Jiangxi province small watershed mountain torrent susceptibility mapping method based on lifting algorithm
A susceptibility, small watershed technology, applied in knowledge-based models of computer systems, neural learning methods, computing, etc., to achieve the effect of accurate torrent susceptibility
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Embodiment 1
[0045] Such as figure 1 As shown, the present invention provides a technical solution, a method for mapping the susceptibility of mountain torrents in small watersheds in Jiangxi Province based on a lifting algorithm, including the following main steps:
[0046] S1. Data preprocessing: sample the data, and randomly divide the sample data into training set, verification set and test set as model input;
[0047] S2. Establishment of the lifting method model: using the above data, three lifting algorithms are used to evaluate the susceptibility of mountain torrent disasters;
[0048] S3. Hyperparameter optimization: use the Bayesian optimization algorithm and verification set data to optimize the hyperparameters of the model to obtain the best parameters for the algorithm to run;
[0049] S4. Run the test set data to verify the accuracy of the algorithm;
[0050] S5. Mapping the susceptibility of mountain torrents in small watersheds: import the results of the susceptibility of...
Embodiment 2
[0074] Taking Jiangxi Province as the research area, this method is used to evaluate the susceptibility of flash floods in each small watershed, and the susceptibility mapping is carried out;
[0075] Such as figure 2 As shown, the specific steps of the technical route of the method are as follows:
[0076] The first step is to preprocess the small watershed dataset:
[0077] Select the average slope, shape coefficient, gradient of the longest confluence path, centroid elevation, terrain humidity index, normalized difference vegetation index, distance from the river, and rainfall with a frequency of 80% within 10 minutes in the data set parameter library. The 10 characteristic factors of flood peak modulus and confluence time are used as the independent variables of integrated regression, and the number of flash floods in history is selected as the regression dependent variable;
[0078] On the basis of data cleaning, standardization and downsampling, 60% is randomly divide...
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