CN116384627A - Geological disaster evaluation method based on machine learning - Google Patents
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Abstract
The invention discloses a geological disaster evaluation method based on machine learning, which comprises the following steps: determining a target area, and acquiring a sample set based on the target area; constructing a geological disaster susceptibility evaluation index system, and obtaining a first data set based on the sample set and the geological disaster susceptibility evaluation index system; constructing a geological disaster vulnerability evaluation model, inputting a training set into the geological disaster vulnerability evaluation model for training to obtain a trained geological disaster vulnerability evaluation model, and inputting a test set into the trained geological disaster vulnerability evaluation model to obtain a vulnerability evaluation result; constructing a geological disaster vulnerability evaluation index system, obtaining a second data set based on the sample set and the geological disaster vulnerability evaluation index system, and obtaining a vulnerability evaluation result based on the second data; and obtaining a final geological disaster evaluation result based on the susceptibility evaluation result and the vulnerability evaluation result. And an evaluation result error caused by manual evaluation is avoided through an intelligent algorithm.
Description
Technical Field
The invention relates to the technical field of geological disasters, in particular to a geological disaster evaluation method based on machine learning.
Background
Geological disasters are geological effects or geological phenomena that are formed under the action of natural or human factors and damage to human lives and properties. The geological disaster in China is very serious, and especially in the middle and western regions of China, the control of the geological disaster has become a major matter of economic construction of related countries and life and property safety of people and major project construction success and failure of related countries. In recent decades, geological disasters have seriously reduced environmental quality and endanger human safety and ecological development of the whole biosphere, and prevention and control of the geological disasters are attracting more and more attention.
The geological disaster types include collapse, landslide, debris flow, ground subsidence (including karst subsidence and mining subsidence), ground fissures, ground subsidence, and the like. Along with the wider application range of remote sensing, the thought of new-age geological disaster evaluation is generally based on multi-source satellite remote sensing data or remote sensing products and GIS (geographic information system) combined with a mathematical model or intelligent algorithm to predict geological disasters, so that loss of lives and properties of people is reduced. However, due to the difference of the construction of the evaluation index system or the prediction model, the prediction results are different, and the geological disaster evaluation method still needs to be further optimized and improved.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a geological disaster evaluation method based on machine learning.
In order to achieve the technical purpose, the invention provides the following technical scheme: a machine learning-based geological disaster evaluation method, comprising:
determining a target area, and acquiring a sample set based on the target area;
constructing a geological disaster susceptibility evaluation index system, and obtaining a first data set based on the sample set and the geological disaster susceptibility evaluation index system, wherein the first data set comprises a training set and a testing set;
constructing a geological disaster vulnerability evaluation model, inputting the training set into the geological disaster vulnerability evaluation model for training to obtain a trained geological disaster vulnerability evaluation model, and inputting the test set into the trained geological disaster vulnerability evaluation model to obtain a vulnerability evaluation result;
constructing a geological disaster vulnerability evaluation index system, obtaining a second data set based on the sample set and the geological disaster vulnerability evaluation index system, and obtaining a vulnerability evaluation result based on the second data;
and obtaining a final geological disaster evaluation result based on the vulnerability evaluation result and the vulnerability evaluation result.
Optionally, the process of obtaining the sample set includes:
acquiring an original remote sensing image of the target area;
processing the original remote sensing image based on ArcGIS to obtain a plurality of grid cells;
selecting a historical disaster point of the grid unit and a random point outside a buffer area of the historical disaster point as a sample of the grid unit;
and obtaining a sample set based on the samples of the grid cells.
Optionally, the geological disaster susceptibility evaluation index system comprises the following indexes: slope, slope direction, slope height, river density, elevation, geotechnical type, distance from fault, distance from river, vegetation coverage degree and rainfall.
Optionally, forward normalization is performed on the geological disaster susceptibility evaluation index to obtain a first data set.
Optionally, inputting the training set into the geological disaster susceptibility evaluation model, and optimizing the support vector machine model based on whale algorithm to train to obtain susceptibility evaluation results.
Optionally, the whale algorithm optimization support vector machine model optimization step includes:
initializing relevant parameters of a whale algorithm, wherein the relevant parameters comprise: population scale, maximum iteration number, penalty parameter and kernel parameter;
calculating the fitness value of each whale individual based on a K-fold cross validation method, obtaining the first fitness of the whale individual, and recording the current individual and the optimal value of the population;
updating the whale individual position based on a whale algorithm to obtain an updated whale individual position;
calculating a second fitness of the whale individuals based on the updated whale individual positions, and obtaining a new population based on the first fitness and the second fitness;
iterating based on the new population until the termination condition is met, so as to obtain optimal parameters;
and obtaining a trained geological disaster susceptibility evaluation model based on the optimal parameters.
Optionally, the geological disaster vulnerability evaluation index system comprises the following indexes: population density, traffic road density, and economic loss.
The invention has the following technical effects:
the method and the system can rapidly and accurately summarize the development characteristics of landslide based on the prediction model to further forecast the occurrence probability of landslide disaster, rapidly and accurately establish an emergency scheme in a region where the geological disaster is dangerous, and provide scientific basis for disaster prevention and reduction work.
The invention carries out accurate and reliable geological disaster evaluation based on the multisource remote sensing data and the intelligent algorithm, and can realize automatic selection of the weight factors through the intelligent algorithm, thereby avoiding evaluation result errors caused by manual evaluation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, the embodiment discloses a geological disaster evaluation method based on machine learning, which includes:
determining a target area, and acquiring a sample set based on the target area;
constructing a geological disaster susceptibility evaluation index system, and obtaining a first data set based on the sample set and the geological disaster susceptibility evaluation index system, wherein the first data set comprises a training set and a testing set;
constructing a geological disaster vulnerability evaluation model, inputting the training set into the geological disaster vulnerability evaluation model for training to obtain a trained geological disaster vulnerability evaluation model, and inputting the test set into the trained geological disaster vulnerability evaluation model to obtain a vulnerability evaluation result;
constructing a geological disaster vulnerability evaluation index system, obtaining a second data set based on the sample set and the geological disaster vulnerability evaluation index system, and obtaining a vulnerability evaluation result based on the second data;
and obtaining a final geological disaster evaluation result based on the vulnerability evaluation result and the vulnerability evaluation result.
In this embodiment, the Guizhou province is taken as an example, and geological disaster evaluation is performed.
Guizhou is located in the west of the Yangzi block, in the West Lutetius mountain making belt, in the east Asia midwife mountain making belt and in the Alps-Tutius new generation in the ascending crust deformation zone. The ground structure is subjected to 5 stages of wuling, snow peak, jia Lidong, hua Lixi-Ying and Yan Shan Himalaya, and the ground stress fields of several mountain making motions form three main structural forms of extrusion type, straight twisting type and knob type under the boundary condition of various ends. And Guizhou belongs to tropical warm climates, and the average rainfall over many years is 1100-1300 mm.
Geological disaster 1613 co-occurs in 2011-2020 in Guizhou area, resulting in 456 casualties and missing, and direct economic loss of 17.71 hundred million yuan. From the disaster type, the system is mainly landslide, accounting for 81.71% of the total disaster; secondly, collapse, accounting for 11.66% of the total number of disasters.
Firstly, acquiring an original remote sensing image of a target area;
processing the original remote sensing image based on ArcGIS to obtain 9 grid units, including: qian nan, qian southeast, qian southwest, guiyang, liujishui, zunyi, anshui, pichia and copper kernel;
selecting a historical disaster point of the grid unit and a random point outside a buffer area of the historical disaster point as a sample of the grid unit; based on the samples of the 9 grid cells, a sample set is obtained.
Constructing a geological disaster susceptibility evaluation index system, wherein the index system comprises the following indexes: slope, slope direction, slope height, river density, elevation, geotechnical type, distance from fault, distance from river, vegetation coverage degree and rainfall.
And obtaining corresponding index data based on the geographic remote sensing ecological network to obtain a first data set.
Based on the first data set, carrying out dimensionless treatment on the 10 selected indexes, wherein the method specifically comprises the following steps: and selecting 10 indexes for grading, determining a grading threshold value and carrying out assignment to obtain sample quantized data.
And constructing a support vector machine model based on whale algorithm optimization. The choice of SVM model parameters directly affects the accuracy of the evaluation results. According to the embodiment, the searching capability of the whale algorithm is utilized to search the penalty parameter and the kernel function of the SVM, so that the prediction accuracy of the SVM is improved.
The support vector machine (support vector machines, SVM) is a two-class model whose basic model is a linear classifier defined at maximum separation in feature space, the maximum separation distinguishing it from the perceptron; the SVM may perform a nonlinear classification by introducing a kernel function.
The choice of parameters for the SVM directly affects its training and generalization performance, with the penalty factor C and kernel g parameters being 2 parameters that must be adjusted. And optimizing SVM parameters by adopting a WOA algorithm, and obtaining an optimal SVM prediction model by iterative optimization.
The whale algorithm optimization support vector machine model specifically comprises the following steps:
(1) data preprocessing, setting whale number, maximum iteration number and parameter optimizing range of SVM
(2) Randomly generating an initial whale position, calculating a fitness value and searching an optimal position;
(3) updating whale positions by adopting a WOA algorithm;
(4) calculating the fitness value again, and comparing the fitness value to update the optimal position of whale;
(5) judging whether a termination condition is met, entering the next step if the termination condition is met, otherwise entering the step (3);
(6) obtaining optimal model parameters;
(7) retraining the training set using the optimal parameters;
(8) and predicting the test set by using the trained model.
Evaluating based on the predicted result, classifying the evaluated result in a susceptibility way, including: high risk, medium risk and low risk.
The evaluation of the vulnerability of the geological disaster is to evaluate the severity of the disaster-bearing body possibly damaged by the geological disaster, and mainly evaluate the value of the disaster-bearing body. According to the geological disaster risk investigation and evaluation technical requirement (1:50000) requirement (trial) of 3 months in 2020, the vulnerability of general investigation areas should be evaluated by personnel and infrastructure vulnerability respectively, weight is set to comprehensively determine the vulnerability of disaster-bearing bodies, population density, traffic road density and economic loss are selected as geological disaster vulnerability evaluation indexes of Guizhou areas, and the corresponding indexes are classified and assigned to obtain the regional geological disaster vulnerability evaluation results. Wherein grading the vulnerability assessment results comprises: high risk, medium risk and low risk.
And integrating the regional geological disaster susceptibility evaluation result and the vulnerability evaluation result to obtain the final Guizhou regional geological disaster evaluation result. The method comprises the following specific steps:
and (3) based on the ArcGIS platform, reclassifying and assigning the calculated geographical disaster vulnerability partition map of the Guizhou area, and utilizing the calculation function of a grid calculator in a map algebra tool box in a Spatial Analyst Tools tool box. In order to facilitate partitioning, the embodiment sequentially assigns the geological disaster vulnerability and the vulnerability grade of the Guizhou area from high to low, multiplies the grading assignment of the vulnerability and vulnerability partition map to obtain a risk grading matrix, and performs risk grading based on the risk grading matrix to divide the risk grading matrix into 3 grades, namely a high risk area, a medium risk area and a low risk area.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. A machine learning-based geological disaster evaluation method, comprising:
determining a target area, and acquiring a sample set based on the target area;
constructing a geological disaster susceptibility evaluation index system, and obtaining a first data set based on the sample set and the geological disaster susceptibility evaluation index system, wherein the first data set comprises a training set and a testing set;
constructing a geological disaster vulnerability evaluation model, inputting the training set into the geological disaster vulnerability evaluation model for training to obtain a trained geological disaster vulnerability evaluation model, and inputting the test set into the trained geological disaster vulnerability evaluation model to obtain a vulnerability evaluation result;
constructing a geological disaster vulnerability evaluation index system, obtaining a second data set based on the sample set and the geological disaster vulnerability evaluation index system, and obtaining a vulnerability evaluation result based on the second data;
and obtaining a final geological disaster evaluation result based on the vulnerability evaluation result and the vulnerability evaluation result.
2. The machine learning based geological disaster evaluation method according to claim 1, wherein: the process of obtaining the sample set includes:
acquiring an original remote sensing image of the target area;
processing the original remote sensing image based on ArcGIS to obtain a plurality of grid cells;
selecting a historical disaster point of the grid unit and a random point outside a buffer area of the historical disaster point as a sample of the grid unit;
and obtaining a sample set based on the samples of the grid cells.
3. The machine learning based geological disaster evaluation method according to claim 1, wherein: the geological disaster susceptibility evaluation index system comprises the following indexes: slope, slope direction, slope height, river density, elevation, geotechnical type, distance from fault, distance from river, vegetation coverage degree and rainfall.
4. A machine learning based geological disaster assessment method according to claim 3, wherein: and carrying out forward normalization on the geological disaster susceptibility evaluation index to obtain a first data set.
5. The machine learning based geological disaster evaluation method according to claim 1, wherein: inputting the training set into the geological disaster susceptibility evaluation model, and training based on a whale algorithm optimization support vector machine model to obtain susceptibility evaluation results.
6. The machine learning based geological disaster evaluation method according to claim 5, wherein: the whale algorithm optimizing support vector machine model comprises the following steps:
initializing relevant parameters of a whale algorithm, wherein the relevant parameters comprise: population scale, maximum iteration number, penalty parameter and kernel parameter;
calculating the fitness value of each whale individual based on a K-fold cross validation method, obtaining the first fitness of the whale individual, and recording the current individual and the optimal value of the population;
updating the whale individual position based on a whale algorithm to obtain an updated whale individual position;
calculating a second fitness of the whale individuals based on the updated whale individual positions, and obtaining a new population based on the first fitness and the second fitness;
iterating based on the new population until the termination condition is met, so as to obtain optimal parameters;
and obtaining a trained geological disaster susceptibility evaluation model based on the optimal parameters.
7. The machine learning based geological disaster evaluation method according to claim 1, wherein: the geological disaster vulnerability evaluation index system comprises the following indexes: population density, traffic road density, and economic loss.
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