Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network

Umi Kalthum Ngah,Lea Tien Tay,Mohammad Subhi Al-Batah,Nor Ashidi Mat Isa,Mutasem Sh Alkhasawneh

doi:10.1155/2013/415023

Umi Kalthum Ngah, Lea Tien Tay + Show 3 more

Open Access

https://doi.org/10.1155/2013/415023

Copy DOI

Abstract

Landslide is one of the natural disasters that occur in Malaysia. Topographic factors such as elevation, slope angle, slope aspect, general curvature, plan curvature, and profile curvature are considered as the main causes of landslides. In order to determine the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper. There are three main stages involved in this study. The first stage is the extraction of extra topographic factors. Previous landslide studies had identified mainly six topographic factors. Seven new additional factors have been proposed in this study. They are longitude curvature, tangential curvature, cross section curvature, surface area, diagonal line length, surface roughness, and rugosity. The second stage is the specification of the weight of each factor using two methods. The methods are multilayer perceptron (MLP) network classification accuracy and Zhou's algorithm. At the third stage, the factors with higher weights were used to improve the MLP performance. Out of the thirteen factors, eight factors were considered as important factors, which are surface area, longitude curvature, diagonal length, slope angle, elevation, slope aspect, rugosity, and profile curvature. The classification accuracy of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors.

Highlights

Landslide is one of the most aggressive natural disasters that causes loss of lives and billions of dollars worth of damages annually worldwide [1]
Slope aspect, plan curvature, profile curvature and general curvature, are the conventional topographic factors which are extracted from digital elevation model [15]
These factors were analysed for the importance rating of factors by using two different methods, that is, multilayer perceptron (MLP) network layers weights (Zhou method) and output classification accuracy

Summary

Introduction

Landslide is one of the most aggressive natural disasters that causes loss of lives and billions of dollars worth of damages annually worldwide [1]. Some studies have merged the DEM to landslide hazard mapping in their applications [3, 6, 7, 17] Neural networks have gained popularity from their simplicity, generality and easy application. They have shown good performance when used in landslides prediction and weight determination of the landslide causative factors [18, 19]. In the year 1999, Zhou has introduced an algorithm to determine the weights of each of the input factors through the neural network training.

Methods

Results

Conclusion