Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis

Cao, Ying; Yin, Kunlong; Zhou, Chao; Ahmed, Bayes

doi:10.3390/s20030845

Cited by 43 publications

(21 citation statements)

References 40 publications

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“…Such as the Lorenzo-1 and Rules Viaduct landslides of the Rules Reservoir in Southern Spain [15], the InSAR data shows that the three acceleration periods of both landslides are related to drawdown periods of the water level. Many similar cases are also studied in the TGRA [17,41]. When the reservoir level drops rapidly, the pore-water pressure within the landslide begins to dissipate, and the dissipation speed lags greatly behind the reservoir drawdown speed.…”

Section: The Relationship Between Landslide and Influencing Factorsmentioning

confidence: 99%

Landslide Characterization Applying Sentinel-1 Images and InSAR Technique: The Muyubao Landslide in the Three Gorges Reservoir Area, China

et al. 2020

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Landslides are a common natural hazard that causes casualties and unprecedented economic losses every year, especially in vulnerable developing countries. Considering the high cost of in-situ monitoring equipment and the sparse coverage of monitoring points, the Sentinel-1 images and Interferometric Synthetic Aperture Radar (InSAR) technique were used to conduct landslide monitoring and analysis. The Muyubao landslide in the Three Gorges Reservoir area in China was taken as a case study. A total of 37 images from March 2016 to September 2017 were collected, and the displacement time series were extracted using the Stanford Method for Persistent Scatterer (StaMPS) small baselines subset method. The comparison to global positioning system monitoring results indicated that the InSAR processing of the Muyubao landslide was accurate and reliable. Combined with the field investigation, the deformation evolution and its response to triggering factors were analyzed. During this monitoring period, the creeping process of the Muyubao landslide showed obvious spatiotemporal deformation differences. The changes in the reservoir water level were the trigger of the Muyubao landslide, and its deformation mainly occurred during the fluctuation period and high-water level period of the reservoir.

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Section: The Relationship Between Landslide and Influencing Factorsmentioning

confidence: 99%

Landslide Characterization Applying Sentinel-1 Images and InSAR Technique: The Muyubao Landslide in the Three Gorges Reservoir Area, China

et al. 2020

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“…The nodes in the input layer of the model consist of rainfall during the period to be predicted (1-1), and the groundwater ( t − 1) composition for the first 5 months to be predicted, and the output layer node of the groundwater in a certain county during the period to be predicted. The number of nodes in the hidden layer of the neural network has a great impact on the neural network, if the number of nodes is small, the network performance may be extremely poor, if the number of nodes is too much, the training is easy to fall into the local minimum [ 15 ]. In order to avoid the blindness of selection, on the basis of BP neural network, write the loop code for the number of hidden layer nodes.…”

Section: Results and Analysismentioning

confidence: 99%

Genetic Algorithm in Multimedia Dynamic Prediction of Groundwater in Open-Pit Mine

Zhang

Chen

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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This study is aiming at the nonlinear mapping relationship between the groundwater level and its influencing factors. Through the design and calculation process of matlab7 platform, taking the monitoring wells distributed in an open-pit mining area as an example, the short-term prediction of groundwater dynamics in the study area is carried out by using BP neural network model and BP neural network model based on genetic algorithm. Root mean squared error (RMSE), Mean absolute percent-age error (MAPE) and Nash–Sutcliffe efficiency (NSE) are used coefficients,, and the results were compared with BP neural network and stepwise regression model. From the results of the comparative analysis, the genetic algorithm optimized the BP neural network model in the training phase and the test phase, the RMSE was 0.25 and 0.36, the MAPE was 6.7 and 8.13%, and the NSE was 0.87 and 0.72, respectively. The BP neural network model optimized by genetic algorithm is obviously superior to the BP neural network model, which is an ideal prediction model for short-term groundwater level. This model can provide a prediction method for groundwater dynamic prediction and has a good application prospect.

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“…Compared to traditional algorithms, GA can handle large search spaces efficiently and is less prone to converging on a locally optimal solution. Recently, GA has been progressively developed in conjunction with other techniques and has been applied to many optimization problems [37][38][39].…”

Section: Ga-svm Modelmentioning

confidence: 99%

Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model

Zhou

Cui

et al. 2021

IJGI

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Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA–SVM model as a tool for mapping mineral prospectivity.

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Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis

Cited by 43 publications

References 40 publications

Landslide Characterization Applying Sentinel-1 Images and InSAR Technique: The Muyubao Landslide in the Three Gorges Reservoir Area, China

Landslide Characterization Applying Sentinel-1 Images and InSAR Technique: The Muyubao Landslide in the Three Gorges Reservoir Area, China

Genetic Algorithm in Multimedia Dynamic Prediction of Groundwater in Open-Pit Mine

Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model

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