A Research on Susceptibility Mapping of Multiple Geological Hazards in Yanzi River Basin, China

Collapses, landslides, and debris flows are the main geological hazards faced by mankind, which bring heavy losses of life and property to people every year. The purpose of this paper is to establish a method for determining the optimal weighting scheme for multiple geological hazard susceptibility mapping. The information gain ratio (IGR) method was used to analyze the predictive ability of the conditioning factors. The support vector machine (SVM) algorithm was used to evaluate the susceptibility to collapse, landslide, and debris flow of the study area. The receiver operating characteristic curves (ROC) and classification statistics of geological hazard samples were applied to evaluate the performance of the models. The analytic hierarchy process (AHP) and frequency ratio (FR) method were combined to determine the optimal weighting scheme for collapse, landslide, and debris flow. All the conditioning factors have shown a certain predictive ability, making the models of collapse, landslide, and debris flow achieve very good performance. The multiple geological hazard susceptibility maps with the weights of 0.297, 0.539, and 0.164 for collapse, landslide, and debris flow was optimal for this study area with high-precision classification of all the geological hazard samples. The conclusions of this paper could provide meaningful references for risk migration and land use in the study area.

[1]  Zhu Liang,et al.  Classification and susceptibility assessment of debris flow based on a semi-quantitative method combination of the fuzzy C-means algorithm, factor analysis and efficacy coefficient , 2020 .

[2]  Ali P. Yunus,et al.  Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance , 2020 .

[3]  Georgios Aim. Skianis,et al.  Assessment of rural community and agricultural development using geomorphological–geological factors and GIS in the Trikala prefecture (Central Greece) , 2013, Stochastic Environmental Research and Risk Assessment.

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

[5]  Dieu Tien Bui,et al.  A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers , 2019, Geocarto International.

[6]  Ali P. Yunus,et al.  Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan , 2019, Landslides.

[7]  P. Reichenbach,et al.  Landslide hazard assessment in the Collazzone area, Umbria, Central Italy , 2006 .

[8]  Ataollah Shirzadi,et al.  Development of an Artificial Intelligence Approach for Prediction of Consolidation Coefficient of Soft Soil: A Sensitivity Analysis , 2019, The Open Construction and Building Technology Journal.

[9]  B. Pradhan,et al.  Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran , 2013, Journal of Earth System Science.

[10]  Saro Lee,et al.  Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree , 2017 .

[11]  P. Reichenbach,et al.  A review of statistically-based landslide susceptibility models , 2018 .

[12]  L. Tham,et al.  Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China , 2008 .

[13]  S. Bouchon,et al.  A methodological approach for the definition of multi‐risk maps at regional level: first application , 2009 .

[14]  Ali P. Yunus,et al.  Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques , 2019, Scientific Reports.

[15]  Biswajeet Pradhan,et al.  Suitability estimation for urban development using multi-hazard assessment map. , 2017, The Science of the total environment.

[16]  Liangjie Wang,et al.  A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network , 2016, Geosciences Journal.

[17]  Seyed Amir Naghibi,et al.  A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China , 2018, Bulletin of Engineering Geology and the Environment.

[18]  Biswajeet Pradhan,et al.  Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area , 2011, Comput. Geosci..

[19]  Tom Fawcett,et al.  Robust Classification for Imprecise Environments , 2000, Machine Learning.

[20]  D. Rozos,et al.  Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: a case study from the Eastern Achaia County of Peloponnesus, Greece , 2011 .

[21]  T. L. Saaty.  A Scaling Method for Priorities in Hierarchical Structures , 1977 .

[22]  Jie Dou,et al.  New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed , 2019, Forests.

[23]  Mustafa Neamah Jebur,et al.  Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines , 2015, Environmental Earth Sciences.

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

[25]  A. Pradhan,et al.  Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea , 2014, Natural Hazards.

[26]  Szu-Hsien Peng,et al.  Potential Hazard Map for Disaster Prevention Using GIS-Based Linear Combination Approach and Analytic Hierarchy Method , 2012 .

[27]  Fuchu Dai,et al.  Landslide risk assessment and management: an overview , 2002 .

[28]  B. Pradhan,et al.  A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods , 2019, Journal of Hydrology.

[29]  J. Malet,et al.  Recommendations for the quantitative analysis of landslide risk , 2013, Bulletin of Engineering Geology and the Environment.

[30]  D. Bui,et al.  A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area, India , 2017, International Journal of Sediment Research.

[31]  Chong Xu,et al.  GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China , 2012 .

[32]  Binh Thai Pham,et al.  Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping , 2020 .

[33]  Wei Chen,et al.  GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models , 2017 .

[34]  Martin Mergili,et al.  Regional-scale analysis of high-mountain multi-hazard and risk indicators in the Pamir (Tajikistan) with GRASS GIS , 2013 .

[35]  Susan L. Cutter,et al.  Integrated Multihazard Mapping , 2010 .

[36]  B. Pradhan,et al.  GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks , 2016, Environmental Earth Sciences.

[37]  Valentina Gallina,et al.  A review of multi-risk methodologies for natural hazards: Consequences and challenges for a climate change impact assessment. , 2016, Journal of environmental management.

[38]  Turan Erden,et al.  Net earthquake hazard and elements at risk (NEaR) map creation for city of Istanbul via spatial multi-criteria decision analysis , 2014, Natural Hazards.

[39]  T. Saaty.  How to Make a Decision: The Analytic Hierarchy Process , 1990 .

[40]  Linwei Sun,et al.  Multiple geo-environmental hazards susceptibility assessment: a case study in Luoning County, Henan Province, China , 2019, Geomatics, Natural Hazards and Risk.

[41]  Maria Laura Mastellone,et al.  Basic principles of multi-risk assessment: a case study in Italy , 2012, Natural Hazards.