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

Ying Cao,Bayes Ahmed,Chao Zhou,Kunlong Yin

doi:10.3390/s20030845

Abstract

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.

Highlights

The groundwater, with storage and migration behaviors, is one of the main natural factors affecting landslide stability
The monitoring and prediction of the groundwater level are significant for landslide early warning
Comparing the prediction results of multi-factor Genetic Algorithm-Support Vector Machine (GA-Support Vector Machine (SVM)) and single-factor Genetic Algorithms (GA)-SVM; it can be found that multi-factor GA-SVM has higher accuracy at both locations

Summary

Introduction

The groundwater, with storage and migration behaviors, is one of the main natural factors affecting landslide stability. It impacts the various stages of landslide development [1,2]. The groundwater level in a landslide is mainly affected by external input factors (rainfall, reservoir level, irrigation, etc.) and the permeability of the sliding body (looseness of the soil, development of earth cracks, etc.). For landslides with loose soil and well-developed cracks, the groundwater level clearly rises after rainfall infiltration, which becomes an unfavorable factor for landslide stability [5]. Is learned by example, which consists of transmission and signal forward transmission. BPNN is learned by example, which consists of some some input.

Results

Discussion

Conclusion