A novel genetic algorithm for optimization of conditioning factors in shallow translational landslides and susceptibility mapping

Genetic algorithm (GA) is an effective approach in selecting the best factors without considering all possible combinations in landslide susceptibility mapping (LSM). The approach experienced a local optimal solution for hazard mapping. In this study, we propose a novel genetic algorithm (NGA) for solving the problems of optimal precision in selecting conditioning factors based on the crossover and mutation. In the southwestern part of China, including Wenchuan, Ludshan, and Ludian areas, the findings of this study confirm the applicability of NGA, which has a strong robustness compared to GA obviously. Results indicated that the highest area under curve (AUC) of GA is 93.47, 83.45, and 82.21% in Wenchuan, Lushan, and Ludian, respectively. Cumulative error of the precision (∆R) is 3.19, 10.48, and 6.05%, and error of the highest precision (∆P) is 0.01, 0.03, and 0.12% for Wenchuan, Lushan, and Ludian, respectively. Compared to the GA, the highest accuracy of NGA is 93.48% (Wenchuan), 83.48% (Lushan), and 82.28% (Ludian). It also revealed that ∆R is 0.77, 1.26, and 1.82%, and ∆P is 0.00, 0.04, and 0.05% for Wenchuan, Lushan, and Ludian, respectively. By comparing with GA, the novel approach of NGA has stronger robustness and higher accuracy on selecting the optimal conditioning factors of landslide. Additionally, the relationship of landslide occurrence with controlling factors was assessed in every study area. According to the results, lithology, distance to roads, elevation, and slope were regarded as the most effective factors for shallow translational landslides. These factors implied that internal structure and composition of rock, anthropogenic activity, and topography factors posed the main impacts on landslide occurrence. Finally, we implemented landslide susceptibility assessment in three study areas. Results showed that high landslide susceptibility was in the east and northeastern parts of Wenchuan; central region northward of Lushan; and southwest, central region, and west of Ludian.

[1]  Gustavo E. A. P. A. Batista,et al.  Evaluating Classifiers Using ROC Curves , 2008, IEEE Latin America Transactions.

[2]  Yang Li,et al.  Land suitability assessment for post-earthquake reconstruction: A case study of Lushan in Sichuan, China , 2015, Journal of Geographical Sciences.

[3]  G. Shi.  Basin Modeling in the Kuqa Depression of the Tarim Basin (Western China): A Fully Temperature-dependent Model of Overpressure History , 2008 .

[4]  Hans C. Jessen,et al.  Applied Logistic Regression Analysis , 1996 .

[5]  H. Shafri,et al.  Landslides and lineament mapping along the Simpang Pulai to Kg Raja highway, Malaysia , 2011 .

[6]  S. L. Kuriakose,et al.  From Landslide Inventories to Landslide Risk Assessment; An Attempt to Support Methodological Development in India , 2013 .

[7]  Chuan Tang,et al.  Landslides triggered by the 20 April 2013 Lushan earthquake, Sichuan Province, China , 2014 .

[8]  Zhiqiang Yin,et al.  Distribution characteristics of geohazards induced by the Lushan Earthquake and their comparisons with the Wenchuan Earthquake , 2014, Journal of Earth Science.

[9]  Jakub Nalepa,et al.  Adaptive Genetic Algorithm to Select Training Data for Support Vector Machines , 2014, EvoApplications.

[10]  L. Ayalew,et al.  The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan , 2005 .

[11]  Implementation of Random Forest in landslide susceptibility study, a case study of the Tokamachi area, Niigata, Japan , 2014 .

[12]  Zhi-hua Yang,et al.  Post-earthquake rainfall-triggered slope stability analysis in the Lushan area , 2015, Journal of Mountain Science.

[13]  Juan Remondo,et al.  Quantitative landslide risk assessment and mapping on the basis of recent occurrences , 2008 .

[14]  B. Pradhan,et al.  Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models , 2015, Geosciences Journal.

[15]  S. Pirasteh,et al.  Use of Digital Elevation Model for Study of Drainage Morphometry and Identification Stability and Saturation Zones in Relations to Landslide Assessments in Parts of the Shahbazan Area, SW Iran , 2003 .

[16]  Li Yong,et al.  GEOLOGICAL BACKGROUND OF LONGMEN SHAN SEISMIC BELT AND SURFACE RUPTURES IN WENCHUAN EARTHQUAKE , 2009 .

[17]  Ping Lu,et al.  Object-Oriented Change Detection for Landslide Rapid Mapping , 2011, IEEE Geoscience and Remote Sensing Letters.

[18]  P. Cui,et al.  Susceptibility and risk assessment of earthquake-induced landslides based on Landslide Response Units in the Subao River basin, China , 2012, Environmental Earth Sciences.

[19]  Chai Tianyou,et al.  Survey on Genetic Algorithm , 1996 .

[20]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[21]  H. Shahabi,et al.  Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models , 2014 .

[22]  许冲,et al.  inventory of landslides triggered by the 2014 ms6.5 ludian earthquake and its implications on several earthquake parameters , 2014 .

[23]  Genlong Wang,et al.  Comparison of the landslides triggered by the 2013 Lushan earthquake with those triggered by the strong 2008 Wenchuan earthquake in areas with high seismic intensities , 2015, Bulletin of Engineering Geology and the Environment.

[24]  Huang Run-qiu,et al.  LARGE-SCALE LANDSLIDES AND THEIR SLIDING MECHANISMS IN CHINA SINCE THE 20TH CENTURY , 2007 .

[25]  T. Kavzoglu,et al.  Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm , 2015 .

[26]  Rui Liu,et al.  Analysis of landslide hazard area in Ludian earthquake based on Random Forests , 2015 .

[27]  S. Bai,et al.  GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China , 2010 .

[28]  Saro Lee,et al.  Determination and application of the weights for landslide susceptibility mapping using an artificial neural network , 2004 .

[29]  Xu Chong.  GIS PLATFORM AND CERTAINTY FACTOR ANALYSIS METHOD BASED WENCHUAN EARTHQUAKE-INDUCED LANDSLIDE SUSCEPTIBILITY EVALUATION , 2010 .

[30]  Present-day crustal movement and tectonic deformation in China continent , 2002 .

[31]  Chong Xu,et al.  Landslides triggered by slipping-fault-generated earthquake on a plateau: an example of the 14 April 2010, Ms 7.1, Yushu, China earthquake , 2013, Landslides.

[32]  Rong Long Wang,et al.  An improved genetic algorithm with conditional genetic operators and its application to set-covering problem , 2007, Soft Comput..

[33]  J. Shaw,et al.  Structural geometry of the source region for the 2013 Mw 6.6 Lushan earthquake: Implication for earthquake hazard assessment along the Longmen Shan , 2014 .

[34]  Manfred F. Buchroithner,et al.  Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model , 2011 .

[35]  Mrinal Kanti Ghose,et al.  Global Search Analysis of Spatial Gene Expression Data Using Genetic Algorithm , 2010, CNSA.

[36]  Christos Polykretis,et al.  A comparative study of landslide susceptibility mapping using landslide susceptibility index and artificial neural networks in the Krios River and Krathis River catchments (northern Peloponnesus, Greece) , 2015, Bulletin of Engineering Geology and the Environment.

[37]  Yecai Guo,et al.  Support Vector Machine Wavelet Blind Equalization Algorithm Based on Improved Genetic Algorithm , 2012 .

[38]  Ramkrishna Maiti,et al.  Integrating the Analytical Hierarchy Process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shiv-khola watershed, Darjeeling Himalaya , 2013, International Journal of Disaster Risk Science.

[39]  Jixian Xiao,et al.  Application of Improved Genetic Algorithm in Logistics Transportation , 2012 .

[40]  Huang Jingfeng,et al.  GIS-based logistic regression method for landslide susceptibility mapping in regional scale * , 2006 .

[41]  Saro Lee,et al.  Probabilistic landslide susceptibility and factor effect analysis , 2005 .

[42]  I. Yilmaz.  Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine , 2010 .

[43]  Cristiano Ballabio,et al.  Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy , 2012, Mathematical Geosciences.

[44]  Saro Lee,et al.  Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data , 2005 .

[45]  Hajir Karimi,et al.  Correlation of viscosity in nanofluids using genetic algorithm-neural network (GA-NN) , 2011 .

[46]  A. Ferrer,et al.  A Logistic Regression Model Applied to Evaluate the Influence of Operating Time of AgI Ground Acetonic Generators on the Size and Hardness of Hail , 2004 .

[47]  S. Mandal,et al.  Application of Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) Model in Assessing Landslide Susceptibility and Risk , 2015 .

[48]  P. Flentje,et al.  Landslides qualitative hazard and risk assessment method and its reliability , 2004 .

[49]  Wu Yi.  Survey on genetic algorithm applying to reactive power optimization of power systems , 2002 .

[50]  B. Pradhan,et al.  Landslide susceptibility assessment at Wadi Jawrah Basin, Jizan region, Saudi Arabia using two bivariate models in GIS , 2015, Geosciences Journal.

[51]  Honglin He,et al.  Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China , 2013, Natural Hazards.

[52]  H. A. Nefeslioglu,et al.  Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey , 2006 .