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

被引:47
作者
Alkhasawneh, Mutasem Sh. [1 ]
Ngah, Umi Kalthum [1 ]
Tay, Lea Tien [1 ]
Isa, Nor Ashidi Mat [1 ]
Al-batah, Mohammad Subhi [2 ]
机构
[1] Univ Sains Malaysia, Sch Elect & Elect Engn, Imaging & Computat Intelligence ICI Grp, Nibong Tebal 14300, Penang, Malaysia
[2] Jadara Univ, Fac Sci & Informat Technol, Dept Comp Sci, Irbid 21110, Jordan
关键词
LOGISTIC-REGRESSION; REGION; SLOPE; MODEL; HAZARD;
D O I
10.1155/2013/415023
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
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页数:12
相关论文
共 41 条
[11]  
Beale MH, 2009, NEURAL NETWORK TOOLB
[12]  
Berry J.K., 2002, Geoworld, V15, P20
[13]   Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey) [J].
Ercanoglu, M ;
Gokceoglu, C .
ENGINEERING GEOLOGY, 2004, 75 (3-4) :229-250
[14]   Artificial Neural Networks applied to landslide susceptibility assessment [J].
Ermini, L ;
Catani, F ;
Casagli, N .
GEOMORPHOLOGY, 2005, 66 (1-4) :327-343
[15]  
Evans I S, 1979, Final report on grant DA-ERO-591-73-G0040
[16]   Discontinuity controlled probabilistic slope failure risk maps of the Altindag (settlement) region in Turkey [J].
Gokceoglu, C ;
Sonmez, H ;
Ercanoglu, M .
ENGINEERING GEOLOGY, 2000, 55 (04) :277-296
[17]   Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland [J].
Grohmann, Carlos Henrique ;
Smith, Mike J. ;
Riccomini, Claudio .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2011, 49 (04) :1200-1213
[18]   Computing surface roughness of individual cells of digital elevation models via multiscale analysis [J].
Hani, Ahmad Fadzil Mohamad ;
Sathyamoorthy, Dinesh ;
Asiryadam, Vijanth Sagayan .
COMPUTERS & GEOSCIENCES, 2012, 43 :137-146
[19]  
Hobson R. D., 1972, Spatial analysis in geomorphology, P225
[20]  
Hongling Tian, 2010, Proceedings 2010 Sixth International Conference on Natural Computation (ICNC 2010), P1830, DOI 10.1109/ICNC.2010.5584507