Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

Landslides are among the most frequent natural hazards in the world. Rainfall is an important triggering factor for landslides and is responsible for topples, slides, and debris flows—three of the most important types of landslides. However, several previous relevant research studies covered general landslides and neglected the rainfall–topples–slides–debris flows disaster chain. Since landslide hazard mapping (LHM) is a critical tool for disaster prevention and mitigation, this study aimed to build a GeoDetector and Bayesian network (BN) model framework for LHM in Shuicheng County, China, to address these geohazards. The GeoDetector model will be used to screen factors, eliminate redundant information, and discuss the interaction between elements, while the BN model will be used for constructing a causality disaster chain network to determine the probability and risk level of the three types of landslides. The practicability of the BN model was confirmed by error rate and scoring rules validation. The prediction accuracy results were tested using overall accuracy, Matthews correlation coefficient, relative operating characteristics curve, and seed cell area index. The proposed framework is demonstrated to be sufficiently accurate to construct the complex LHM. In summary, the combination of the GeoDetector and BN model is very promising for spatial prediction of landslides.

[1]  Yang Yang,et al.  New method for landslide susceptibility mapping supported by spatial logistic regression and GeoDetector: A case study of Duwen Highway Basin, Sichuan Province, China , 2019, Geomorphology.

[2]  Xiaoying Zheng,et al.  Geographical Detectors‐Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China , 2010, Int. J. Geogr. Inf. Sci..

[3]  M. Rossi,et al.  Rainfall thresholds for the initiation of landslides in central and southern Europe , 2007 .

[4]  Mustafa Neamah Jebur,et al.  Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale , 2014 .

[5]  Yinhui Li,et al.  An efficient intrusion detection system based on support vector machines and gradually feature removal method , 2012, Expert Syst. Appl..

[6]  Yimin Mao,et al.  Landslide susceptibility assessment using uncertain decision tree model in loess areas , 2017, Environmental Earth Sciences.

[7]  Qingyun Du,et al.  Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China , 2019, International journal of environmental research and public health.

[8]  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..

[9]  B. Pham,et al.  Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods , 2017, Theoretical and Applied Climatology.

[10]  B. Pham,et al.  Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS , 2016, Natural Hazards.

[11]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[12]  Wanchang Zhang,et al.  GIS-based earthquake-triggered-landslide susceptibility mapping with an integrated weighted index model in Jiuzhaigou region of Sichuan Province, China , 2019, Natural Hazards and Earth System Sciences.

[13]  A. Troncone,et al.  A method for the analysis of soil slips triggered by rainfall , 2012 .

[14]  Tianrong Huang,et al.  Using Bayesian networks in analyzing powerful earthquake disaster chains , 2013, Natural Hazards.

[15]  Yichen Zhang,et al.  Hazard Assessment of Earthquake Disaster Chains Based on a Bayesian Network Model and ArcGIS , 2019, ISPRS Int. J. Geo Inf..

[16]  B. Marcot,et al.  Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation , 2006 .

[17]  D. Petley,et al.  A Systematic Review of the Health Impacts of Mass Earth Movements (Landslides) , 2015, PLoS currents.

[18]  Wei Luo,et al.  Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods , 2018, Landslides.

[19]  Wei Chen,et al.  Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China , 2017, Landslides.

[20]  V. Doyuran,et al.  A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate , 2004 .

[21]  Yi Wang,et al.  Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. , 2019, The Science of the total environment.

[22]  H. Pourghasemi,et al.  Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions , 2018, Bulletin of Engineering Geology and the Environment.

[23]  C. Wu.  Landslide Susceptibility Based on Extreme Rainfall-Induced Landslide Inventories and the Following Landslide Evolution , 2019 .

[24]  Kai Virtanen,et al.  Improving Construction of Conditional Probability Tables for Ranked Nodes in Bayesian Networks , 2016, IEEE Transactions on Knowledge and Data Engineering.

[25]  Pijush Samui,et al.  Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area , 2020 .

[26]  A-Xing Zhu,et al.  Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping , 2018, CATENA.

[27]  D. Petley.  Global patterns of loss of life from landslides , 2012 .

[28]  Ali P. Yunus,et al.  Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. , 2020, The Science of the total environment.

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

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

[31]  Santiago Beguería,et al.  Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery , 2011, Remote. Sens..

[32]  Huabei Liu,et al.  Bayesian network models for probabilistic evaluation of earthquake-induced liquefaction based on CPT and Vs databases , 2019, Engineering Geology.

[33]  Jie Dou,et al.  Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm , 2015, Remote. Sens..

[34]  Candan Gokceoglu,et al.  Discussion on “Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS” by Choi et al. (2012), Engineering Geology, 124, 12–23 , 2012 .

[35]  Fausto Guzzetti,et al.  Rainfall thresholds for the possible occurrence of landslides in Italy , 2010 .

[36]  A. Troncone,et al.  Stability analysis of infinite clayey slopes subjected to pore pressure changes , 2012 .

[37]  Wei Chen,et al.  Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility , 2019, CATENA.

[38]  Hong-jian Zhou,et al.  Risk assessment of disaster chain: Experience from Wenchuan earthquake-induced landslides in China , 2015, Journal of Mountain Science.

[39]  Qiyun Ma,et al.  Dynamics of Fractional Vegetation Coverage and Its Relationship with Climate and Human Activities in Inner Mongolia, China , 2016, Remote. Sens..

[40]  B. Pham,et al.  Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. , 2019, The Science of the total environment.

[41]  S. L. Gariano,et al.  An algorithm for the objective reconstruction of rainfall events responsible for landslides , 2015, Landslides.

[42]  Yuk Feng Huang,et al.  Rainfall-induced landslides in Hulu Kelang area, Malaysia , 2013, Natural Hazards.

[43]  Yi Wang,et al.  Comparative study of landslide susceptibility mapping with different recurrent neural networks , 2020, Comput. Geosci..

[44]  Yi Li,et al.  Susceptibility assessment of earthquake-induced landslides using Bayesian network: A case study in Beichuan, China , 2012, Comput. Geosci..

[45]  Weihua Zhao,et al.  Field survey of a catastrophic high-speed long-runout landslide in Jichang Town, Shuicheng County, Guizhou, China, on July 23, 2019 , 2020, Landslides.

[46]  Jiansong Wu,et al.  Risk Assessment of Underground Subway Stations to Fire Disasters Using Bayesian Network , 2018, Sustainability.

[47]  M. Lazzari,et al.  Landslide Disasters Triggered by Extreme Rainfall Events: The Case of Montescaglioso (Basilicata, Southern Italy) , 2018, Geosciences.

[48]  Yu Huang,et al.  Review on landslide susceptibility mapping using support vector machines , 2018, CATENA.

[49]  Tonglin Zhang,et al.  A measure of spatial stratified heterogeneity , 2016 .

[50]  Jiquan Zhang,et al.  Applying a Series and Parallel Model and a Bayesian Networks Model to Produce Disaster Chain Susceptibility Maps in the Changbai Mountain area, China , 2019, Water.

[51]  A. Troncone,et al.  Analytical Method for Predicting the Mobility of Slow-Moving Landslides owing to Groundwater Fluctuations , 2011 .

[52]  U. Haque,et al.  Fatal landslides in Europe , 2016, Landslides.

[53]  Birgit Kleinschmit,et al.  Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan , 2017, Remote. Sens..

[54]  Xin Shen,et al.  Tree-Species Classification in Subtropical Forests Using Airborne Hyperspectral and LiDAR Data , 2017, Remote. Sens..