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[1]HUANG Wen-jen,HSU Chang-li.Displacement Predicted for the Backpropagation Neural Network Applying in Shonzongliao Landslide Area[J].Research of Soil and Water Conservation,2009,16(06):271-275.

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Displacement Predicted for the Backpropagation Neural Network Applying in Shonzongliao Landslide Area

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 16 Number of periods: 2009 06 Page number: 271-275 Column: Public date: 2009-12-20

Title:: Displacement Predicted for the Backpropagation Neural Network Applying in Shonzongliao Landslide Area

Author(s):: HUANG Wen-jen¹, HSU Chang-li²; 1. Graduate Institute of Disaster Prevention on Hillslopes and Water Resources Engineering, National Pingtung University of Science and Technology, Pingtung, Taiwan 91201, China;
2. Department of Soil and Water Conservation, Pingtung University of Science and Technology, Pingtung, Taiwan 91201, China

Keywords:: landslide; backpropagation neural network; Levenberg-Marquardt algorithm; monitoring management value

CLC:: P642.22

DOI:: -

Abstract:: Landslides has become one disaster type of the most serious destroy on the steep lands.The way to monitoring and assessment for landslide area can help government agencies to select suitable management and plan mitigation in unstable landslide areas. This research presents a case study of landslide monitoring and assessment at Shonzongliao Landslide area, Township, Taitung County, attempt to predict slope movements using backpropagation neural network (BPNN), as well as use powerful tools to model and investigate various complex and non-linear phenomena. The BPNN can performed calculation to use MATLAB program with the Levenberg-Marquardt algorithm. The data from the case study are used to train and test the developed model, to enable prediction of the magnitude ground movements with the seven input helpful of variables that have regard to direct physical significance. According to monitoring date picked 12 set event of typhoon or rainstorm. 3 situation of train versus simulation and test is introduced in the network architecture apart from 6, 8, 10 set-batch. The developed BPNN optimal model by 8 set-batch demonstrates that have good potential accurately for predicting error 13.1% to the slope movement. This promising result can offer the reference of building of monitoring and utilizing management value at the landslide areas.

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