CN116611333A - Urban flood risk point prediction method - Google Patents
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Abstract
The invention discloses a method for predicting urban flood risk points, which belongs to the technical field of urban flood risk point identification and prediction, and comprises the steps of obtaining and establishing a rainfall event library according to historical daily rainfall data of a rainfall station and accumulated water data of an urban water collecting area; according to the rainfall event library, calculating the occurrence condition probability of the ponding; obtaining rainfall probability prediction data, predicting future flood risk occurrence probability according to the ponding occurrence condition probability and the rainfall probability prediction data, completing urban flood risk point prediction, and sending out early warning according to the flood risk occurrence probability; and carrying out error analysis on the occurrence probability of the flood risk to obtain an error analysis result, updating a rainfall event base according to the error analysis result, and carrying out urban flood risk point prediction based on the updated rainfall event base. The method solves the problem that the existing urban flood risk point prediction method is low in efficiency.
Description
Technical Field
The invention belongs to the technical field of urban flood risk point identification and prediction, and particularly relates to an urban flood risk point prediction method.
Background
In urban flood control, urban flood risk point prediction is an important foundation for water accumulation point monitoring facilities, flood control and drainage facility planning and design and emergency plan formulation. Although the urban flood risk point prediction is mainly based on a numerical model or a method combining numerical simulation and machine learning, and the urban hydrologic model and the machine learning method are greatly improved, the most advanced simulation method still is not fast enough at present, and particularly, the urban water collecting area refined simulation and the application requiring iterative analysis are realized.
Disclosure of Invention
Aiming at the defects in the prior art, the urban flood risk point prediction method provided by the invention solves the problem of low efficiency of the existing urban flood risk point prediction method.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a city flood risk point prediction method comprises the following steps:
s1, acquiring and establishing a rainfall event library according to historical daily rainfall data of a rainfall station and accumulated water data of an urban water collecting area;
s2, calculating the occurrence condition probability of the ponding according to the rainfall event library;
s3, rainfall probability prediction data are obtained, future flood risk occurrence probability is predicted according to the ponding occurrence condition probability and the rainfall probability prediction data, urban flood risk point prediction is completed, and early warning is sent according to the flood risk occurrence probability;
s4, carrying out error analysis on the flood risk occurrence probability to obtain an error analysis result, updating a rainfall event base according to the error analysis result, and returning to the step S2.
The beneficial effects of the invention are as follows: urban flood risk point prediction is carried out based on statistical data, and the rapid prediction of flood risk points can be realized without elaborate model simulation; the rainfall probability prediction is combined with the historical rainfall and flood event statistical data, and the condition probability is adopted to predict the storm flood risk, so that the extreme storm risk prediction precision can be improved, urban water affairs and emergency departments are effectively supported, and a prevention and control plan is formulated.
Further, the step S1 specifically includes:
s101, grid division is carried out on an urban water collecting area, and a division result is obtained;
s102, acquiring historical daily rainfall data of a rainfall station and accumulated water data of an urban water collecting area, and acquiring rainfall events and accumulated water samples according to the historical daily rainfall data of the rainfall station, the accumulated water data of the urban water collecting area and the dividing result:
e i ＝{[r i ,t i ]}
c i ＝{[f ij ,t i ]}
wherein e i Is the ith rainfall event; r is (r) i Daily rainfall data for the ith rainfall event; t is t i A rainfall date for the ith rainfall event; i is rainfall event number; c i A ponding sample in the ith rainfall event; f (f) ij The method comprises the steps of marking whether a space grid of a jth urban water collecting area is ponding or not in an ith rainfall event; j is the space grid number of the urban water collecting area;
s103, obtaining a rainfall event library according to rainfall events and ponding samples.
The beneficial effects of the above-mentioned further scheme are: based on the space grid, a database of daily rainfall events and ponding samples is established, the space variability of the data of the rainfall events and the data of the ponding samples is fully considered, and the accuracy of statistical data is effectively improved.
Further, the step S2 specifically includes:
s201, according to a rainfall event library, carrying out rainfall grade division based on rainfall, and obtaining a rainfall grade division result;
s202, calculating the occurrence condition probability of the ponding according to the rainfall grade division result:
wherein P is j (c|e k ) The probability of the occurrence condition of the ponding when the rainfall grade is the kth grade; j is the space grid number of the urban water collecting area; k is rainfall grade number; c is the total collection of ponding samples; e, e k A rainfall event total set when the rainfall grade is the kth grade; count (f) ij ) k The number of times of water accumulation occurs when the rainfall grade is the kth grade; f (f) ij The method comprises the steps of marking whether a space grid of a jth urban water collecting area is ponding or not in an ith rainfall event; count (r) i ) k The number of times of occurrence of rainfall events when the rainfall level is the kth level; r is (r) i Daily rainfall data for the ith rainfall event.
The beneficial effects of the above-mentioned further scheme are: and carrying out rainfall grade division based on rainfall, respectively calculating the water accumulation occurrence condition probability under the occurrence condition of a certain grade rainfall event, and improving the accuracy of the water accumulation occurrence condition probability in the statistical rainfall event.
Further, the expression of the flooding risk occurrence probability in the step S3 is:
wherein P is j (c) The probability of occurrence of flood risk is represented as the probability of ponding of the spatial grid of the jth urban water collection area; p (e) k ) Rainfall probability prediction data for hierarchical prediction; p (P) j (c|e k ) The probability of the occurrence condition of the ponding when the rainfall grade is the kth grade is given to the space grid of the jth urban water collecting area; j is the space grid number of the urban water collecting area; k is rainfall grade number; v is the maximum level of rainfall level.
The beneficial effects of the above-mentioned further scheme are: based on the hierarchical prediction rainfall and the corresponding prediction rainfall probability data, the ponding probability under the corresponding prediction rainfall event is calculated, so that the prediction accuracy of the extreme storm risk can be improved, and the urban water affair and emergency departments are effectively supported to formulate a prevention and control plan.
Further, the step S4 specifically includes:
s401, acquiring actual measurement ponding data of space grids of water collecting areas of all cities;
s402, according to the occurrence probability of flood risk, obtaining spatial grid prediction ponding data of each urban water collecting area;
s403, carrying out error analysis according to the predicted ponding data of the spatial grids of the water collecting areas of the cities and the actual measured ponding data of the spatial grids of the water collecting areas of the cities to obtain an error analysis result:
wherein RMSE is the error analysis result; f (f) ij ' is the actual measurement ponding data of the space grid of the jth urban water collecting area in the ith rainfall event;predicting ponding data for a spatial grid of a jth urban water collecting area in an ith rainfall event; i is rainfall event number; j is the space grid number of the urban water collecting area; n is the total number of space grids of the urban water collecting area;
s404, judging whether the error analysis result is smaller than a set threshold value, if so, not updating the rainfall event library, otherwise, removing early data in the rainfall event library, supplementing newly added daily rainfall data, obtaining a new rainfall event library, and returning to the step S2.
The beneficial effects of the above-mentioned further scheme are: the basic condition change caused by urban development and continuous updating and accumulation of urban rainfall events and water accumulation sample data are considered, and the sample capacity is gradually updated, so that the matching property and the prediction precision of the method and the actual conditions can be effectively ensured.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, in one embodiment of the present invention, a method for predicting urban flood risk points includes the following steps:
s1, acquiring and establishing a rainfall event library according to historical daily rainfall data of a rainfall station and accumulated water data of an urban water collecting area;
s2, calculating the occurrence condition probability of the ponding according to the rainfall event library;
s3, rainfall probability prediction data are obtained, future flood risk occurrence probability is predicted according to the ponding occurrence condition probability and the rainfall probability prediction data, urban flood risk point prediction is completed, and early warning is sent according to the flood risk occurrence probability;
s4, carrying out error analysis on the flood risk occurrence probability to obtain an error analysis result, updating a rainfall event base according to the error analysis result, and returning to the step S2.
The step S1 specifically comprises the following steps:
s101, grid division is carried out on an urban water collecting area, and a division result is obtained;
s102, acquiring historical daily rainfall data of a rainfall station and accumulated water data of an urban water collecting area, and acquiring rainfall events and accumulated water samples according to the historical daily rainfall data of the rainfall station, the accumulated water data of the urban water collecting area and the dividing result:
e i ＝{[r i ,t i ]}
c i ＝{[f ij ,t i ]}
wherein e i Is the ith rainfall event; r is (r) i Daily rainfall data for the ith rainfall event; t is t i A rainfall date for the ith rainfall event; i is rainfall event number; c i A ponding sample in the ith rainfall event; f (f) ij For the jth city in the ith rainfall eventThe identification of whether the water collecting area space grid is accumulated or not; j is the space grid number of the urban water collecting area;
s103, obtaining a rainfall event library according to rainfall events and ponding samples.
In this embodiment, first, historical daily rainfall data of a city water collecting area of a rainfall station for approximately 5 years is obtained, the city water collecting area is divided into a plurality of grid units of 30m by 30m, historical water accumulation point data of the city water collecting area for approximately 5 years is obtained, and a rainfall event base is established.
The data format of the sample library rainfall event is assumed to be:
e i ＝{[r i ,t i ]}
wherein e i Is the ith rainfall event; r is (r) i Daily rainfall data for the ith rainfall event; t is t i A rainfall date for the ith rainfall event; i is the rainfall event number.
Assume that the data format of the ponding sample in the rainfall event library is:
c i ＝{[f ij ,t i ]}
wherein c i For the ith water sample; f (f) ij The method comprises the steps of marking whether a space grid of a jth urban water collecting area is ponding or not in an ith rainfall event; j is the space grid number of the urban water collecting area.
Based on the space grid, a database of daily rainfall event and water accumulation sample data is established, the space variability of the rainfall event data and the water accumulation sample data is fully considered, and the accuracy of statistical data is effectively improved.
The step S2 specifically comprises the following steps:
s201, according to a rainfall event library, carrying out rainfall grade division based on rainfall, and obtaining a rainfall grade division result;
s202, calculating the occurrence condition probability of the ponding according to the rainfall grade division result:
wherein P is j (c|e k ) To reduce downThe occurrence condition probability of ponding when the rain grade is the kth grade; j is the space grid number of the urban water collecting area; k is rainfall grade number; c is the total collection of ponding samples; e, e k A rainfall event total set when the rainfall grade is the kth grade; count (f) ij ) k The number of times of water accumulation occurs when the rainfall grade is the kth grade; f (f) ij The method comprises the steps of marking whether a space grid of a jth urban water collecting area is ponding or not in an ith rainfall event; count (r) i ) k The number of times of occurrence of rainfall events when the rainfall level is the kth level; r is (r) i Daily rainfall data for the ith rainfall event.
In this embodiment, daily rainfall events are classified into five classes (e.g., light rain: r i Less than or equal to 10 (mm); middle rain: 10<r i Less than or equal to 25 (mm); heavy rain: 25<r i Less than or equal to 50 (mm); stormwater: 50<r i Less than or equal to 100 (mm); heavy storm: 100<r i Less than or equal to 200 (mm); extreme heavy rain: r is (r) i 200 (mm) or more). Calculating the occurrence probability of ponding under a certain level of rainfall event:
wherein P is j (c|e k ) The probability of the occurrence condition of the ponding when the rainfall grade is the kth grade; j is the space grid number of the urban water collecting area; k is rainfall grade number; c is the total collection of ponding samples; e, e k A rainfall event total set with the rainfall grade being the kth grade; count (f) ij ) k The number of times of water accumulation occurs when the rainfall grade is the kth grade; f (f) ij The method comprises the steps of marking whether a space grid of a jth urban water collecting area is ponding or not in an ith rainfall event; count (r) i ) k The number of times of occurrence of rainfall events when the rainfall level is the kth level; r is (r) i Daily rainfall data for the ith rainfall event.
The rainfall data are divided into five grades, the conditional probability of water accumulation under the occurrence condition of a rainfall event of a certain grade is calculated respectively, and the accuracy of the statistical rainfall water accumulation occurrence probability is improved.
The expression of the flood risk occurrence probability in the step S3 is as follows:
wherein P is j (c) The probability of occurrence of flood risk is represented as the probability of ponding of the spatial grid of the jth urban water collection area; p (e) k ) Rainfall probability prediction data for hierarchical prediction; p (P) j (c|e k ) The probability of the occurrence condition of the ponding when the rainfall grade is the kth grade is given to the space grid of the jth urban water collecting area; j is the space grid number of the urban water collecting area; k is rainfall grade number; v is the maximum level of rainfall level.
In this embodiment, it is assumed that the rainfall forecast data includes a hierarchical prediction rainfall and a corresponding prediction rainfall probability, and a future flooding risk occurrence probability calculation formula is:
wherein P is j (c) The flood risk occurrence probability; p (e) k ) Rainfall probability prediction data for hierarchical prediction; p (P) j (c|e k ) The probability of the occurrence condition of the ponding when the rainfall grade is the kth grade; j=1, 2,3 … n, representing the spatial grid number of the urban catchment area; k=i, ii, iii, iv, v, and represents the rainfall level.
And if the calculated flood risk occurrence probability exceeds 50%, the possibility of water accumulation is considered to exist, and early warning and planning are needed.
Based on the hierarchical prediction rainfall and the corresponding prediction rainfall probability data, the ponding probability under the corresponding prediction rainfall condition is calculated, so that the extreme storm risk prediction accuracy can be improved, and the urban water affair and emergency departments are effectively supported to formulate a prevention and control plan.
The step S4 specifically includes:
s401, acquiring actual measurement ponding data of space grids of water collecting areas of all cities;
s402, according to the occurrence probability of flood risk, obtaining spatial grid prediction ponding data of each urban water collecting area;
s403, carrying out error analysis according to the predicted ponding data of the spatial grids of the water collecting areas of the cities and the actual measured ponding data of the spatial grids of the water collecting areas of the cities to obtain an error analysis result:
wherein RMSE is the error analysis result; f (f) ij ' is the actual measurement ponding data of the space grid of the jth urban water collecting area in the ith rainfall event;predicting ponding data for a spatial grid of a jth urban water collecting area in an ith rainfall event; i is rainfall event number; j is the space grid number of the urban water collecting area; n is the total number of space grids of the urban water collecting area;
s404, judging whether the error analysis result is smaller than a set threshold value, if so, not updating the rainfall event library, otherwise, removing early data in the rainfall event library, supplementing newly added daily rainfall data, obtaining a new rainfall event library, and returning to the step S2.
In this embodiment, (1) taking into consideration the urban development and the water accumulation point remediation, etc., the basic conditions of the water collecting area are gradually changed, and the model is subjected to error analysis by combining the actually measured water accumulation point data and the predicted water accumulation point data, and the sample capacity is continuously updated by adopting a mode of periodically removing early samples and supplementing new samples.
Wherein f ij ' is the actual measurement ponding point data of the space grid of the jth urban water collecting area in the ith rainfall event;predicting ponding point data for a spatial grid of a jth urban water collecting area in an ith rainfall event; n is the total number of space grids in the urban water collecting area.
The error is smaller than a preset threshold value, the original rainfall event library is still adopted for carrying out ponding prediction on the next rainfall event, if the error is larger than the preset threshold value, early samples are removed, newly added rainfall events are supplemented, and a new event library is established.
(2) Considering that the basic conditions of certain grid units of the water collecting area can be suddenly changed when the water accumulation points are remedied, so that the original predicted sample is not accurate any more, taking time nodes such as the water accumulation point remediation as a boundary, removing the grid sample before the water accumulation point remediation, and updating the sample capacity.
The change of the basic condition caused by urban development and the continuous update and accumulation of the urban rainfall ponding event data are considered, the sample capacity is gradually updated, and the matching property and the prediction precision of the method and the actual condition can be effectively ensured.
Claims (5)
1. The urban flood risk point prediction method is characterized by comprising the following steps of:
s1, acquiring and establishing a rainfall event library according to historical daily rainfall data of a rainfall station and accumulated water data of an urban water collecting area;
s2, calculating the occurrence condition probability of the ponding according to the rainfall event library;
s3, rainfall probability prediction data are obtained, future flood risk occurrence probability is predicted according to the ponding occurrence condition probability and the rainfall probability prediction data, urban flood risk point prediction is completed, and early warning is sent according to the flood risk occurrence probability;
s4, carrying out error analysis on the flood risk occurrence probability to obtain an error analysis result, updating a rainfall event base according to the error analysis result, and returning to the step S2.
2. The urban flood risk point prediction method according to claim 1, wherein the step S1 specifically comprises:
s101, grid division is carried out on an urban water collecting area, and a division result is obtained;
s102, acquiring historical daily rainfall data of a rainfall station and accumulated water data of an urban water collecting area, and acquiring rainfall events and accumulated water samples according to the historical daily rainfall data of the rainfall station, the accumulated water data of the urban water collecting area and the dividing result:
e i ＝{[r i ,t i ]}
c i ＝{[f ij ,t i ]}
wherein e i Is the ith rainfall event; r is (r) i Daily rainfall data for the ith rainfall event; t is t i A rainfall date for the ith rainfall event; i is rainfall event number; c i A ponding sample in the ith rainfall event; f (f) ij The method comprises the steps of marking whether a space grid of a jth urban water collecting area is ponding or not in an ith rainfall event; j is the space grid number of the urban water collecting area;
s103, obtaining a rainfall event library according to rainfall events and ponding samples.
3. The urban flood risk point prediction method according to claim 1, wherein the step S2 specifically comprises:
s201, according to a rainfall event library, carrying out rainfall grade division based on rainfall, and obtaining a rainfall grade division result;
s202, calculating the occurrence condition probability of the ponding according to the rainfall grade division result:
wherein P is j (c|e k ) The probability of the occurrence condition of the ponding when the rainfall grade is the kth grade; j is the space grid number of the urban water collecting area; k is rainfall grade number; c is the total collection of ponding samples; e, e k A rainfall event total set when the rainfall grade is the kth grade; count (f) ij ) k The number of times of water accumulation occurs when the rainfall grade is the kth grade; f (f) ij The method comprises the steps of marking whether a space grid of a jth urban water collecting area is ponding or not in an ith rainfall event; count (r) i ) k The number of times of occurrence of rainfall events when the rainfall level is the kth level; r is (r) i Daily rainfall data for the ith rainfall event.
4. The urban flood risk point prediction method according to claim 1, wherein the expression of the probability of occurrence of the flood risk in step S3 is:
wherein P is j (c) The probability of occurrence of flood risk is represented as the probability of ponding of the spatial grid of the jth urban water collection area; p (e) k ) Rainfall probability prediction data for hierarchical prediction; p (P) j (c|e k ) The probability of the occurrence condition of the ponding when the rainfall grade is the kth grade is given to the space grid of the jth urban water collecting area; j is the space grid number of the urban water collecting area; k is rainfall grade number; v is the maximum level of rainfall level.
5. The urban flood risk point prediction method according to claim 1, wherein the step S4 is specifically:
s401, acquiring actual measurement accumulated water point data of space grids of water collecting areas of all cities;
s402, according to the occurrence probability of flood risk, obtaining the spatial grid prediction ponding point data of each urban water collecting area;
s403, carrying out error analysis according to the predicted water accumulation point data of the spatial grids of the water collecting areas of the cities and the actual measurement water accumulation point data of the spatial grids of the water collecting areas of the cities to obtain an error analysis result:
wherein RMSE is the error analysis result; f (f) ij ' is the actual measurement ponding point data of the space grid of the jth urban water collecting area in the ith rainfall event;predicting ponding point data for a spatial grid of a jth urban water collecting area in an ith rainfall event; i is the dropRain event numbering; j is the space grid number of the urban water collecting area; n is the total number of space grids of the urban water collecting area;
s404, judging whether the error analysis result is smaller than a set threshold value, if so, not updating the rainfall event library, otherwise, removing early data in the rainfall event library, supplementing newly added daily rainfall data, obtaining a new rainfall event library, and returning to the step S2.
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| CN117010726A (en) * | 2023-09-29 | 2023-11-07 | 水利部交通运输部国家能源局南京水利科学研究院 | Intelligent early warning method and system for urban flood control |
| 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 |
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