CN110646867A - Urban drainage monitoring and early warning method and system - Google Patents
Urban drainage monitoring and early warning method and system Download PDFInfo
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Abstract
The application discloses a method and a system for monitoring and early warning of urban drainage, which solve the problems of small prediction range and low accuracy in the prior art. A city drainage monitoring and early warning method comprises the following steps: collecting historical meteorological data to generate a short-term rainfall prediction model; obtaining forecast rainfall data through real-time meteorological data and a short-time rainfall forecast model; the short-time drainage model was calculated using the predicted rainfall data and the SWMM model technique. A city drainage monitoring and early warning system comprises: and the meteorological data module is used for periodically collecting historical meteorological data in the area. And the online monitoring module is used for acquiring real-time meteorological data. And the rainfall prediction module is used for receiving the short-time rainfall prediction model and the real-time meteorological data to calculate and predict rainfall data. And the SWMM calculation module is used for receiving the predicted rainfall data and calculating the short-time drainage model. The method and the device for forecasting the rainfall of the urban area make up for the defects in the aspects of forecasting range and accuracy in the prior art.
Description
Technical Field
The application relates to the field of urban drainage monitoring and early warning, in particular to an urban drainage monitoring and early warning method and system.
Background
In the current urban rainfall flood management work, the SWMM model technology is often used, a real rainstorm event is simulated based on the monitoring rainfall, the water quantity space-time distribution of an urban drainage pipe network is predicted, the drainage capacity of a drainage system is evaluated, and flood early warning is carried out. But the method has great defects in the aspects of prediction range and accuracy due to the lack of more accurate future rainfall prediction data.
Disclosure of Invention
The embodiment of the application provides a method and a system for monitoring and early warning of urban drainage, and solves the problems of small prediction range and low accuracy in the prior art.
The application provides a city drainage monitoring and early warning method, which comprises the following steps:
collecting X years of historical meteorological data in the region, and simulating to generate a region short-term rainfall prediction model;
obtaining forecast rainfall data through regional real-time meteorological data and a regional short-time rainfall forecast model;
and calculating a short-time drainage model of the area by using an SWMM model technology and the predicted rainfall data.
Further, the method also comprises the following steps:
and dividing the waterlogging risk level according to the regional short-time drainage model, and predicting the waterlogging risk.
Preferably, the method further comprises the steps of:
and evaluating the short-term rainfall prediction model of the area by using the predicted rainfall data and the real-time rainfall data.
Preferably, the method further comprises the steps of:
and (4) evaluating the regional short-time drainage model by using the SWMM model to calculate liquid level and flow data and the actually monitored liquid level and flow data.
Preferably, the real-time meteorological data comprises temperature, humidity, barometric pressure, wind speed and rainfall data.
Preferably, the data for constructing the SWMM model includes pipe network census data, land use type data, auxiliary structure data, and sponge measure data.
Preferably, the actually monitored liquid level and flow data includes liquid level data of important points such as drainage canals, inspection wells, drainage ports, sunken roads, underground parking lots, urban river and lake water systems and the like.
This application still provides a city drainage monitoring early warning system, contains: the rainfall forecasting system comprises a meteorological data module, an online monitoring module, a rainfall forecasting module and an SWMM calculation module;
the meteorological data module is used for collecting meteorological data in the area regularly, constructing a short-time rainfall prediction model and conveying the short-time rainfall prediction model to the rainfall prediction module;
the online monitoring module is used for collecting real-time meteorological data and transmitting the real-time meteorological data to the rainfall prediction module;
the rainfall prediction module receives the short-time rainfall prediction model and the real-time meteorological data to obtain predicted rainfall data.
And the SWMM calculation module receives the predicted rainfall data and calculates a short-time drainage model.
Further, the method also comprises the following steps: an inland inundation early warning module;
the waterlogging early warning module receives the prediction result of the SWMM calculation module, carries out waterlogging risk grade division, pre-judges waterlogging risks in advance and issues early warning information.
Preferably, it further comprises: a rainfall prediction evaluation module and an SWMM evaluation module;
the rainfall prediction module is used for evaluating the prediction precision of the short-time rainfall prediction model through the predicted rainfall data and the actually detected rainfall data;
and the SWMM evaluation module is used for evaluating the regional short-time drainage model by using the SWMM model to calculate the liquid level and flow data and the actually monitored liquid level and flow data.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
the method integrates monitoring, forecasting and decision-making, and provides an effective decision-making basis for coping with urban waterlogging risks in advance. The method has the advantages that the big data technology is utilized to carry out statistical processing and mining analysis on various meteorological observation data such as temperature, humidity, air pressure and rainfall, the rainfall of the urban area is predicted, the SWMM model technology is combined to simulate the drainage capacity of the urban drainage system, the shortage of the SWMM model in the aspects of prediction range and accuracy due to the lack of accurate future rainfall prediction data is made up to a certain extent, and the method has important theoretical and practical significance for improving the early warning accuracy of urban flood disasters.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of an embodiment of a method for monitoring and warning urban drainage;
FIG. 2 is a flow chart of another embodiment of a method for monitoring and warning urban drainage;
FIG. 3 is a flow chart of a third embodiment of a method for monitoring and warning urban drainage;
FIG. 4 is a schematic diagram of a short-term rainfall prediction model in a neural network region;
fig. 5 is a structure diagram of a city drainage monitoring and early warning.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Example 1
Fig. 1 is a flow chart of an embodiment of an urban drainage monitoring and early warning method.
A city drainage monitoring and early warning method comprises the following steps:
in step 101, historical meteorological data of each area of the city in the last X years are collected, data cleaning and preprocessing are carried out on the meteorological data, a BP neural network is used, a powerful nonlinear function approximation function of the BP neural network is utilized, and an area short-time rainfall prediction model is trained.
It should be noted that, the year X is not particularly limited, and the more the historical meteorological data is, the more accurate the constructed rainfall prediction model is. Preferably, X.gtoreq.5.
Data washing and preliminary treatment, because the amplitude of historical meteorological data is not of uniform size, directly use it to carry out neural network simulation training, the learning process of neural network can be blocked to the great fluctuation of observation data to can not reflect its less change, consequently, need carry out normalization to historical meteorological data:
in the formula (1), xnormA value normalized for the sample; x is the number ofiIs the initial value of the sample; x is the number ofmaxIs the maximum value of the sample; x is the number ofminIs the sample minimum.
According to the correlation degree of each meteorological factor and future rainfall, the factor with the larger correlation coefficient is selected as the input parameter of the neural network, and the factor with the larger correlation coefficient is generally considered to comprise air pressure field data, temperature field data, humidity field data and wind field data. Therefore, the data of the air pressure field, the data of the temperature field, the data of the humidity field and the data of the wind field are selected as input parameters of the BP neural network, the rainfall of the next day is selected as output parameters of the BP neural network, and the short-time rainfall prediction model of each area is trained.
For example, the weather data of 6-9 months with large precipitation in Beijing in the last 5 years can be selected for model training. And randomly selecting 3/4 data as training samples to establish a training sample set every month, and selecting the rest 1/4 data as test samples to establish a test sample set. The input parameters are classified into 4 types of atmospheric pressure field factors, temperature field factors, humidity field factors and wind field factors: selecting 8 parts per day: 00. 16:00, next day 0: the ground air pressure of 00 and the height of 500hPa at high altitude are taken as air pressure field factors; selecting 8 parts per day: 00. 16:00, next day 0: the high altitude 500hPa temperature and the ground temperature of 00 are used as temperature field factors; selecting 8 parts per day: 00. 16:00, next day 0: a ground humidity of 00 as a humidity field factor; selecting 8: 00. 16:00, next day 0: the wind speed of 00 is used as the wind field factor. And taking the hour rainfall sequence of 0:00-1:00, 1:00-2:00, 2:00-3:00, 3:00-4:00, 4:00-5:00 and 5:00-6:00 of the next day as an output parameter.
And training the short-time rainfall prediction model in the region by utilizing the powerful nonlinear function approximation function of the BP neural network. And establishing a training data set and a testing data set according to the time interval required by the rainfall prediction model.
For example, the input layer of the neural network comprises a time sequence of relevant meteorological factors such as air pressure, temperature, humidity and wind speed of the current day of the monitoring area, and the time sequence is obtained through actual monitoring data of the online monitoring module; the output layer is a rainfall sequence of the area for the next N hours; the hidden layer may be made up of one or more layers. Wherein P (T-M), P (T-M +1) … P (T), T (T-M +1) … T (T) are various relevant meteorological factors in the M time period before the T moment and are model input parameters; r (t +1) and R (t +2) … R (t + N) are respectively the predicted rainfall capacity of the region in the future of N hours at the time t and are model output variables.
102, obtaining forecast rainfall data through regional real-time meteorological data and a regional short-time rainfall forecast model;
in step 102, real-time meteorological data of each region of the city is monitored on line through relevant monitoring equipment.
Since the air pressure field, the temperature field, the humidity field and the wind field data are factors with large correlation coefficients of the BP neural network, preferably, the real-time meteorological data includes temperature, humidity, air pressure, wind speed and rainfall data.
Forecasting the forecast rainfall data [ R (T +1), R (T +2), …, R (T + N) ] in the future N time period by using meteorological data [ P (T-M), P (T-M +1) … P (T), …, T (T-M), T (T-M +1) … T (T) in the M time period before the current time T of the region by using a short-time rainfall forecasting model. Wherein, P (T-M), P (T-M +1) … P (T) are air pressure sequence data in M time period before T time, T (T-M), T (T-M +1) … T (T) are temperature sequence data in M time period before T time, R (T +1), R (T +2), …, and R (T + N) are regional forecast rainfall sequence data in N time period after T time
103, calculating a short-time drainage model of the area by using an SWMM model technology and the predicted rainfall data;
in step 103, an SWMM model is constructed according to the actual situation of the urban drainage pipe network system, and preferably, the data for constructing the SWMM model comprises pipe network general survey data, land utilization type data, auxiliary structure data and sponge measure data.
The real-time rainfall data is contained in real-time meteorological data;
and (3) performing two-dimensional simulation prediction on real-time liquid level data, flow, ground flooded area, depth and the like of each area of the city under the rainfall prediction scene by using a future rainfall sequence [ R (t +1), R (t +2), …, R (t + N) ] predicted by a rainfall prediction module and a city drainage pipe network SWMM model.
Example 2
Fig. 2 is a flow chart of another embodiment of a method for monitoring and warning urban drainage.
102, obtaining forecast rainfall data through regional real-time meteorological data and a regional short-time rainfall forecast model;
103, calculating a short-time drainage model of the area by using an SWMM model technology and the predicted rainfall data;
and step 104, dividing the waterlogging risk level according to the regional short-time drainage model, and predicting the waterlogging risk.
In step 104, a pipe network drainage force diagram is drawn by using the simulation result of the SWMM calculation module, the waterlogging risk area is identified, the cause and the countermeasure of waterlogging easiness are further analyzed, and early warning is carried out on the flood danger point position. And dividing the inland inundation risk grade by taking the ponding depth and the ponding time as measurement standards.
It should be noted that the dangerous water depth Y and the dangerous water accumulation time Z are related to the actual situation of the specific modeling area, and are not further limited herein.
Example 3
Fig. 3 is a flow chart of a third embodiment of a city drainage monitoring and early warning method.
102, obtaining forecast rainfall data through regional real-time meteorological data and a regional short-time rainfall forecast model;
103, calculating a short-time drainage model of the area by using an SWMM model technology and the predicted rainfall data;
and 105, evaluating the area short-time rainfall prediction model by using the rainfall prediction data and the real-time rainfall data.
And repeatedly training and verifying the regional short-term rainfall prediction model by using the predicted rainfall data and the real-time rainfall data, so that the model prediction accuracy is gradually improved along with the accumulation of data volume.
And 106, evaluating the regional short-time drainage model by using the SWMM model to calculate liquid level and flow data and the actually monitored liquid level and flow data.
The actually monitored liquid level and flow data are obtained by monitoring important point locations in the area on line through monitoring equipment. Preferably, the real-time liquid level data includes liquid level data of important points such as drainage canals, inspection wells, drainage ports, sunken roads, underground parking lots, urban river and lake water systems and the like.
Carrying out quantitative evaluation on the simulation precision of the SWMM model by using a Nash-Sutcliffe efficiency coefficient; and the liquid level and flow data which are actually monitored by the important nodes are used for calibrating the SWMM model parameters, so that the prediction precision of the model is improved. The concrete formula is as follows:
in the formula (2), the NSE is a Nash-Sutcliffe efficiency coefficient, the NSE value is-infinity-1, the larger the value is, the better the simulation effect is, and the lower the simulation accuracy is when the NSE is less than 0. y isiThe measured values of the liquid level and the flow data are obtained; y isi0Predicting the liquid level and flow data; y ispThe average value of the measured values of the liquid level and the flow data is obtained; and n is the length of the data sequence.
Example 4
A city drainage monitoring and early warning method comprises the following steps:
Example 5
A city drainage monitoring and early warning method comprises the following steps:
Example 6
A city drainage monitoring and early warning method comprises the following steps:
Example 7
A city drainage monitoring and early warning method comprises the following steps:
Example 8
A city drainage monitoring and early warning method comprises the following steps:
Example 9
FIG. 4 is a schematic structural diagram of a short-term rainfall prediction model in a neural network region.
The input layer of the neural network comprises time sequences of relevant meteorological factors such as air pressure, temperature, wind speed and the like, and is obtained through actual monitoring data of the online monitoring module; the output layer predicts the time sequence of rainfall for the area; the hidden layer may be made up of one or more layers. P (T-M), P (T-M +1) … P (T) are air pressure sequence data in a time period before the T moment, T (T-M), T (T-M +1) … T (T) are temperature sequence data in a time period M before the T moment, and the T (T-M), the T (T-M +1) and the P (T) are model input parameters; and R (t +1) … R (t + N) is area predicted rainfall sequence data in the time period N after the time t, and is output as a model.
For example, selecting day-by-day meteorological data of 6-9 months with large precipitation in Beijing in the last 5 years for model training. The data of 6 months, 7 months and 9 months are taken as a training sample set, and the data of 8 months are taken as a testing sample set. The input parameters are classified into 4 types of atmospheric pressure field factors, temperature field factors, humidity field factors and wind field factors: selecting 8 parts per day: 00. 16:00, next day 0: the ground air pressure of 00 and the height of 500hPa at high altitude are taken as air pressure field factors; selecting 8 parts per day: 00. 16:00, next day 0: the high altitude 500hPa temperature and the ground temperature of 00 are used as temperature field factors; selecting 8 parts per day: 00. 16:00, next day 0: a ground humidity of 00 as a humidity field factor; selecting 8: 00. 16:00, next day 0: the wind speed of 00 is used as the wind field factor. And taking the hour rainfall sequence of 0:00-1:00, 1:00-2:00, 2:00-3:00, 3:00-4:00, 4:00-5:00 and 5:00-6:00 of the next day as an output parameter.
Example 10
Fig. 5 is a structure diagram of a city drainage monitoring and early warning.
This application still provides a city drainage monitoring early warning system, contains: a meteorological data module 11, an online monitoring module 12, a rainfall prediction module 13 and an SWMM calculation module 14.
And the meteorological data module is used for collecting meteorological data in the area regularly, constructing a short-time rainfall prediction model and conveying the short-time rainfall prediction model to the rainfall prediction module.
And the online monitoring module is used for collecting real-time meteorological data and conveying the real-time meteorological data to the rainfall prediction module.
The rainfall prediction module receives the short-time rainfall prediction model and the real-time meteorological data to obtain predicted rainfall data.
And the SWMM calculation module receives the predicted rainfall data and calculates a short-time drainage model.
Further, the method also comprises the following steps: and an inland inundation early warning module 15.
The waterlogging early warning module receives the prediction result of the SWMM calculation module, carries out waterlogging risk grade division, pre-judges waterlogging risks in advance and issues early warning information.
Preferably, it further comprises: a rainfall prediction evaluation module 16 and a SWMM evaluation module 17.
And the rainfall prediction module is used for evaluating the prediction precision of the short-time rainfall prediction model through the predicted rainfall data and the actually detected rainfall data.
And the SWMM evaluation module is used for evaluating the regional short-time drainage model by using the SWMM model to calculate the liquid level and flow data and the actually monitored liquid level and flow data.
It should be noted that, in the present application, the short time generally refers to a range of 0 to 12 hours in the future, and in the present application, the specific time length varies according to the amount of meteorological data, and if the meteorological data is less, the time is short, for example, 6 hours, and if the meteorological data is more, the time is long, for example, 12 hours, which is not further limited herein.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A city drainage monitoring and early warning method is characterized by comprising the following steps:
collecting X years of historical meteorological data in the region, and simulating to generate a region short-term rainfall prediction model;
obtaining forecast rainfall data through regional real-time meteorological data and a regional short-time rainfall forecast model;
and calculating a short-time drainage model of the area by using an SWMM model technology and the predicted rainfall data.
2. The municipal drainage monitoring and early warning method according to claim 1, further comprising the steps of:
and dividing the waterlogging risk level according to the regional short-time drainage model, and predicting the waterlogging risk.
3. The municipal drainage monitoring and early warning method according to claim 1, further comprising the steps of:
and evaluating the short-term rainfall prediction model of the area by using the predicted rainfall data and the real-time rainfall data.
4. The municipal drainage monitoring and early warning method according to claim 1, further comprising the steps of:
and (4) evaluating the regional short-time drainage model by using the SWMM model to calculate liquid level and flow data and the actually monitored liquid level and flow data.
5. The municipal drainage monitoring and early warning method according to claim 1, wherein the real-time meteorological data comprises temperature, humidity, air pressure, wind speed and rainfall data.
6. The urban drainage monitoring and early warning method according to claim 1, wherein the data for constructing the SWMM model comprises pipe network census data, land use type data, auxiliary structure data and sponge measure data.
7. The urban drainage monitoring and early warning method according to claim 4, wherein the actually monitored liquid level and flow data comprise liquid level data of important points such as drainage canals, inspection wells, drainage ports, sunken roads, underground parking lots, urban river and lake water systems and the like.
8. A city drainage monitoring and early warning system using the method of any one of claims 1 to 7, comprising: the rainfall forecasting system comprises a meteorological data module, an online monitoring module, a rainfall forecasting module and an SWMM calculation module;
the meteorological data module is used for collecting meteorological data in the area regularly, constructing a short-time rainfall prediction model and conveying the short-time rainfall prediction model to the rainfall prediction module;
the online monitoring module is used for collecting real-time meteorological data and transmitting the real-time meteorological data to the rainfall prediction module;
the rainfall prediction module receives a short-time rainfall prediction model and real-time meteorological data to obtain predicted rainfall data;
and the SWMM calculation module receives the predicted rainfall data and calculates a short-time drainage model.
9. The municipal drainage monitoring and early warning system according to claim 8, further comprising: an inland inundation early warning module;
the waterlogging early warning module receives the short-time drainage model sent by the SWMM calculation module, carries out waterlogging risk grade division, pre-judges waterlogging risks in advance and issues early warning information.
10. The municipal drainage monitoring and early warning system according to claim 8, further comprising: a rainfall prediction evaluation module and an SWMM evaluation module;
the rainfall prediction module is used for evaluating the prediction precision of the short-time rainfall prediction model through the predicted rainfall data and the actually detected rainfall data;
and the SWMM evaluation module is used for evaluating the regional short-time drainage model by using the SWMM model to calculate the liquid level and flow data and the actually monitored liquid level and flow data.
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| 黄纪萍: "城市排水管网水力模拟及内涝预警***研究", 《中国优秀硕士学位论文全文数据库，工程科技‖辑》 * |
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