CN115798151A - Urban flood risk monitoring system and method - Google Patents
Urban flood risk monitoring system and method Download PDFInfo
- Publication number
- CN115798151A CN115798151A CN202211435354.XA CN202211435354A CN115798151A CN 115798151 A CN115798151 A CN 115798151A CN 202211435354 A CN202211435354 A CN 202211435354A CN 115798151 A CN115798151 A CN 115798151A
- Authority
- CN
- China
- Prior art keywords
- flood
- rainfall
- data
- urban
- ponding
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Alarm Systems (AREA)
Abstract
The invention discloses a city flood risk monitoring system and method, wherein the monitoring system comprises a rainfall statistic module for obtaining the historical rainfall data of the city; the data acquisition module is used for acquiring urban ponding data; the data analysis module is used for judging the flood occurrence probability according to the urban historical rainfall data and the urban ponding data, generating an early warning instruction and transmitting the early warning instruction to the flood early warning module; and the flood early warning module is used for carrying out early warning in different forms according to the early warning instruction. According to the method, historical rainfall data of a city are counted, ponding data of the rainfall is collected, a flood prediction model is built by adopting a BP neural network model, the rainfall type is determined according to the rising rate of the ponding, the flood occurrence probability is obtained according to the water level value corresponding to the rainfall type when the flooding occurs, flood risk monitoring is achieved, and meanwhile, different forms of early warning are carried out on flood risks of different degrees, and flood disasters are effectively avoided.
Description
Technical Field
The invention belongs to the field of flood prevention, and particularly relates to a system and a method for monitoring urban flood risks.
Background
Flood is a natural phenomenon that the peak height is large and the water level rises sharply, and waterlogging is a natural phenomenon that surface water is formed because long-term rainfall or rainstorm cannot be discharged into a river channel in time. When flood and waterlogging cause damage to human beings, the flood and waterlogging disaster occurs.
In recent years, with the development of socioeconomic, a major part of the loss of flood disasters has been transferred to cities, and the characteristics of flood have changed greatly. Many cities are along rivers, lakes, seas or mountains and water, and some cities are in plain low lands and are often threatened by flooding. Compared with rural areas, urban population and assets are highly concentrated, disaster losses are much greater, and therefore there is a need for urban flood monitoring.
Most of traditional urban flood monitoring acquire water level data through sensors, and directly judge whether flood risks exist according to the water level data, the data source of the method is single, rainfall types and flood occurrence probability cannot be accurately acquired, and cases with inaccurate judgment results are frequently rare.
Disclosure of Invention
The invention aims to provide a city flood risk monitoring system and method, which aim to solve the problems in the prior art.
On one hand, in order to achieve the purpose, the invention provides an urban flood risk monitoring system which comprises a rainfall statistic module, a data acquisition module, a data analysis module and a flood early warning module;
the rainfall statistic module is used for acquiring historical rainfall data of the city;
the data acquisition module is used for acquiring urban ponding data;
the data analysis module is used for judging the flood occurrence probability according to the historical rainfall data and the urban ponding data, generating an early warning instruction and transmitting the early warning instruction to the flood early warning module;
and the flood early warning module is used for carrying out early warning in different forms according to the early warning instruction.
Optionally, the urban historical rainfall data includes rainfall type, rainfall characteristics, flood level value, rainfall duration, and water level rise rate; the rainfall type is the main type of urban rainfall, and the rainfall characteristic is the rainfall intensity corresponding to the rainfall type; the water level rising rate is the ratio of the flood water level value to the rainfall time; the urban ponding data comprises a ponding water level value and a ponding water level rising rate.
Optionally, the data analysis module adopts a BP neural network to construct a flood prediction model, performs model training by using the historical rainfall data and the urban ponding data as input, and outputs the flood occurrence probability.
Optionally, the flood prediction model determines a rainfall type according to the ponding water level rising rate in the urban ponding data, the water level rising rate in the rainfall data and the rainfall intensity, presets an expected probability value according to the rainfall type, randomly gives a weight to each connection layer, sets a flood threshold according to a flood water level value corresponding to the rainfall type, obtains a first flood occurrence probability according to a ratio of the ponding water level value in the urban ponding data to the flood threshold, compares the output flood occurrence probability with the preset expected probability value, obtains a model error, updates the randomly given weight according to the model error, repeatedly calculates to reduce the error, and outputs the flood occurrence probability through an output layer when the model converges.
Optionally, when the flood probability reaches 70%, the data analysis module generates a flood prevention instruction and transmits the flood prevention instruction to the flood early warning module, and when the flood probability reaches 90%, the data analysis module generates an emergency early warning instruction and transmits the emergency early warning instruction to the flood early warning module.
Optionally, the flood early warning module includes an early warning lamp and an audio device, and when a flood prevention instruction is obtained, the early warning lamp is controlled to flash; and when the emergency early warning instruction is acquired, controlling the audio device to carry out audio alarm.
On the other hand, in order to achieve the above object, the present invention provides a city flood risk monitoring method, which comprises the following steps:
counting urban historical rainfall data, wherein the urban historical rainfall data comprises rainfall type, rainfall characteristics, flood level value, rainfall duration and water level rising rate; the rainfall type is the main type of urban rainfall, and the rainfall characteristic is the rainfall intensity corresponding to the rainfall type; the water level rising rate is the ratio of the flood water level value to the rainfall duration;
collecting urban ponding data, wherein the urban ponding data comprises a ponding water level value and a ponding water level rising rate;
constructing a flood prediction model based on a BP neural network, training by taking the historical rainfall data and the urban ponding data as input based on the flood prediction model, and acquiring the flood occurrence probability;
and carrying out early warning in different forms based on the flood occurrence probability.
Optionally, the training process using the historical rainfall data and the urban ponding data as input includes:
determining the type of rainfall based on the ponding water level rising rate in the urban ponding data, the water level rising rate in the rainfall data and the rainfall intensity;
presetting expected probability values based on the rainfall types, randomly giving weights to all the connecting layers, and setting a flood threshold value based on a flood water level value corresponding to the rainfall types;
acquiring a first flood occurrence probability based on the ratio of the ponding water level value in the urban ponding data to the flood threshold value, and comparing the first flood occurrence probability with a preset expected probability value to acquire a model error;
and updating the weight value randomly given based on the model error, repeatedly calculating to reduce the error, and outputting the flood occurrence probability through an output layer when the model is converged.
Optionally, when the flood occurrence probability reaches 70%, performing light form early warning; and when the flood occurrence probability reaches 90%, carrying out audio prompting early warning.
The invention has the technical effects that:
the invention provides a city flood risk monitoring system and method, which are characterized in that a flood prediction model is constructed by counting historical rainfall data of a city and collecting ponding data of the rainfall, a BP neural network model is adopted, the rainfall type is determined according to the ponding rising rate, the flood occurrence probability is obtained according to the water level value when the rainfall corresponds to the rainfall type, the flood risk monitoring is realized, and meanwhile, different forms of early warning are carried out on flood risks of different degrees, so that the flood disasters can be effectively avoided.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
fig. 1 is a schematic structural diagram of an urban flood risk monitoring system according to an embodiment of the present invention;
fig. 2 is a flowchart of a city flood risk monitoring method according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than here.
Example one
As shown in fig. 1, the embodiment provides an urban flood risk monitoring system, which includes a rainfall statistics module, a data acquisition module, a data analysis module, and a flood early warning module; specifically, the method comprises the following steps:
the rainfall statistic module is used for acquiring historical rainfall data of the city; the urban historical rainfall data refers to a main rainfall type when the urban flood occurs, rainfall characteristics, a water level value when the urban flood occurs, rainfall duration and a rising rate of the water level, wherein the rainfall characteristics are rainfall intensity corresponding to the rainfall type, and the rising rate of the water level is obtained by calculating through the rainfall statistic module according to the ratio of the obtained flood water level value to the rainfall duration.
The data acquisition module is used for acquiring ponding data at the time of urban rainfall; the ponding data is the water level value and ponding rate of ponding of this rainfall in-process urban ponding.
After historical rainfall data of the city and ponding data of the rainfall are obtained, a data analysis module constructs a flood prediction model by adopting a BP neural network, and model training is carried out by taking the historical rainfall data of the city and the ponding data of the city as input, namely, the flood occurrence probability is output; the BP neural network is one of the most widely applied neural network models at present, and comprises an input layer, a hidden layer and an output layer, wherein the specific process of acquiring the flood occurrence probability by the flood prediction model comprises the following steps: the method comprises the steps of firstly inputting urban ponding data and urban historical rainfall data on an input layer of a neural network, determining rainfall types according to ponding water level rising rates in the urban ponding data and rainfall intensity and water level rising rates in the rainfall data on a hidden layer, presetting expected probability values according to the rainfall types, randomly giving weights to all connection layers, setting flood thresholds according to flood water level values corresponding to the rainfall types, calculating according to the ratio of the ponding water level values in the urban ponding data to the flood thresholds, obtaining first flood occurrence probability, comparing the first flood occurrence probability with the preset expected probability values, obtaining model errors, updating the randomly given weights according to the model errors, repeatedly calculating to reduce errors, and outputting the accurate flood occurrence probability through an output layer when the model converges.
As a preferred embodiment of this application, when judging flood probability of occurrence is 70%, data analysis module generation flood prevention instruction transmits extremely flood early warning module works as flood probability of occurrence is 90%, data analysis module generation urgent early warning instruction transmits extremely flood early warning module, and the specific threshold value setting in this section is carried out the artificial settlement according to actual demand, and all belongs to the protection scope of this application.
The flood early warning module comprises a plurality of early warning lamps and a portable audio device, and when a flood prevention instruction is acquired, the flood early warning module controls the early warning lamps to flicker to carry out early warning and informs workers to take preventive measures in advance; when an emergency early warning instruction is obtained, the flood early warning module controls the portable audio device to sound, and alarms in an audio form to inform workers carrying the audio device to take preventive measures immediately.
Example two
As shown in fig. 2, the present embodiment provides a method for monitoring urban flood risk, including the following steps:
counting urban historical rainfall data, wherein the urban historical rainfall data comprises rainfall types, rainfall characteristics, flood water level values, rainfall duration and water level rising rate; the rainfall type is the main type of urban rainfall, and the rainfall characteristic is the rainfall intensity corresponding to the rainfall type; the water level rising rate is the ratio of the flood water level value to the rainfall duration;
collecting urban ponding data, wherein the urban ponding data comprises a ponding water level value and a ponding water level rising rate;
constructing a flood prediction model based on a BP neural network, training by taking the historical rainfall data and the urban ponding data as input based on the flood prediction model, and acquiring the flood occurrence probability;
and carrying out early warning in different forms based on the flood occurrence probability.
As a preferred embodiment of the present application, the process of training the historical rainfall data and the urban ponding data as input includes:
determining the type of rainfall based on the ponding water level rising rate in the urban ponding data, the water level rising rate in the rainfall data and the rainfall intensity;
presetting expected probability values based on the rainfall types, randomly giving weights to all the connecting layers, and setting a flood threshold value based on a flood water level value corresponding to the rainfall types;
acquiring a first flood occurrence probability based on the ratio of the ponding water level value in the urban ponding data to the flood threshold value, and comparing the first flood occurrence probability with a preset expected probability value to acquire a model error;
and updating the randomly given weight value based on the model error, repeatedly calculating to reduce the error, and outputting the flood occurrence probability through an output layer when the model is converged.
As a preferred embodiment of the present application, when the flood occurrence probability reaches 70%, performing light form early warning; and when the flood occurrence probability reaches 90%, carrying out audio prompting early warning.
The embodiments in the present description 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 method disclosed by the embodiment corresponds to the system disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the system part for description.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. A city flood risk monitoring system is characterized by comprising a rainfall statistic module, a data acquisition module, a data analysis module and a flood early warning module;
the rainfall statistic module is used for acquiring historical rainfall data of the city;
the data acquisition module is used for acquiring urban ponding data;
the data analysis module is used for judging the flood occurrence probability according to the historical rainfall data and the urban ponding data, generating an early warning instruction and transmitting the early warning instruction to the flood early warning module;
and the flood early warning module is used for carrying out early warning in different forms according to the early warning instruction.
2. The urban flood risk monitoring system according to claim 1, wherein the urban historical rainfall data comprises rainfall type, rainfall characteristics, flood level value, rainfall duration, water level rise rate; the rainfall type is the main type of urban rainfall, and the rainfall characteristic is the rainfall intensity corresponding to the rainfall type; the water level rising rate is the ratio of the flood water level value to the rainfall time; the urban ponding data comprises a ponding water level value and a ponding water level rising rate.
3. The urban flood risk monitoring system according to claim 1, wherein the data analysis module adopts a BP neural network to construct a flood prediction model, performs model training with the historical rainfall data and the urban ponding data as inputs, and outputs the flood occurrence probability.
4. The urban flood risk monitoring system according to claim 3, wherein the flood prediction model determines a rainfall type according to a ponding water level rising rate in the urban ponding data, a water level rising rate in the rainfall data, and a rainfall intensity, presets an expected probability value according to the rainfall type, randomly assigns weights to each connection layer, sets a flood threshold according to a flood level value corresponding to the rainfall type, obtains a first flood occurrence probability according to a ratio of the ponding water level value in the urban ponding data to the flood threshold, compares the output flood occurrence probability with the preset expected probability value, obtains a model error, updates the randomly assigned weights according to the model error, repeats calculation to reduce the error, and outputs the flood occurrence probability through the output layer when the model converges.
5. The urban flood risk monitoring system according to claim 4, wherein when the flood occurrence probability reaches 70%, the data analysis module generates a flood prevention instruction to transmit to the flood early warning module, and when the flood occurrence probability reaches 90%, the data analysis module generates an emergency early warning instruction to transmit to the flood early warning module.
6. The urban flood risk monitoring system according to claim 1, wherein the flood warning module comprises a warning light and an audio device, and when a flood prevention instruction is obtained, the warning light is controlled to flash; and when the emergency early warning instruction is acquired, controlling the audio device to carry out audio alarm.
7. A city flood risk monitoring method is characterized by comprising the following steps:
counting urban historical rainfall data, wherein the urban historical rainfall data comprises rainfall types, rainfall characteristics, flood water level values, rainfall duration and water level rising rate; the rainfall type is the main type of urban rainfall, and the rainfall characteristic is the rainfall intensity corresponding to the rainfall type; the water level rising rate is the ratio of the flood water level value to the rainfall duration;
collecting urban ponding data, wherein the urban ponding data comprises a ponding water level value and a ponding water level rising rate;
constructing a flood prediction model based on a BP neural network, training by taking the historical rainfall data and the urban ponding data as input based on the flood prediction model, and acquiring the flood occurrence probability;
and carrying out early warning in different forms based on the flood occurrence probability.
8. The method of claim 7, wherein the process of training the historical rainfall data and the municipal water data as inputs comprises:
determining the type of rainfall based on the ponding water level rising rate in the urban ponding data, the water level rising rate in the rainfall data and the rainfall intensity;
presetting expected probability values based on the rainfall types, randomly giving weights to all the connecting layers, and setting a flood threshold value based on a flood water level value corresponding to the rainfall types;
acquiring a first flood occurrence probability based on the ratio of the ponding water level value in the urban ponding data to the flood threshold value, and comparing the first flood occurrence probability with a preset expected probability value to acquire a model error;
and updating the randomly given weight value based on the model error, repeatedly calculating to reduce the error, and outputting the flood occurrence probability through an output layer when the model is converged.
9. The urban flood risk monitoring method according to claim 7, wherein a light form pre-warning is performed when the flood occurrence probability reaches 70%; and when the flood occurrence probability reaches 90%, carrying out audio prompting early warning.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211435354.XA CN115798151A (en) | 2022-11-16 | 2022-11-16 | Urban flood risk monitoring system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211435354.XA CN115798151A (en) | 2022-11-16 | 2022-11-16 | Urban flood risk monitoring system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115798151A true CN115798151A (en) | 2023-03-14 |
Family
ID=85438180
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211435354.XA Pending CN115798151A (en) | 2022-11-16 | 2022-11-16 | Urban flood risk monitoring system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115798151A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116611333A (en) * | 2023-05-23 | 2023-08-18 | 中国水利水电科学研究院 | Urban flood risk point prediction method |
| CN116861317A (en) * | 2023-09-04 | 2023-10-10 | 北京建筑大学 | Cell waterlogging early warning method and system based on BP neural network |
| CN117576876A (en) * | 2023-11-17 | 2024-02-20 | 中国水利水电科学研究院 | Urban flood disaster early warning method for potential involved personnel |
| CN117831231A (en) * | 2024-03-05 | 2024-04-05 | 南京金固智慧市政研究院有限公司 | Method for carrying out flooding early warning on easily flooded and easily waterlogged areas |
-
2022
- 2022-11-16 CN CN202211435354.XA patent/CN115798151A/en active Pending
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116611333A (en) * | 2023-05-23 | 2023-08-18 | 中国水利水电科学研究院 | Urban flood risk point prediction method |
| CN116611333B (en) * | 2023-05-23 | 2023-11-14 | 中国水利水电科学研究院 | Urban flood risk point prediction method |
| CN116861317A (en) * | 2023-09-04 | 2023-10-10 | 北京建筑大学 | Cell waterlogging early warning method and system based on BP neural network |
| CN117576876A (en) * | 2023-11-17 | 2024-02-20 | 中国水利水电科学研究院 | Urban flood disaster early warning method for potential involved personnel |
| CN117576876B (en) * | 2023-11-17 | 2024-05-14 | 中国水利水电科学研究院 | Urban flood disaster early warning method for potential go through dangers people |
| CN117831231A (en) * | 2024-03-05 | 2024-04-05 | 南京金固智慧市政研究院有限公司 | Method for carrying out flooding early warning on easily flooded and easily waterlogged areas |
| CN117831231B (en) * | 2024-03-05 | 2024-05-10 | 南京金固智慧市政研究院有限公司 | Method for carrying out flooding early warning on easily flooded and easily waterlogged areas |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115798151A (en) | Urban flood risk monitoring system and method | |
| CN109784746A (en) | Urban-rural water supply network waterpower simulation of water quality platform | |
| CN108320462A (en) | Urban Flood control early-warning and predicting system | |
| CN115471078B (en) | Flood risk point assessment method and device based on urban water affair system | |
| CN112329257A (en) | Hydrological model segmented screening method suitable for forecasting and early warning of torrential rain, torrential flood and flood in small watershed of mountainous area | |
| CN111325947B (en) | Storm surge flood early warning method and system based on long-term and short-term memory network | |
| CN113011903A (en) | Water pollution accurate tracing method based on GIS and hydraulic model | |
| CN104318058A (en) | Mudslide early warning method based on rainfall monitoring | |
| CN106801463A (en) | The hull-borne detection method and system of drainpipe | |
| CN110675579A (en) | Reservoir early warning monitoring method and system | |
| CN112926216A (en) | Sponge city construction evaluation method combining full life cycle cost and hydrological effect | |
| CN112528563A (en) | Urban waterlogging early warning method based on SVM algorithm | |
| CN116469227A (en) | Drainage basin flood early warning system | |
| CN207924874U (en) | Urban Flood control early-warning and predicting device | |
| CN117275188A (en) | Mountain torrent disaster monitoring and early warning system and mountain torrent disaster monitoring and early warning method | |
| CN113869804B (en) | Power grid equipment risk early warning method and system under flood disaster | |
| CN113256117A (en) | Underground infrastructure flood risk analysis system and method based on Internet of things | |
| CN109086978A (en) | A kind of drainage system against rain waterlogging methods of risk assessment | |
| CN110570628B (en) | Power transmission line pole tower geological disaster monitoring, early warning and analyzing system and using method | |
| CN204963941U (en) | Water level rising early warning device | |
| CN203310475U (en) | Warning system for abnormal water level in fire pool or water tank | |
| CN116861317A (en) | Cell waterlogging early warning method and system based on BP neural network | |
| CN115858281A (en) | Energy consumption management system and method based on Internet of things | |
| Panganiban | Rainfall measurement and flood warning systems: A review | |
| TWI592890B (en) | Method for generating two-dimensional flood potential map |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |