A data-driven approach for landslide susceptibility mapping: a case study of Shennongjia Forestry District, China

Abstract The main purpose of this study was to establish a data-driven approach and assess its potential for shallow landslide susceptibility mapping of the Shennongjia Forestry District, China. For the data processing, Fisher segmenting was used for the classification of topographical factors (gradient, relief amplitude, plan curvature, and normalized difference vegetation index) and the improved Otsu method was used to determine the buffer length thresholds of the locational factors (proximity to roads, rivers and faults). The computations show that these two objective methods help to avoid the uncertainty caused by the subjective judgments and the obtained thresholds are more consistent with actual conditions. Considering the different data types, the locational and topographical factors were evaluated by fuzzy comprehensive evaluation while the geological factors were evaluated by frequency index, and then the three separate evaluations were combined using radar chart analysis. The validation result shows that the susceptibility map based on a data-driven approach has a high accuracy of prediction and this approach will be salutary for risk mitigation and land-use management in mountainous regions.

[1]  B. Pradhan,et al.  Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy , 2016 .

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

[3]  Jizhen Liu,et al.  A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler , 2013 .

[4]  Chen Jingming.  Evaluation of topographic effects on four commonly used vegetation indices , 2013 .

[5]  Beat Kleiner,et al.  Graphical Methods for Data Analysis , 1983 .

[6]  L. Ayalew,et al.  Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan , 2004 .

[7]  Mazlan Hashim,et al.  Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment , 2015, Scientific Reports.

[8]  Michael Friendly,et al.  Milestones in the history of thematic cartography, statistica l graphics, and data visualization , 2008 .

[9]  Bujang B. K. Huat,et al.  Evaluation and development of cut-slope assessment systems for peninsular Malaysia in predicting landslides in granitic formation , 2006 .

[10]  B. Pradhan.  Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia , 2010 .

[11]  B. Pradhan,et al.  A comparative assessment of prediction capabilities of Dempster–Shafer and Weights-of-evidence models in landslide susceptibility mapping using GIS , 2013 .

[12]  B. Pradhan,et al.  Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models , 2012 .

[13]  M. Matteucci,et al.  Artificial neural networks and cluster analysis in landslide susceptibility zonation , 2008 .

[14]  C Russell Middaugh,et al.  Radar chart array analysis to visualize effects of formulation variables on IgG1 particle formation as measured by multiple analytical techniques. , 2013, Journal of pharmaceutical sciences.

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

[16]  Ming-Lang Lin,et al.  Statistical approach to storm event-induced landslides susceptibility , 2008 .

[17]  Chen Liang,et al.  A GIS-based buffer gradient analysis on spatiotemporal dynamics of urban expansion in Shanghai and its major satellite cities , 2010 .

[18]  C. J. Westen,et al.  Qualitative landslide susceptibility assessment by multicriteria analysis: A case study from San Antonio del Sur, Guantánamo, Cuba , 2008 .

[19]  B. Pradhan,et al.  Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg–Marquardt and Bayesian regularized neural networks , 2012 .

[20]  Norman Kerle,et al.  Landslide hazard and risk assessment using semi-automatically created landslide inventories , 2013 .

[21]  S. Stegmann,et al.  Hydrogeology and its effect on slope stability along the coastal aquifer of Nice, France , 2011 .

[22]  John M. Chambers,et al.  Graphical Methods for Data Analysis , 1983 .

[23]  M. J. Crozier,et al.  A proposed cell model for multiple-occurrence regional landslide events: Implications for landslide susceptibility mapping , 2017 .

[24]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[25]  Haluk Akgün,et al.  Evaluation and comparison of landslide susceptibility mapping methods: a case study for the Ulus district, Bartın, northern Turkey , 2015, Int. J. Geogr. Inf. Sci..

[26]  Koichi Suzuki,et al.  Groundwater flow after heavy rain in landslide-slope area from 2-D inversion of resistivity monitoring data , 2001 .

[27]  Ali Asghar Alesheikh,et al.  Sustainability Through Uncertainty Management in Urban Land Suitability Assessment , 2013 .

[28]  B. Pradhan,et al.  Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran , 2012, Natural Hazards.

[29]  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 .

[30]  Omid Rahmati,et al.  Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models , 2016 .

[31]  Kang-Tsung Chang,et al.  Modeling shallow landsliding susceptibility by incorporating heavy rainfall statistical properties , 2011 .

[32]  Tri Dev Acharya,et al.  Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China) , 2018 .

[33]  P. Reichenbach,et al.  Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy , 1999 .

[34]  Wei Chen,et al.  A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China , 2017 .

[35]  Lan Feng,et al.  Assessing coastal reclamation suitability based on a fuzzy-AHP comprehensive evaluation framework: A case study of Lianyungang, China. , 2014, Marine pollution bulletin.

[36]  F. Bulut,et al.  Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey) , 2007 .

[37]  C. Gokceoglu,et al.  Landslide Susceptibility Zoning North of Yenice (NW Turkey) by Multivariate Statistical Techniques , 2004 .

[38]  B. Pradhan,et al.  Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran , 2012 .

[39]  Chunfang Kong,et al.  Landslide susceptibility evaluation based on BPNN and GIS: a case of Guojiaba in the Three Gorges Reservoir Area , 2015, Int. J. Geogr. Inf. Sci..

[40]  P. Reichenbach,et al.  Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium , 2009 .

[41]  Huichan Chai,et al.  Application of frequency ratio, statistical index, and index of entropy models and their comparison in landslide susceptibility mapping for the Baozhong Region of Baoji, China , 2015, Arabian Journal of Geosciences.

[42]  Selçuk Reis,et al.  Designing and Developing a Province-Based Spatial Database for the Analysis of Potential Environmental Issues in Trabzon, Turkey , 2008 .

[43]  L. Cascini,et al.  Application of a SPH depth-integrated model to landslide run-out analysis , 2014, Landslides.

[44]  John P. Wilson,et al.  Multi-scale linkages between topographic attributes and vegetation indices in a mountainous landscape , 2007 .

[45]  Wang Jiaji.  LANDSLIDE SUSCEPTIBILITY ASSESSMENT BASED ON GIS AND WEIGHTED INFORMATION VALUE:A CASE STUDY OF WANZHOU DISTRICT,THREE GORGES RESERVOIR , 2014 .

[46]  Chin-Laung Lei,et al.  Radar chart: scanning for satisfactory QoE in QoS dimensions , 2012, IEEE Network.

[47]  B. Pradhan,et al.  Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya , 2012, Natural Hazards.

[48]  P. Reichenbach,et al.  Optimal landslide susceptibility zonation based on multiple forecasts , 2010 .

[49]  Inge Revhaug,et al.  Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan , 2015, PloS one.

[50]  Edwin T. Engman,et al.  Status of remote sensing algorithms for estimation of land surface state parameters , 1995 .