipitation function, and m represents the precipitation field; e (m) represents a function of total evaporation in the zone; f (m) represents a function of total soil infiltration of the area; v3(m) represents a regional depression water accumulation function; v4(m) representing a regional water body water storage capacity function; v5(m) represents a regional conduit space impoundment capacity function; v6(m) represents an off-region water discharge amount function.
It is known that different seasons, precipitation stages, rainfall, and the like are different, and the total evaporation and soil infiltration of a region are also different. In order to ensure the effectiveness of the prediction, it is necessary to perform the prediction by stages (such as season/quarter/month/day) according to different seasons/seasons, in this embodiment, for example, the year is divided into the first, middle and last three ten days of each month by month, and the rainfall times of the previous year in the corresponding ten days of the month are counted, and each ten day is about 10 days (since not every month is 30 days, the error value of each ten day can be dynamically adjusted within one dayFor example, 9 days or 11 days). And carrying out big data analysis according to the historical water circulation monitoring data of each ten-day time. Therefore, in this embodiment, the precipitation lot m corresponds to the data of the precipitation lot counted in the previous year in the corresponding to-be-predicted ten days of the month to be predicted, and the functional relational expressions of the precipitation directions, such as e (m), f (m), V (V), can be obtained by using the big data regression analysis3(m)、V4(m)、V5(m) and V6(m)。
However, this is not a limitation to the present application, and the rainfall field number m may be appropriately dynamically adjusted according to the prediction accuracy requirement, for example, if the probability of occurrence of waterlogging and the number of occurrences of waterlogging in a certain season need to be predicted, the rainfall field number m corresponds to data of the rainfall field number counted in the year of the year before the season to be predicted, and the function relation expression of each rainfall direction is correspondingly corrected. Correspondingly, the rainfall field m can also correspond to data of rainfall fields accumulated and counted in other periods in the past year.
After the data of the precipitation field m in the period to be predicted and the functional relation expression of each precipitation heading are determined, an estimation function of the precipitation amount of the corresponding area of the city to be predicted can be constructed according to the model of the area capable of bearing the precipitation amount B.
S1022, assigning weight coefficients to the independent variables of the estimation function capable of bearing precipitation in the region:
B(m)＝θ0+θ1E(m)+θ2f(m)+θ3V3(m)+θ4V4(m)+θ5V5(m)+θ6V6(m)
wherein, theta0，θ1，θ2…θ6The corresponding weight coefficient of each independent variable of the function B (m).
In different time periods, the proportions of the precipitation destinations are different, so that corresponding weight coefficients need to be given to the precipitation destinations, namely, the independent variables of the function B (m) according to the time period to be predicted.
And S1023, matching the estimation function which is endowed with the weight coefficient and can bear the precipitation amount with a function base in a big data regression analysis statistical model, and performing regression analysis to determine an approximate function B' (m) which can bear the precipitation amount in the region.
In a big data regression analysis statistical model, a 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 needs a large amount of data as support, and focuses on the data and the results produced by the data, without paying attention to the specific processes and reasons for producing the results.
S1024, according to the approximate function B' (m), constructing an estimation function of the regional waterlogging accumulated water volume:
W(m)＝P(m)-B′(m)
wherein W (m) represents a regional waterlogging water accumulation function, and P (m) represents a regional precipitation function.
After the approximate function B' (m) capable of bearing the precipitation of the region is determined, an estimation function of the precipitation of the region can be constructed according to a water balance principle model.
S1025, endowing each independent variable of the estimation function of the regional waterlogging water accumulation amount with a weight coefficient;
W(m)＝δ0+δ1P(m)-δ2B(m)
wherein, delta0，δ1，δ2The weight coefficient corresponding to each argument of the bit function W (m).
Corresponding to different time periods to be predicted, the area precipitation and the area borne precipitation are possibly influenced by different time periods in one year, and the occupation ratios of the area precipitation and the area borne precipitation are possibly different, so that corresponding weight coefficients need to be given according to the time periods to be predicted for correction.
S1026, matching the estimation function of the regional waterlogging water volume endowed with the weight coefficient with a function library in a big data regression analysis statistical model, and performing regression analysis to determine an approximate function W' (m) of the regional waterlogging water volume: w '(m) ═ f (p (m)), and the approximation function W' (m) is a second mapping relationship between the regional waterlogging water accumulation and the regional precipitation.
The regression analysis is similar to the regression analysis of the estimation function capable of carrying the precipitation in the region, and more specifically, please refer to the related prior art, which is not a technical problem to be solved in the present application, and therefore, the details are not repeated.
In some embodiments, the method further comprises:
s105, hydrologic analysis is carried out on each low-lying point to determine a warning line of each low-lying point.
And S106, judging whether the elevation of the accumulated water of each low-lying point in the rainfall field is higher than the corresponding warning line or not.
S107, screening out low-lying points, with the accumulated water elevation higher than the corresponding warning line, in the rainfall field, and marking the screened low-lying points as low-lying points to be pre-warned.
And S108, early warning is carried out on the low-lying points to be early warned.
In the hydrological analysis, a tabu search algorithm may still be used, although other algorithms may be used to determine the warning lines for each depression. After the respective accumulated water depth of each low-lying point in the predicted rainfall field is determined through the embodiment, whether each low-lying point needs to be pre-warned or not is judged, so that people can be reminded of paying attention.
In some embodiments, the step of warning the low-lying point to be warned comprises:
s1081, hydrologic analysis is conducted on the low-lying points to be pre-warned, so that the geographical positions of the low-lying points to be pre-warned are determined.
S1082, marking is carried out in a GIS map according to the geographical position of the low-lying point to be pre-warned, and the corresponding position coordinate and pre-warning information of the low-lying point to be pre-warned are output.
Position coordinate information of low-lying points to be pre-warned is marked in the GIS map and output to the risk control center, so that the risk control center can start an emergency pre-warning scheme in time.
In some embodiments, the step of warning the low-lying point to be warned comprises:
s1083, determining the corresponding early warning level of each low-lying point to be early warned according to the difference value between the depth of accumulated water in the rainfall field of each low-lying point to be early warned and the corresponding warning line.
Each depression is located at a different position, and the depth of accumulated water in the field rain is different. Usually, the depth of accumulated water is 0-10 mm, the accumulated water can automatically subside in a short period of time by first-level early warning, and the urban traffic and human life are basically not influenced. When the depth of the accumulated water is 10-30 mm, secondary early warning is carried out, the accumulated water is automatically removed 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 carried out, the accumulated water cannot be automatically removed 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, four-level early warning is carried out, the accumulated water cannot be automatically removed for a long time, great influence is caused on urban traffic and human life, even the accumulated water in local areas can be caused, and the life and property safety of people is seriously threatened.
According to the difference value between the depth of the accumulated water in the field rainfall and the corresponding warning line, the early warning level can be conveniently determined.
And S1084, outputting early warning information according 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 method has the following advantages:
determining a first mapping relation between the water accumulatable quantity of each low-lying point in the city to be predicted and the corresponding water accumulation depth, and the percentage of the water accumulatable quantity of each low-lying point to the sum of the water accumulatable quantities of all the low-lying points; determining a second mapping relation between the regional waterlogging accumulated water volume of the city to be predicted and the corresponding regional rainfall volume through historical water circulation monitoring data of the city to be predicted; when the rainfall occurs, matching is carried out according to a second mapping relation obtained by big data analysis, and the corresponding regional waterlogging accumulated water volume of the rainfall is obtained; according to the percentage of the accumulated water quantity of each low-lying point to the sum of the accumulated water quantities of all the low-lying points, distributing the waterlogging accumulated water quantity of the rainfall area to each low-lying point, and then according to the first mapping relation, determining the water accumulation depth of each low-lying point, so that the rainfall is monitored, and the possibility of causing urban waterlogging is predicted according to the water accumulation quantity. Compared with the related technology, the method for predicting whether the urban waterlogging occurs or not can be more effectively predicted by adopting an SWMM model or GIS analysis or a method for predicting whether the urban waterlogging occurs or not by weather professionals according to experience knowledge.
Referring to fig. 2, in another aspect of the present application, there is provided an urban waterlogging prediction device, including:
the first analysis module 201 is configured to perform hydrological analysis on underlying surface topographic data of the city to be predicted to determine a first mapping relationship between the ponding amount and the corresponding ponding depth of each low-lying point in the city to be predicted, and a percentage of the ponding amount of each low-lying point to a total ponding amount of all the low-lying points;
the second analysis module 202 is 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 accumulated water volume and the corresponding regional precipitation volume of the city to be predicted;
the prediction module 203 is used for predicting the regional waterlogging and water accumulation of the rainfall according to the second mapping relation;
the distribution determining module 204 is configured to distribute the water accumulation amount of the water logging area of the rainfall field to each of the low-lying points according to the percentage corresponding to each of the low-lying points, and determine the water accumulation depth of each of the low-lying points in the rainfall field according to the first mapping relation corresponding to each of the low-lying points.
Optionally, the second analysis module 202 comprises:
a first constructing unit 2021, configured to construct an estimation function of a loadable precipitation capacity of a region corresponding to the city to be predicted:
B(m)＝E(m)+F(m)+V3(m)+V4(m)+V5(m)+V6(m)
wherein, B (m) represents the area can bear precipitation function, and m represents the precipitation field; e (m) represents a function of total evaporation in the zone; f (m) represents a regionA total soil infiltration function; v3(m) represents a regional depression water accumulation function; v4(m) representing a regional water body water storage capacity function; v5(m) represents a regional conduit space impoundment capacity function; v6(m) represents an off-region water displacement function;
a first assigning unit 2022, configured to assign weight coefficients to arguments of the estimation function that can carry precipitation in the area:
B(m)＝θ0+θ1E(m)+θ2f(m)+θ3V3(m)+θ4V4(m)+θ5V5(m)+θ6V6(m)
wherein, theta0，θ1，θ2…θ6The weighting coefficient corresponding to each independent variable of the function B (m);
a first analysis unit 2023, configured to match the estimation function that can bear the precipitation amount in the region to which the weight coefficient is assigned with a function library in a big data regression analysis statistical model, and perform regression analysis to determine an approximate function B' (m) that can bear the precipitation amount in the region;
a second construction unit 2024, configured to construct an estimation function of the regional waterlogging water volume according to the approximation function B' (m):
W(m)＝P(m)-B′(m)
wherein W (m) represents a regional waterlogging water accumulation function, and P (m) represents a regional precipitation function;
a second assigning unit 2025, configured to assign a weight coefficient to each independent variable of the estimation function of the regional waterlogging accumulated water amount;
W(m)＝δ0+δ1P(m)-δ2B(m)
wherein, delta0，δ1，δ2The weight coefficient corresponding to each independent variable of the bit function W (m);
a second analysis unit 2026, configured to match the estimation function of regional waterlogging water accumulation amount given with the weighting 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 regional waterlogging water accumulation amount: w '(m) ═ f (p (m)), and the approximation function W' (m) is a second mapping relationship between the regional waterlogging water accumulation and the regional precipitation.
Optionally, the apparatus further comprises:
a first determination module 205, configured to perform hydrological analysis on each of the low-lying points to determine a warning line for each of the low-lying points;
the judging module 206 is configured to judge whether an elevation of accumulated water at each low-lying point in the rainfall field is higher than the warning line;
the screening module 207 is used for screening low-lying points, of which the elevation of accumulated water in the rainfall field is higher than the corresponding warning line, and marking 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 hydrological analysis on the low-lying point to be pre-warned to determine a geographical position of the low-lying point to be pre-warned;
and the first output unit 2082 is used for marking in a GIS map according to the geographical position of the low-lying point to be pre-warned, and outputting the corresponding position coordinate and pre-warning information of the low-lying point to be pre-warned.
Optionally, the early warning module 208 includes:
the second determining unit 2083 is configured to determine a corresponding early warning level of each low-lying point to be early warned according to a difference between a water accumulation depth of each low-lying point to be early warned in the rainfall field and a corresponding warning line;
and the second output unit 2084 is used for outputting the early warning information according with the corresponding early warning level to the low-lying point to be early warned.
For the system embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The urban waterlogging prediction method and device provided by the application are introduced in detail, specific examples are applied in the method to explain the principle and the implementation mode of the application, and the description of the embodiments is only used for helping to understand the method and the core idea of the application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A method for predicting urban waterlogging is characterized by comprising the following steps:
hydrologic analysis is carried out on underlying surface topographic data of the city to be predicted so as to determine a first mapping relation between the accumulated water quantity and the corresponding accumulated water depth of each low-lying point in the city to be predicted and the percentage of the area of each low-lying point in 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 to determine a second mapping relation between the regional waterlogging accumulated water quantity of the city to be predicted and the corresponding regional precipitation quantity;
forecasting regional waterlogging accumulated water volume of the rainfall field according to the second mapping relation;
distributing the regional waterlogging accumulated water amount of the rainfall field to each low-lying point according to the percentage corresponding to each low-lying point, and determining the accumulated water depth of each low-lying point in the rainfall field according to the first mapping relation corresponding to each low-lying point.
2. The method of claim 1, wherein 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 accumulation and the corresponding regional precipitation of the city to be predicted comprises:
constructing an estimation function capable of bearing precipitation in a corresponding area of the city to be predicted:
B(m)＝E(m)+F(m)+V3(m)+V4(m)+V5(m)+V6(m)
wherein, B (m) represents the area can bear precipitation function, and m represents the precipitation field; e (m) represents a function of total evaporation in the zone; f (m) represents a function of total soil infiltration of the area; v3(m) represents a regional depression water accumulation function; v4(m) representing a regional water body water storage capacity function; v5(m) represents a regional conduit space impoundment capacity function; v6(m) represents an off-region water displacement function;
and assigning weight coefficients to independent variables of an estimation function of the area capable of bearing precipitation:
B(m)＝θ0+θ1E(m)+θ2f(m)+θ3V3(m)+θ4V4(m)+θ5V5(m)+θ6V6(m)
wherein, theta0，θ1，θ2…θ6The weighting coefficient corresponding to each independent variable of the function B (m);
matching the estimation function which is endowed with the weight coefficient and can bear the precipitation with a function library in a big data regression analysis statistical model, and performing regression analysis to determine an approximate function B' (m) which can bear the precipitation in the region;
according to the approximate function B' (m), constructing an estimation function of the regional waterlogging accumulated water volume:
W(m)＝P(m)-B′(m)
wherein W (m) represents a regional waterlogging water accumulation function, and P (m) represents a regional precipitation function;
assigning a weight coefficient to each independent variable of the estimation function of the regional waterlogging accumulated water volume;
W(m)＝δ0+δ1P(m)-δ2B(m)
wherein, delta0，δ1，δ2The weight coefficient corresponding to each independent variable of the bit function W (m);
matching the estimation function of the regional waterlogging water accumulation amount endowed with the weight coefficient with a function library in a big data regression analysis statistical model, and performing regression analysis to determine an approximate function W' (m) of the regional waterlogging water accumulation amount: w '(m) ═ f (p (m)), and the approximation function W' (m) is a second mapping relationship between the regional waterlogging water accumulation and the regional precipitation.
3. The method of claim 1, further comprising:
hydrologic analysis is carried out on each depression point to determine a warning line of each depression point;
judging whether the elevation of accumulated water of each low-lying point in the rainfall field is higher than the corresponding warning line or not;
screening low-lying points, of which the elevation of accumulated water in the rainfall field is higher than the corresponding warning line, and marking the screened low-lying points as low-lying points to be pre-warned;
and early warning is carried out on the low-lying points to be early warned.
4. The method of claim 3, wherein the step of pre-warning the low-lying point to be pre-warned comprises:
hydrologic analysis is carried out on the depression to be subjected to early warning so as to determine the geographical position of the depression to be subjected to early warning;
and marking in a GIS map according to the geographical position of the low-lying point to be pre-warned, and outputting the corresponding position coordinate and pre-warning information of the low-lying point to be pre-warned.
5. The method according to claim 3 or 4, wherein the step of pre-warning the low-lying point to be pre-warned comprises:
determining the corresponding early warning level of each low-lying point to be early warned according to the difference value between the depth of accumulated water in the rainfall field of each low-lying point to be early warned and the corresponding warning line;
and outputting early warning information according with the corresponding early warning level to the low-lying point to be early warned.
6. An urban waterlogging prediction device, comprising:
the first analysis module is used for carrying out hydrologic analysis on underlying surface topographic data of the city to be predicted so as to determine a first mapping relation between the water accumulation amount and the corresponding water accumulation depth of each low-lying point in the city to be predicted and the percentage of the area of each low-lying point in 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 accumulated water volume and the corresponding regional precipitation of the city to be predicted;
the prediction module is used for predicting the regional waterlogging and water accumulation of the rainfall in the field according to the second mapping relation;
and the distribution determining module is used for distributing the water accumulation amount of the water logging area of the rainfall field to each low-lying point according to the percentage corresponding to each low-lying point, and determining the water accumulation depth of each low-lying point in the rainfall field according to the first mapping relation corresponding to each low-lying point.
7. The apparatus of claim 6, wherein the second analysis module comprises:
the first construction unit is used for constructing an estimation function of the rainfall capacity which can be borne by the corresponding area of the city to be predicted:
B(m)＝E(m)+F(m)+V3(m)+V4(m)+V5(m)+V6(m)
wherein, B (m) represents the area can bear precipitation function, and m represents the precipitation field; e (m) represents a function of total evaporation in the zone; f (m) represents a function of total soil infiltration of the area; v3(m) represents a regional depression water accumulation function; v4(m) regional water storageA water quantity function; v5(m) represents a regional conduit space impoundment capacity function; v6(m) represents an off-region water displacement function;
a first assignment unit, configured to assign a weight coefficient to each argument of the estimation function that can carry precipitation in the area:
B(m)＝θ0+θ1E(m)+θ2f(m)+θ3V3(m)+θ4V4(m)+θ5V5(m)+θ6V6(m)
wherein, theta0，θ1Theta 2 … theta 6 is the weight coefficient corresponding to each independent variable of the function B (m);
the first analysis unit is used for matching the estimation function which is endowed with the weight coefficient and can bear the precipitation with a function library in a big data regression analysis statistical model, and performing regression analysis to determine an approximate function B' (m) which can bear the precipitation in the region;
a second construction unit, configured to construct an estimation function of the regional waterlogging water volume according to the approximation function B' (m):
W(m)＝P(m)-B′(m)
wherein W (m) represents a regional waterlogging water accumulation function, and P (m) represents a regional precipitation function;
the second assignment unit is used for assigning a weight coefficient to each independent variable of the estimation function of the regional waterlogging accumulated water;
W(m)＝δ0+δ1P(m)-δ2B(m)
wherein, delta0，δ1，δ2The weight coefficient corresponding to each independent variable of the bit function W (m);
and the second analysis unit is used for matching the estimation function of the regional waterlogging water volume, which is endowed with the weight coefficient, with a function library in a big data regression analysis statistical model, and performing regression analysis to determine an approximate function W' (m) of the regional waterlogging water volume: w '(m) ═ f (p (m)), and the approximation function W' (m) is a second mapping relationship between the regional waterlogging water accumulation and the regional precipitation.
8. The apparatus of claim 6, further comprising:
the first determination module is used for carrying out hydrological analysis on each low-lying point so as to determine a warning line of each low-lying point;
the judging module is used for judging whether the elevation of accumulated water of each low-lying point in the rainfall field is higher than the corresponding warning line or not;
the screening module is used for screening low-lying points, with the accumulated water elevation higher than the corresponding warning line, in the rainfall field and marking the screened low-lying points as low-lying points to be pre-warned;
and the early warning module is used for early warning the low-lying points to be early warned.
9. The apparatus of claim 6, wherein the early warning module comprises:
the first determining unit is used for carrying out hydrological analysis on the low-lying point to be subjected to early warning so as to determine the geographical position of the low-lying point to be subjected to early warning;
and the first output unit is used for marking in a GIS map according to the geographical position of the low-lying point to be pre-warned and outputting the corresponding position coordinate and pre-warning information of the low-lying point to be pre-warned.
10. The apparatus of claim 8 or 9, wherein the early warning module comprises:
the second determining unit is used for determining the corresponding early warning level of each low-lying point to be early warned according to the difference value between the depth of accumulated water in the rainfall field of each low-lying point to be early warned and the corresponding warning line;
and the second output unit is used for outputting the early warning information which accords with the corresponding early warning level to the low-lying point to be early warned.
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