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

被引:40
|
作者
Cao, Ying [1 ]
Yin, Kunlong [1 ]
Zhou, Chao [2 ]
Ahmed, Bayes [3 ]
机构
[1] China Univ Geosci, Fac Engn, Wuhan 430074, Peoples R China
[2] China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430078, Peoples R China
[3] UCL, Inst Risk & Disaster Reduct, Gower St, London WC1E 6BT, England
基金
中国国家自然科学基金;
关键词
landslides monitoring; groundwater level prediction; Support Vector Machine; influencing factors; Three Gorges Reservoir area; EXTREME LEARNING-MACHINE; 3 GORGES RESERVOIR; DISPLACEMENT PREDICTION; NEURAL-NETWORKS; SLOPE; AREA; OPTIMIZATION; PARAMETERS; STABILITY; ALGORITHM;
D O I
10.3390/s20030845
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA.
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页数:20
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