應用倒傳遞類神經網路模擬山棕寮地滑地滑移特性

Applying Back Propagation Neural Network on Displacement Characteristic Assessment in Shanzongliao Landslide Area

林廣榮

指導教授 ： 許中立

國立屏東科技大學/農學院/森林系所/碩士(2009年)

https://doi.org/10.6346/NPUST.2009.00297

摘要

地滑為陡坡地最具破壞之災害型態之一，藉由實施地滑地監測與評估，可以協助政府擬訂適當的管理對策。本研究以台東縣池上鄉山棕寮地滑地為例，利用倒傳遞類神經網路(BPNN)具有建構高複雜且非線性關係模式預測坡面位移變化。本研究所發展之倒傳遞類神經網路分析係應用MATLAB程式之Levenberg-Marquardt演算法求解。網路輸入係以直接關係位移物理因子為變數，建構最佳之四層網路，其中一層輸入層採用前期降雨量、入滲係數、降雨強度、地形坡度、地下水位、土壤凝聚力及土壤內摩擦角等7個因子為輸入變數，另有二層隱藏層及一層輸出層。經所發展建構之倒傳遞類神經網路分析結果顯示，其對於地滑地坡面位移具有良好之預測精度，可提供作為陡坡地防災與利用管理之參考。

關鍵字

地滑 ； 位移 ； 倒傳遞類神經網路 ； 前期降雨量 ； 入滲係數

並列摘要

Landslides has become one disaster type of the most serious destroy on the slope 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 Shanzongliao landslide area, Taitung County, attempt to predict slope movements using back propagation 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 of the ground movements with the help of input variables that have direct physical significance. An infiltration coefficient is introduced in the network architecture apart from antecedent rainfall, rainfall intensity, slope profile, groundwater level and shear strength of soil. A four-layered back propagation neural network with an input layer, two hidden layers and one output layer is found optimal. The developed BPNN model demonstrates a promising result, and have good potential accurately for predicting the slope movement, and can offer the reference of disaster prevention and utilizing management at the steep slope.

並列關鍵字

Landslide, ； slope movement ； Back propagation neural network ； Antecedent rainfall ； Infiltration coefficient

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