An Assessment of Data Mining and Bivariate Statistical Methods for Landslide Susceptibility Mapping

Landslides are recognized as one of the environmental challenges that lead to land degradation, reduce fertility, and cause significant damage to the ecosystem. Therefore, proper identification of landslide-prone areas through modeling will be significantly helpful for land development managers and planners by providing them with appropriate management strategies to prevent land degradation. In this research, landslide susceptibility mapping was carried out for West Azerbaijan province in Iran using Frequency Ratio (FR), Shannon Entropy (SE), Random Forest (RF), and an ensemble of random forest and bagging (RF-BA) methods. Based on field surveys, local interviews, and review of similar studies, 12 factors influencing landslide occurrence, namely altitude, slope angle, slope aspect, distance from fault, distance from river, distance from road, drainage density, road density, rainfall, soil, land use, and lithology, were identified. In the field surveys, 110 landslides in the area were specified; 70% of the data (77 landslides) were randomly selected and utilized for modeling and the remaining 30% (33 landslides) for validation. The results of the ROC curve showed the accuracy of 0.92, 0.91, 0.89, and 0.88 with the RF-BA, RF, FR, and SE models, respectively.

[1]  Huanan Yu,et al.  Time-Domain Analysis of Tamper Displacement during Dynamic Compaction Based on Automatic Control , 2021, Coatings.

[2]  Jenq-Neng Hwang,et al.  GMNet: Graded-Feature Multilabel-Learning Network for RGB-Thermal Urban Scene Semantic Segmentation , 2021, IEEE Transactions on Image Processing.

[3]  Ke Zhang,et al.  A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm , 2021, Remote. Sens..

[4]  Zenggang Xiong,et al.  Research on AI security enhanced encryption algorithm of autonomous IoT systems , 2021, Inf. Sci..

[5]  Jian Xu,et al.  Study on Strength Behavior of Basalt Fiber-Reinforced Loess by Digital Image Technology (DIT) and Scanning Electron Microscope (SEM) , 2021, Arabian Journal for Science and Engineering.

[6]  S. Masri,et al.  An empirical time‐domain trend line‐based bridge signal decomposing algorithm using Savitzky–Golay filter , 2021, Structural Control and Health Monitoring.

[7]  Yanli Wu,et al.  Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping , 2020 .

[8]  A-Xing Zhu,et al.  Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble. , 2020, The Science of the total environment.

[9]  Nhat-Duc Hoang,et al.  Spatial prediction of shallow landslide using Bat algorithm optimized machine learning approach: A case study in Lang Son Province, Vietnam , 2019, Adv. Eng. Informatics.

[10]  Yuhao Wang,et al.  A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction , 2019, Landslides.

[11]  Yi Wang,et al.  Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) , 2019, Applied Sciences.

[12]  Subodh Dhakal,et al.  Landslide susceptibility mapping using Weight of Evidence Method in Haku, Rasuwa District, Nepal , 2019, Journal of Nepal Geological Society.

[13]  Ke Zhang,et al.  Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China , 2019, Water Science and Engineering.

[14]  Biswajeet Pradhan,et al.  Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm , 2019, Remote. Sens..

[15]  Biswajeet Pradhan,et al.  Flood Susceptibility Mapping Using GIS-Based Analytic Network Process: A Case Study of Perlis, Malaysia , 2019, Water.

[16]  Shouyun Liang,et al.  Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China , 2019 .

[17]  D. Bui,et al.  Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling , 2019, Forests.

[18]  Xiaojing Wang,et al.  Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression , 2018, Applied Sciences.

[19]  Biswajeet Pradhan,et al.  Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping , 2018, Sensors.

[20]  Vijendra Kumar Pandey,et al.  Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya , 2018, Geocarto International.

[21]  Saro Lee,et al.  Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea , 2018 .

[22]  A-Xing Zhu,et al.  Landslide susceptibility assessment in the Anfu County, China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND) , 2018, Earth Science Informatics.

[23]  Hongen Li,et al.  Antecedent rainfall induced shallow landslide-A case study of Yunnan landslide, China , 2017 .

[24]  Khabat Khosravi,et al.  Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India , 2017, Environmental Processes.

[25]  Iman Nasiri Aghdam,et al.  Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran) , 2016, Environmental Earth Sciences.

[26]  Claudia Meisina,et al.  Site-specific to local-scale shallow landslides triggering zones assessment using TRIGRS , 2015 .

[27]  Paraskevas Tsangaratos,et al.  Estimating landslide susceptibility through a artificial neural network classifier , 2014, Natural Hazards.

[28]  M. Sarfaraz,et al.  Lattice Boltzmann Method for Simulating Impulsive Water Waves Generated by Landslides , 2014 .

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

[30]  Soyoung Park,et al.  Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea , 2013, Environmental Earth Sciences.

[31]  Haijun Wang,et al.  Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China , 2012 .

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

[33]  Isik Yilmaz,et al.  Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat - Turkey) , 2009, Comput. Geosci..

[34]  Saro Lee,et al.  Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models , 2006 .

[35]  Saro Lee,et al.  Landslide susceptibility mapping using GIS and the weight-of-evidence model , 2004, Int. J. Geogr. Inf. Sci..

[36]  M. Stocking,et al.  A Handbook for the Field Assessment of Land Degradation , 2001 .

[37]  Zenggang Xiong,et al.  A privacy-preserving aggregation scheme based on negative survey for vehicle fuel consumption data , 2021, Inf. Sci..

[38]  Lintianran Weng,et al.  Deep cascading network architecture for robust automatic modulation classification , 2021, Neurocomputing.

[39]  Jingyu Yang,et al.  Sign Language/Gesture Recognition Based on Cumulative Distribution Density Features Using UWB Radar , 2021, IEEE Transactions on Instrumentation and Measurement.

[40]  Sharafi,et al.  Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models , 2019, Water.

[41]  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.

[42]  Robert S. Chen,et al.  Natural Disaster Hotspots: A Global Risk Analysis , 2005 .