Rainfall and earthquake-induced landslide susceptibility assessment using GIS and Artificial Neural Network

Abstract. A GIS-based method for the assessment of landslide susceptibility in a selected area of Qingchuan County in China is proposed by using the back-propagation Artificial Neural Network model (ANN). Landslide inventory was derived from field investigation and aerial photo interpretation. 473 landslides occurred before the Wenchuan earthquake (which were thought as rainfall-induced landslides (RIL) in this study), and 885 earthquake-induced landslides (EIL) were recorded into the landslide inventory map. To understand the different impacts of rainfall and earthquake on landslide occurrence, we first compared the variations between landslide spatial distribution and conditioning factors. Then, we compared the weight variation of each conditioning factor derived by adjusting ANN structure and factors combination respectively. Last, the weight of each factor derived from the best prediction model was applied to the entire study area to produce landslide susceptibility maps. Results show that slope gradient has the highest weight for landslide susceptibility mapping for both RIL and EIL. The RIL model built with four different factors (slope gradient, elevation, slope height and distance to the stream) shows the best success rate of 93%; the EIL model built with five different factors (slope gradient, elevation, slope height, distance to the stream and distance to the fault) has the best success rate of 98%. Furthermore, the EIL data was used to verify the RIL model and the success rate is 92%; the RIL data was used to verify the EIL model and the success rate is 53%.

[1]  G. Wieczorek,et al.  Preparing a Detailed Landslide-Inventory Map for Hazard Evaluation and Reduction , 1984 .

[2]  J H Garrett,et al.  WHERE AND WHY ARTIFICIAL NEURAL NETWORKS ARE APPLICABLE IN CIVIL ENGINEERING , 1994 .

[3]  Lefteri H. Tsoukalas,et al.  Fuzzy and neural approaches in engineering , 1997 .

[4]  C. Chung,et al.  Probabilistic prediction models for landslide hazard mapping , 1999 .

[5]  D. Keefer.  Statistical analysis of an earthquake-induced landslide distribution — the 1989 Loma Prieta, California event , 2000 .

[6]  Thomas Glade,et al.  Applying Probability Determination to Refine Landslide-triggering Rainfall Thresholds Using an Empirical “Antecedent Daily Rainfall Model” , 2000 .

[7]  C. F. Lee,et al.  Landslide characteristics and, slope instability modeling using GIS, Lantau Island, Hong Kong , 2002 .

[8]  Tetsuro Esaki,et al.  GIS-Based Probabilistic Mapping of Landslide Hazard Using a Three-Dimensional Deterministic Model , 2004 .

[9]  P. Reichenbach,et al.  Probabilistic landslide hazard assessment at the basin scale , 2005 .

[10]  M. Crozier.  Multiple-occurrence regional landslide events in New Zealand: Hazard management issues , 2005 .

[11]  L. Ermini,et al.  Artificial Neural Networks applied to landslide susceptibility assessment , 2005 .

[12]  H. A. Nefeslioglu,et al.  Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyses , 2005 .

[13]  Saro Lee,et al.  Earthquake-induced landslide-susceptibility mapping using an artificial neural network , 2006 .

[14]  R. Soeters,et al.  Landslide hazard and risk zonation—why is it still so difficult? , 2006 .

[15]  Shou-Heng Liu,et al.  Impacts of the Chi-Chi earthquake on subsequent rainfall-induced landslides in central Taiwan , 2006 .

[16]  Manoj K. Arora,et al.  A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas , 2006 .

[17]  P. Reichenbach,et al.  Estimating the quality of landslide susceptibility models , 2006 .

[18]  M. Hsu,et al.  Modeling typhoon- and earthquake-induced landslides in a mountainous watershed using logistic regression , 2007 .

[19]  Ming-Lang Lin,et al.  Statistical approach to earthquake-induced landslide susceptibility , 2008 .

[20]  H. A. Nefeslioglu,et al.  An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps , 2008 .

[21]  Lewis A. Owen,et al.  GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region , 2008 .

[22]  Christian Conoscenti,et al.  GIS analysis to assess landslide susceptibility in a fluvial basin of NW Sicily (Italy) , 2008 .

[23]  R. Dahal,et al.  Representative Rainfall thresholds for landslides in the Nepal Himalaya , 2008 .

[24]  S. L. Kuriakose,et al.  Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview , 2008 .

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

[26]  Qiang Xu,et al.  Spatial distribution analysis of landslides triggered by 2008.5.12 Wenchuan Earthquake, China , 2010 .

[27]  Biswajeet Pradhan,et al.  Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling , 2010, Environ. Model. Softw..

[28]  Fausto Guzzetti,et al.  Geographical Information Systems in Assessing Natural Hazards , 2010 .

[29]  Chuan Tang,et al.  Landslide hazard assessment of the 2008 Wenchuan earthquake: a case study in Beichuan area , 2011 .

[30]  Xu Qiang,et al.  Spatial distribution of large-scale landslides induced by the 5.12 Wenchuan Earthquake , 2011 .

[31]  L. Ayalew,et al.  The spatial correlation between earthquakes and landslides in Hokkaido (Japan), a GIS-based analysis of the past and the future , 2011 .

[32]  Calculating specific catchment area , 2011 .