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

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.Theclassification accuracy of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors

Scaling And Morphologic Analyses Of Topsar DEMs: A Quantitative Characterisation Perspective

Digital Elevation Models (DEMs) have been widely used for terrain analysis and characterisation. With advent of Synthetic Aperture Radar (SAR) interferometry techniques, DEMs with fine resolution and high accuracy are made available. Motivated by the availability of this remarkable DEM, the terrain properties are analysed by proposing new scaling laws and roughness related indices in this research work. The study areas are part of Cameron Highlands and Petaling regions of Peninsular Malaysia

Phase Unwrapping In SAR Interferometry

Various phase unwrapping algorithms have been developed for extraction of topographic digital terrain model (DTM) and displacement detection in SAR Interferometry since 1988. However, problems such as accuracy and speed of phase unwrapping process still remain a challenge for SAR Interferometry applications. In this thesis, both path following and least square methods for phase unwrapping are analysed. Path following method includes branch-cut, guality-guided, mask-cut and minimum discontinuity methods while least square method covers both weighted least square and unweighted least square method. Quality-guided and mask-cut methods need quality information, whereas minimum discontinuity and least square methods can be implemented either with or without quality information. Quality information applied in this project are correlation map, phase derivative variance map and maximum phase gradient map. The disadvantages associated with weighted minimum discontinuity method and weighted least square method using quality maps are low accuracy and long execution time. Therefore in this project, the use of mask maps in weighted minimum discountinuity method and weighted least square method is studied to subtitute quality maps. This will increase the accuracy of the unwrapped results and reduce the execution time. In addition, we study the possibilities of mask maps to replace quality maps in mask-cut method. We also introduce residue-based quality maps for quality-guided method and residue-based mask maps for mask-cut method, weighted minimum discontinuity method and weighted least square method. The motivition of studying residue-based quality maps and residue-based mask maps is that most of the errors in the unwrapped results propagate from the residue pixels, i.e. phase inconsistencies. Residue-based quality maps and residue-based mask maps deal with the residues directly by masking out them in the maps and this will reduce error propagation. The implementations of residue-based quality maps in quality -guided method, residue-based mask maps and conventional mask maps in mask-cut method, weighted minimum discontinuity method and weighted least square method are applied on two simulated SAR images, I.e. Long's Peak image and Isolation Peak image. The unwrapped solutions are analysed based on the their accuracy percentages, average execution time and memory requirements. The evaluations and comparisons between unwrapped solution of our proposed method and those of existing method are carried out in this project. The application of mask maps in weighted minimum discontinuity method and weighted least square method give satisfactory results with higher accuracy and shorter execution time as compared to those using quality maps. In general, residue-based quality maps and residue-based mask maps are found to be more suitable in phase unwrapping algorithms than quality maps and mask maps respectively, because they mask out all residue pixels and thus the possibilities of errors propagating from residue pixels to other pixels are reduced significantly

Morphological convexity measures for terrestrial basins derived from digital elevation models

Geophysical basins of terrestrial surfaces have been quantitatively characterized through a host of indices such as topological quantities (e.g. channel bifurcation and length ratios), allometric scaling exponents (e.g. fractal dimensions), and other geomorphometric parameters (channel density, Hack's and Hurst exponents). Channel density, estimated by taking the ratio between the length of channel network (L) and the area of basin (A) in planar form, provides a quantitative index that has hitherto been related to various geomorphologically significant processes. This index, computed by taking the planar forms of channel network and its corresponding basin, is a kind of convexity measure in the two-dimensional case. Such a measure - estimated in general as a function of basin area and channel network length, where the important elevation values of the topological region within a basin and channel network are ignored - fails to capture the spatial variability between homotopic basins possessing different altitude-ranges. Two types of convexity measures that have potential to capture the terrain elevation variability are defined as the ratio of (i) length of channel network function and area of basin function and ( ii) areas of basin and its convex hull functions. These two convexity measures are estimated in three data sets that include (a) synthetic basin functions, (b) fractal basin functions, and (c) realistic digital elevation models (DEMs) of two regions of peninsular Malaysia. It is proven that the proposed convexity measures are altitude-dependent and that they could capture the spatial variability across the homotopic basins of different altitudes. It is also demonstrated on terrestrial DEMs that these convexity measures possess relationships with other quantitative indexes such as fractal dimensions and complexity measures (roughness indexes). (C) 2010 Elsevier Ltd. All rights reserved

Modeling and Testing Landslide Hazard Using Decision Tree

This paper proposes a decision tree model for specifying the importance of 21 factors causing the landslides in a wide area of Penang Island, Malaysia. These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. Four models were created using Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CRT), and Quick-Unbiased-Efficient Statistical Tree (QUEST). Twenty-one factors were extracted using digital elevation models (DEMs) and then used as input variables for the models. A data set of 137570 samples was selected for each variable in the analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. 10-fold cross-validation was employed for testing the models. The highest accuracy was achieved using Exhaustive CHAID (82.0%) compared to CHAID (81.9%), CRT (75.6%), and QUEST (74.0%) model. Across the four models, five factors were identified as most important factors which are slope angle, distance from drainage, surface area, slope aspect, and cross curvature

Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron

Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP) and Cascade Forward Neural Network (CFNN), are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Scaled Conjugate Gradient, Conjugate Gradient with Beale, Conjugate Gradient with Fletcher Reeves updates, Conjugate Gradient with Polakribiere updates, One Step Secant, Gradient Descent, Gradient Descent with Momentum and Adaptive Learning Rate, and Gradient Descent with Momentum algorithm. Often, the performance of the landslide prediction depends on the input factors beside the prediction method. In this research work, 14 input factors were used. The prediction accuracies of networks were verified using the Area under the Curve method for the Receiver Operating Characteristics. The results indicated that the best prediction accuracy of 82.89% was achieved using the CFNN network with the Levenberg Marquardt learning algorithm for the training data set and 81.62% for the testing data set

Allometric relationships between traveltime channel networks, convex hulls, and convexity measures

The channel network (S) is a nonconvex set, while its basin [C( S)] is convex. We remove open-end points of the channel connectivity network iteratively to generate a traveltime sequence of networks (S-n). The convex hulls of these traveltime networks provide an interesting topological quantity, which has not been noted thus far. We compute lengths of shrinking traveltime networks L(S-n) and areas of corresponding convex hulls C(S-n), the ratios of which provide convexity measures CM(S-n) of traveltime networks. A statistically significant scaling relationship is found for a model network in the form L(S-n) similar to A[ C(S-n)](0.57). From the plots of the lengths of these traveltime networks and the areas of their corresponding convex hulls as functions of convexity measures, new power law relations are derived. Such relations for a model network are CM(S-n) similar to 1/L(S-n)(0.7) and CM(S-n) similar to 1/ A[C(S-n)](0.43). In addition to the model study, these relations for networks derived from seven subbasins of Cameron Highlands region of Peninsular Malaysia are provided. Further studies are needed on a large number of channel networks of distinct sizes and topologies to understand the relationships of these new exponents with other scaling exponents that define the scaling structure of river networks