CN115330254A - Mountain torrent comprehensive risk early warning method, system and storage medium - Google Patents
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Abstract
The application relates to a method, a system and a storage medium for mountain torrent comprehensive risk early warning, which belong to the field of disaster monitoring, wherein the method comprises the steps of acquiring attribute information of a target small watershed based on a preset mountain torrent risk basic attribute data set; classifying the attribute information to obtain attribute factors; the attribute factors comprise rainfall factors, terrain factors and social factors; constructing a correlation coefficient matrix for the attribute factors; performing principal component analysis on the attribute factors based on the correlation coefficient matrix to obtain a plurality of key factors; normalizing the plurality of key factors to obtain the weight of the risk factor; calculating a risk index based on the risk factor weight; and determining the early warning grade corresponding to the risk index based on a preset risk index range. The application has the effect of effectively improving the accuracy of the early warning of the torrential rain and torrential flood disasters in the small watershed.
Description
Technical Field
The application relates to the field of disaster monitoring, in particular to a mountain torrent comprehensive risk early warning method, a mountain torrent comprehensive risk early warning system and a storage medium.
Background
The early warning of torrential rain and torrential flood disasters in small watershed is one of the most effective means for defending the torrential flood disasters. If the early warning information is sent out in time when or before a disaster happens, casualties and property loss can be effectively reduced. However, the mechanism of the formation process of the torrential rain torrential flood in the small watershed is complex, and the torrential flood forecasting and early warning method in the prior art is mainly analyzed based on 24-hour rainfall point forecasting data, namely, rainfall conditions, torrential factors and influence factors of the torrential flood are comprehensively considered, and finally, the area and early warning level where the torrential flood disaster possibly occurs are determined, so that the early warning period is effectively prolonged.
In view of the prior art described above, the applicant believes that, because the forecast of short duration rainfall is often inaccurate in the local world, namely, the forecasting precision is low, and the accuracy of the early warning period in the prior art is greatly reduced under the condition of inaccurate forecasting precision.
Disclosure of Invention
In order to effectively improve the accuracy of early warning of torrential rain and torrential flood disasters in small watershed, the application provides a method, a system and a storage medium for early warning of torrential flood comprehensive risks.
In a first aspect, the comprehensive risk early warning method for mountain torrents provided by the application adopts the following technical scheme:
a mountain torrent comprehensive risk early warning method comprises the following steps:
acquiring attribute information of a target small watershed based on a preset torrential flood risk basic attribute data set;
classifying the attribute information to obtain attribute factors; the attribute factors comprise rainfall factors, terrain factors and social factors;
constructing a correlation coefficient matrix for the attribute factors;
performing principal component analysis on the attribute factors based on the correlation coefficient matrix to obtain a plurality of key factors;
normalizing the key factors to obtain the weight of the risk factors;
calculating a risk index based on the risk factor weight;
and determining the early warning grade corresponding to the risk index based on a preset risk index range.
By adopting the technical scheme, the attribute factors are subjected to principal component analysis to obtain the key factors, then the risk factor indexes are obtained based on the key factors, and the risk indexes are calculated based on the risk factor indexes, so that quantification of early warning of the torrential rainfall torrential flood disaster in the small watershed is realized, and the accuracy of the early warning of the torrential rainfall torrential flood disaster in the small watershed are effectively improved.
Optionally, the torrential flood risk basic attribute data set comprises vector data of a torrential rain atlas, attribute data of a small watershed and forecast rainfall data;
before the classifying the attribute information, the method includes:
combining the stormwater atlas vector data with the small watershed attribute data;
and interpolating the forecast rainfall data into grid data with preset resolution based on an inverse distance weight method.
By adopting the technical scheme, the rainstorm map set vector data is historical statistical data, the rainstorm map set vector data is combined with the small watershed attribute data and is used for judging the mountain flood resistance risk capability of the small watershed, namely the small watershed which often generates rainstorm is judged to have strong risk resistance, the small watershed which does not often generate rainstorm is judged to have weak risk resistance, namely the risk resistance factor is increased in the small watershed attribute data, and the judgment of follow-up disaster early warning is more accurate.
Optionally, the performing principal component analysis on the attribute factors based on the correlation coefficient matrix to obtain a plurality of key factors includes:
calculating the factor sensitivity of the attribute factors based on a preset principal component analysis model and a preset certainty coefficient model; determining key factors according to the factor sensitivity.
By adopting the technical scheme, the principal component analysis model is used for converting a plurality of indexes into a few comprehensive indexes, the certainty coefficient model is used for realizing the quantification of the complex multi-factor data, and the key factors are determined, so that the influence factors of the mountain torrent disaster can be analyzed, and the follow-up quantification of the mountain torrent disaster can be laid.
Optionally, the calculating the factor sensitivity of the attribute factor based on a preset principal component analysis model and a preset certainty coefficient model includes:
calculating a first ratio of the number of disasters causing the mountain torrent disasters to the preset area of the target area under each attribute factor;
calculating a second ratio of the number of the disasters under each attribute factor to a preset area of a hill area;
respectively calculating the certainty factors of the rainfall factor, the terrain factor and the social factor based on a first ratio, a second ratio and a preset certainty factor model formula;
calculating factor sensitivity of the attribute factor based on a preset principal component analysis model and the certainty coefficient.
By adopting the technical scheme, the certainty coefficient model is a probability function and is used for analyzing the sensitivity of each attribute factor influencing the occurrence of the torrential flood disasters, and the same-region quantification of the complex multi-attribute factor data is realized by adopting the certainty coefficient model to determine the sensitivity of the attribute factors.
Optionally, the deterministic coefficient model formula is:
wherein PPa is the first ratio; PPs is the second ratio; CF is a deterministic coefficient.
By adopting the technical scheme, the certainty coefficient model is a probability function and is used for analyzing the sensitivity of each attribute factor influencing the occurrence of the torrential flood disaster, and the method is favorable for laying a cushion for subsequently determining the key factors.
Optionally, the calculating a risk index based on the risk factor weight includes:
calculating the weight of the risk factor based on a subjective and objective combination method;
and adding the weights of the risk factors to obtain a risk index.
By adopting the technical scheme, the risk index is the product of the factor weight and the risk factor index corresponding to the weight, so that the influence on the torrential flood disaster is quantified, and the early warning accuracy of the torrential rain torrential flood disaster in the small watershed is effectively improved.
Optionally, after determining the early warning level corresponding to the risk index within the preset early warning level range, the method includes:
and marking the target area corresponding to the risk early warning in a preset area map.
By adopting the technical scheme, risk early warning is marked on the target area, mountain torrent disaster risks at different positions in the area can be visually seen, and an early warning effect is achieved.
In a second aspect, the application provides a mountain torrent comprehensive risk early warning system which adopts the following technical scheme:
a mountain torrent comprehensive risk early warning system comprises:
the acquisition module is used for acquiring the attribute information of the target small watershed based on a preset torrent risk basic attribute data set;
the classification module is used for classifying the attribute information to obtain an attribute factor;
a construction module for constructing a correlation coefficient matrix for the attribute factors;
the analysis module is used for carrying out principal component analysis on the attribute factors based on the correlation coefficient matrix to obtain a plurality of key factors;
the processing module is used for carrying out normalization processing on the plurality of key factors to obtain risk factor indexes;
a calculation module for calculating a risk index based on the risk factor indicator;
the determining module is used for determining the early warning grade corresponding to the risk index based on a preset risk index range.
By adopting the technical scheme, the attribute factors are subjected to principal component analysis through the analysis module to obtain the key factors, the risk factor indexes are obtained through the processing module based on the key factors, and then the risk indexes are calculated through the calculation module based on the risk factor indexes, so that quantification of early warning of the torrential rainfall torrential flood disaster in the small watershed is realized, and the accuracy of the early warning of the torrential rainfall torrential flood disaster in the small watershed are effectively improved.
In a third aspect, the present application provides a storage medium, which adopts the following technical solutions:
a computer-readable storage medium, in which a computer program is stored, and when the computer program is loaded and executed by a processor, the method for warning of mountain torrents comprehensive risks is adopted.
By adopting the technical scheme, the mountain torrent comprehensive risk early warning method generates a computer program, the computer program is stored in a computer readable storage medium to be loaded and executed by a processor, and the computer program can be conveniently read and stored through the computer readable storage medium.
In summary, the present application has at least one of the following beneficial technical effects:
1. the method comprises the steps of firstly, carrying out principal component analysis on attribute factors to obtain key factors, then obtaining risk factor indexes based on the key factors, and calculating risk indexes based on the risk factor indexes, so that quantification of early warning of the small watershed torrential rainfall torrential flood disasters is realized, and the accuracy of early warning of the small watershed torrential rainfall torrential flood disasters are effectively improved.
2. The torrential rain atlas vector data are historical statistical data, and the torrential rain atlas vector data are combined with the small watershed attribute data to judge the mountain torrent resistance risk capability of the small watershed, namely, the risk resistance capability is increased in the small watershed attribute data, so that the follow-up disaster early warning judgment is more accurate.
3. The risk index is the product of the factor weight and the risk factor index corresponding to the weight, so that the influence on the torrential flood disaster is quantified, and the early warning accuracy of the torrential rain torrential flood disaster in the small watershed is effectively improved.
Drawings
Fig. 1 is an overall flowchart of a method for comprehensive risk early warning of torrential flood according to an embodiment of the present disclosure.
Fig. 2 is a flowchart of calculating factor sensitivity of an attribute factor based on a preset principal component analysis model and a preset certainty coefficient model in the mountain torrent comprehensive risk early warning method according to the embodiment of the present application.
Detailed Description
The embodiment of the application discloses a mountain torrent comprehensive risk early warning method.
Referring to fig. 1, a method for comprehensive risk early warning of torrential flood includes:
s100, acquiring attribute information of the target small watershed based on a preset torrential flood risk basic attribute data set.
The mountain torrent risk basic attribute data set in the embodiment comprises mountain torrent disaster risk area geographic data, hydrological data and historical mountain torrent disaster data. The method comprises the steps of firstly collecting geographical data, hydrological data and historical mountain torrent disaster data of mountain torrent disaster risk areas, and then sorting the geographical data, the hydrological data and the historical mountain torrent disaster data into a small watershed mountain torrent risk basic attribute data set, wherein in the embodiment, the small watershed mountain torrent risk basic attribute data set takes a small watershed smaller than 200 square kilometers as a unit. The attribute information of the target small watershed comprises a plurality of attribute information fields, and in specific implementation, 77 attribute information fields are provided.
S200, classifying the attribute information to obtain attribute factors; the attribute factors include a rainfall factor, a terrain factor, and a social factor.
The attribute factors include rainfall factors, terrain factors and social factors, in this embodiment, 77 attribute information fields of each small watershed are divided into 3 types, that is, rainfall factors, terrain factors and social factors, wherein the rainfall factors account for 59 attribute information fields, the terrain factors account for 15 attribute information fields, and the social factors account for 3 attribute information fields. It should be noted that, compared with the existing classification of attribute information, the embodiment introduces a social factor, and performs comprehensive early warning quantification on mountain torrent risks based on a rainfall factor, a terrain factor, and the social factor.
Specifically, the mountain torrent risk basic attribute data set comprises vector data of a rainstorm map set, attribute data of a small watershed and forecast rainfall data;
before classifying the attribute information, the method comprises the following steps:
s1, combining the rainstorm map set vector data with the small watershed attribute data.
The rainstorm map set vector data are used for expressing the characteristics and rules of the spatial-temporal distribution of the rainstorm statistical characteristics, the rainstorm map set vector data are historical statistical data, the rainstorm map set vector data are combined with the small watershed attribute data, and the rainstorm map set vector data are used for judging the risk capability of the small watersheds for resisting the torrential flood.
The risk resistance is increased in the small watershed attribute data, so that the follow-up disaster early warning judgment is more accurate.
And S2, interpolating the forecast rainfall data into grid data with preset resolution based on an inverse distance weight method.
The main idea of the inverse distance weight method is an explicit assumption of inverse distance weight interpolation, which means that objects that are closer to each other are more similar than objects that are farther from each other.
If the predicted value of the unmeasured predicted position is obtained, the measured value closest to the predicted position has a greater influence on the predicted value than the measured value farther from the predicted position. The inverse distance weighting method assumes that each measurement corresponds to a measurement point having a local effect that decreases with increasing distance. The inverse distance weighting method assigns a larger weight to a point closer to the predicted position and a smaller weight to a point farther from the predicted position.
The inverse distance weight method is one of the spatial interpolation methods, and the spatial interpolation method further includes a kriging interpolation method, a natural neighborhood interpolation method, a spline function interpolation method, a radial basis function and the like.
The spatial resolution of the grid data in this embodiment is 5 km × 5 km. The spatial resolution is preset. The forecast rainfall data is forecast rainfall data of a station in 24 hours in the future.
Referring to fig. 1, S300, a correlation coefficient matrix is constructed for the attribute factors.
In this embodiment, a correlation coefficient matrix is constructed by using a pearson correlation coefficient method. The correlation coefficient matrix is formed by subtracting the correlation coefficients of each column of the matrix.
The degree of correlation between the variables can be determined according to the value of the correlation coefficient.
Specifically, the value range and the correlation degree of the correlation coefficient are shown in the following table:
| value range | Degree of correlation |
| 0.00-0.19 | Very low correlation |
| 0.20-0.39 | Low degree of correlation |
| 0.40-0.69 | Moderate correlation |
| 0.70-0.89 | Is highly correlated |
| 0.90-1.00 | Very high correlation |
Referring to fig. 1, S400, based on the correlation coefficient matrix, principal component analysis is performed on the attribute factors to obtain a plurality of key factors.
Principal component analysis is a statistical method. A group of variables which are possibly correlated are converted into a group of linearly uncorrelated variables through orthogonal transformation, and the group of converted variables are called principal components. And performing principal component analysis on the attribute factors to obtain a plurality of key factors.
In this embodiment, after a correlation coefficient matrix is constructed by using a pearson correlation coefficient method, factors corresponding to correlation coefficients with low correlation and extremely low correlation in the correlation degree are removed, and then principal component analysis is performed on the remaining attribute factors to obtain a plurality of key factors.
Specifically, based on the correlation coefficient matrix, performing principal component analysis on the attribute factors to obtain a plurality of key factors, including:
and S410, calculating the factor sensitivity of the attribute factor based on a preset principal component analysis model and a preset certainty coefficient model.
The deterministic coefficient model is a probability function and is used for analyzing the sensitivity of each attribute factor influencing the occurrence of the torrential flood disasters, and the same-region quantification of the complex multi-attribute factor data is realized by adopting the deterministic coefficient model to determine the sensitivity of the attribute factors. The interval range of the same interval is [ 1 to-1 ].
Specifically, referring to fig. 2, calculating the factor sensitivity of the attribute factor based on a preset principal component analysis model and a preset certainty coefficient model includes:
s411, calculating a first ratio of the number of the disasters causing the torrential flood disasters to the preset target area under each attribute factor.
The number of the disasters of the mountain torrents can be acquired through a database and can also be acquired through manual input.
The target region area is a region area other than a hill region. In specific implementation, the first ratio of the number of disasters causing the mountain torrent disasters to the preset target area is calculated for each attribute factor in the preset time range.
S412, calculating a second ratio of the number of the disasters under each attribute factor to the preset area of the hill area.
In specific implementation, the second ratio of the number of disasters under each attribute factor in the preset time range to the preset area of the hill region is calculated.
And S413, respectively calculating the certainty factors of the rainfall factor, the terrain factor and the social factor based on the first ratio, the second ratio and a preset certainty factor model formula.
And S414, calculating the factor sensitivity of the attribute factors based on the preset principal component analysis model and the certainty coefficient.
Specifically, the deterministic coefficient model formula is:
wherein PPa is a first ratio; PPs is a second ratio; CF is a deterministic coefficient.
Taking the topographic factors in the attribute factors as an example, if the number of flood disasters is 5, the target area is 20 square kilometers, and the area of a hill area is 50 square kilometers in a time range of 5 years in the topographic factors, the first ratio PPa is 0.25, the second ratio PPs is 0.1, where PPa is greater than PPs, and the certainty coefficient CF is (0.25-0.1)/0.25 (1-0.1) =0.67.
And S420, determining a key factor according to the factor sensitivity.
And obtaining a deterministic coefficient calculation result according to a deterministic coefficient model formula, and performing principal component analysis according to the deterministic coefficient calculation result and the correlation coefficient matrix.
In specific implementation, the certainty coefficient obtained through the certainty coefficient model is the weight of the attribute factor, and if the certainty coefficient of a certain attribute factor is larger than a preset coefficient threshold value, the attribute factor is determined as a sensitive factor of the torrential flood disaster, namely a key factor. For example, if the coefficient threshold is set to 0.06 and the certainty coefficient of the target factor is 0.08, the target factor is determined to be a sensitive factor due to being greater than the coefficient threshold.
The factor sensitivity is illustrated based on a rainfall factor, wherein the rainfall factor comprises the average rainfall in 6 hours, and if the average rainfall in 6 hours is less than or equal to 25mm, the calculation result of the certainty factor is-0.83; between 25mm and 50mm, the calculation result is-0.59; between 50mm and 100mm, the calculation result is-0.2; when the thickness is between 100mm and 150mm, the calculation result is-0.04; at greater than 150mm, the calculation is 0.24. Therefore, the larger the average rainfall in 6 hours is, the higher the risk of mountain torrent disasters is, and the average rainfall in 6 hours is a key factor.
In this embodiment, the key factors include an average rainfall in 6 hours, a rainfall variation coefficient in 6 hours, future rainfall, a peak module, a river length, a river ratio drop, family property, house and population, wherein the family property, the house and the population are social factors, the average rainfall in 6 hours, the rainfall variation coefficient in 6 hours, the future rainfall and the peak module are rainfall factors, and the river length and the river ratio drop are topographic factors.
Referring to fig. 1, S500, normalization processing is performed on a plurality of key factors to obtain risk factor weights.
The normalization process uniformly maps the data of the key factors into a range of 0 to 1. And laying a cushion for the subsequent calculation of the risk index.
And S600, calculating a risk index based on the weight of the risk factor.
The risk index is the quantification of mountain torrent disaster early warning.
Specifically, calculating a risk index based on the risk factor weight includes:
and S610, calculating the weight of the risk factor based on the subjective and objective combination method.
The subjective and objective combination method is a combined weighting method of subjective and objective combination, the subjective and objective combination method usually adopts multiplication or linear synthesis method, and the current method for determining the index attribute weight can be divided into: subjective weighting, objective weighting and combination weighting 3 categories. The result of the subjective weighting method has higher subjectivity, the objective weighting method has mathematical theoretical basis but does not consider the intention of a decision maker, and the subjective and objective combination method is used for making up the defect of single weighting. In this embodiment, the subjective and objective combination method is a method combining subjective weighting and entropy weighting.
And S620, adding the weights of the risk factors to obtain a risk index.
And based on the correlation coefficient matrix, the deterministic coefficients are a plurality of, so that the risk factor weights are a plurality of, and the risk indexes can be obtained by adding the risk factor weights of each risk factor.
Referring to fig. 1, S700, an early warning level corresponding to the risk index is determined based on a preset risk index range.
In specific implementation, the early warning level and the risk index range are shown in the following table:
| early warning level | Graphical color representation | Range of risk indices |
| May happen | Blue color | 0.45-0.6 |
| The possibility is high | Yellow colour | 0.6-0.75 |
| High possibility of | Orange colour | 0.75-0.9 |
| The possibility is very high | Red colour | 0.9-1 |
For example, if the risk index is 0.7, it is determined that the early warning level corresponding to the risk index is more likely.
Specifically, after determining the early warning level corresponding to the risk index within a preset early warning level range, the method includes:
and S710, marking a target area corresponding to the risk early warning in a preset area map.
The regional map is preset, each early warning grade corresponds to one color, and the colors of different early warning grades are different.
The implementation principle of the mountain torrent comprehensive risk early warning method in the embodiment of the application is as follows: the method comprises the steps of firstly, carrying out principal component analysis on attribute factors to obtain key factors, then obtaining risk factor indexes based on the key factors, and calculating risk indexes based on the risk factor indexes, so that quantification of early warning of the torrential rainfall torrential flood disaster in the small watershed is realized, and the accuracy of early warning of the torrential rainfall torrential flood disaster in the small watershed are effectively improved.
The embodiment of the application also discloses a mountain torrent comprehensive risk early warning system.
A mountain torrent comprehensive risk early warning system comprises:
the acquisition module is used for acquiring the attribute information of the target small watershed based on a preset mountain torrent risk basic attribute data set; the classification module is used for classifying the attribute information to obtain an attribute factor;
the building module is used for building a correlation coefficient matrix for the attribute factors;
the analysis module is used for carrying out principal component analysis on the attribute factors based on the correlation coefficient matrix to obtain a plurality of key factors;
the processing module is used for carrying out normalization processing on the plurality of key factors to obtain risk factor indexes;
the calculation module is used for calculating a risk index based on the risk factor index;
and the determining module is used for determining the early warning grade corresponding to the risk index based on the preset risk index range.
The implementation principle of the mountain torrent comprehensive risk early warning system in the embodiment of the application is as follows: the method comprises the steps of firstly, carrying out principal component analysis on attribute factors through an analysis module to obtain key factors, then obtaining risk factor indexes through a processing module based on the key factors, and then calculating risk indexes through a calculation module based on the risk factor indexes, so that quantification of early warning of the torrential rain torrential flood disasters in the small watershed is realized, and the accuracy of the early warning of the torrential rain torrential flood disasters in the small watershed are effectively improved.
The embodiment of the application also discloses a storage medium.
A computer readable storage medium stores a computer program, and when the computer program is loaded and executed by a processor, the mountain torrent comprehensive risk early warning method is adopted.
The computer program may be stored in a computer readable medium, the computer program includes computer program code, the computer program code may be in a source code form, an object code form, an executable file or some intermediate form, and the like, the computer readable medium includes any entity or device capable of carrying the computer program code, a recording medium, a usb disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read Only Memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, a software distribution medium, and the like, and the computer readable medium includes but is not limited to the above components.
The method for warning of comprehensive risk of torrential floods in the embodiments is stored in a computer-readable storage medium through the computer-readable storage medium, and is loaded and executed on a processor, so as to facilitate storage and application of the method.
The above embodiments are preferred embodiments of the present application, and the protection scope of the present application is not limited by the above embodiments, so: equivalent changes in structure, shape and principle of the present application shall be covered by the protection scope of the present application.
Claims (9)
1. A mountain torrent comprehensive risk early warning method is characterized by comprising the following steps:
acquiring attribute information of a target small watershed based on a preset torrential flood risk basic attribute data set;
classifying the attribute information to obtain attribute factors; the attribute factors comprise rainfall factors, terrain factors and social factors;
constructing a correlation coefficient matrix for the attribute factors;
performing principal component analysis on the attribute factors based on the correlation coefficient matrix to obtain a plurality of key factors;
normalizing the plurality of key factors to obtain the weight of the risk factor;
calculating a risk index based on the risk factor weight;
and determining the early warning grade corresponding to the risk index based on a preset risk index range.
2. The method for integrated risk early warning of torrential floods according to claim 1, wherein the torrential flood risk basic attribute data set comprises torrential rain atlas vector data, small watershed attribute data and forecast rainfall data;
before the classifying the attribute information, the method includes:
combining the stormwater atlas vector data with the small watershed attribute data;
and interpolating the forecast rainfall data into grid data with preset resolution based on an inverse distance weight method.
3. The method according to claim 1, wherein the performing principal component analysis on the attribute factors based on the correlation coefficient matrix to obtain a plurality of key factors comprises:
calculating the factor sensitivity of the attribute factors based on a preset principal component analysis model and a preset certainty coefficient model;
determining key factors according to the factor sensitivity.
4. The method for comprehensive risk early warning of torrential floods according to claim 3, wherein the calculating the factor sensitivity of the attribute factor based on a preset principal component analysis model and a preset certainty coefficient model comprises:
calculating a first ratio of the number of disasters causing the mountain torrent disasters to the preset area of the target area under each attribute factor;
calculating a second ratio of the number of the disasters under each attribute factor to a preset area of a hill area;
respectively calculating the certainty factors of the rainfall factor, the terrain factor and the social factor based on a first ratio, a second ratio and a preset certainty factor model formula;
and calculating the factor sensitivity of the attribute factor based on a preset principal component analysis model and the certainty coefficient.
5. The mountain torrent comprehensive risk early warning method according to claim 4, wherein the method comprises the following steps: the deterministic coefficient model formula is:
wherein PPa is the first ratio; PPs is the second ratio; CF is a deterministic coefficient.
6. The method for mountain torrent comprehensive risk early warning according to claim 1, wherein the calculating a risk index based on the risk factor weight comprises:
calculating the weight of the risk factor based on an objective and subjective combination method;
and adding the weights of the risk factors to obtain a risk index.
7. The method as claimed in claim 1, wherein after determining the warning level corresponding to the risk index within the preset warning level range, the method comprises:
and marking a target area corresponding to the risk early warning in a preset area map.
8. The utility model provides a risk early warning system is synthesized to torrential flood which characterized in that includes:
the acquisition module is used for acquiring the attribute information of the target small watershed based on a preset torrent risk basic attribute data set;
the classification module is used for classifying the attribute information to obtain an attribute factor;
a construction module for constructing a correlation coefficient matrix for the attribute factors;
the analysis module is used for carrying out principal component analysis on the attribute factors based on the correlation coefficient matrix to obtain a plurality of key factors;
the processing module is used for carrying out normalization processing on the plurality of key factors to obtain risk factor indexes;
a calculation module for calculating a risk index based on the risk factor indicator;
the determining module is used for determining the early warning grade corresponding to the risk index based on a preset risk index range.
9. A computer-readable storage medium, in which a computer program is stored, which, when loaded and executed by a processor, carries out the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211071399.3A CN115330254A (en) | 2022-09-02 | 2022-09-02 | Mountain torrent comprehensive risk early warning method, system and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115796599A (en) * | 2022-12-27 | 2023-03-14 | 中国水利水电科学研究院 | Method and system for analyzing risk degree of torrential flood channel based on comprehensive characteristics of micro drainage basin |
| CN115798179A (en) * | 2023-01-09 | 2023-03-14 | 深圳联和智慧科技有限公司 | Flood disaster and flood prevention monitoring and early warning method and system based on unmanned aerial vehicle |
| CN116682237A (en) * | 2023-08-03 | 2023-09-01 | 南通午未连海科技有限公司 | Intelligent flood prevention early warning method and platform based on artificial intelligence |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115796599A (en) * | 2022-12-27 | 2023-03-14 | 中国水利水电科学研究院 | Method and system for analyzing risk degree of torrential flood channel based on comprehensive characteristics of micro drainage basin |
| CN115796599B (en) * | 2022-12-27 | 2023-09-26 | 中国水利水电科学研究院 | Mountain torrent canal risk degree analysis method and system based on micro-river basin comprehensive characteristics |
| CN115798179A (en) * | 2023-01-09 | 2023-03-14 | 深圳联和智慧科技有限公司 | Flood disaster and flood prevention monitoring and early warning method and system based on unmanned aerial vehicle |
| CN116682237A (en) * | 2023-08-03 | 2023-09-01 | 南通午未连海科技有限公司 | Intelligent flood prevention early warning method and platform based on artificial intelligence |
| CN116682237B (en) * | 2023-08-03 | 2023-10-20 | 南通午未连海科技有限公司 | Intelligent flood prevention early warning method and platform based on artificial intelligence |
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