Predicting landslide susceptibility based on decision tree machine learning models under climate and land use changes

[1]  A. Arabameri,et al.  Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India , 2020, Environmental Earth Sciences.

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

[3]  Biswajeet Pradhan,et al.  Assessing Soil Erosion Hazards Using Land-Use Change and Landslide Frequency Ratio Method: A Case Study of Sabaragamuwa Province, Sri Lanka , 2020, Remote. Sens..

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

[5]  Robert A. Schowengerdt,et al.  A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification , 1995, IEEE Trans. Geosci. Remote. Sens..

[6]  Asish Saha,et al.  Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms , 2020, Remote. Sens..

[7]  John P. Weyant,et al.  A special issue on the RCPs , 2011 .

[8]  A. Trigila,et al.  Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy) , 2015 .

[9]  Mihai Niculiță Landslide Hazard Induced by Climate Changes in North-Eastern Romania , 2020 .

[10]  Neil Dixon,et al.  Impact of predicted climate change on landslide reactivation: case study of Mam Tor, UK , 2007 .

[11]  Osman Orhan,et al.  Assessing and mapping landslide susceptibility using different machine learning methods , 2020, Geocarto International.

[12]  Keith C. Clarke,et al.  A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area , 1997 .

[13]  J. Sung,et al.  Non-stationary Frequency Analysis for Extreme Precipitation based on Representative Concentration Pathways (RCP) Climate Change Scenarios , 2012 .

[14]  Wei Chen,et al.  Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques , 2017 .

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

[16]  T. Glade,et al.  Assessing the spatiotemporal impact of climate change on event rainfall characteristics influencing landslide occurrences based on multiple GCM projections in China , 2020, Climatic Change.

[17]  M. Long,et al.  Irish peat slides 2006–2010 , 2011 .

[18]  S. L. Gariano,et al.  Landslides in a changing climate , 2016 .

[19]  Fausto Marincioni,et al.  Impact of climate change on landslides frequency: the Esino river basin case study (Central Italy) , 2018, Natural Hazards.

[20]  O. Orhan,et al.  An application on sinkhole susceptibility mapping by integrating remote sensing and geographic information systems , 2020, Arabian Journal of Geosciences.

[21]  J. Remondo,et al.  Analysis of geomorphic systems' response to natural and human drivers in northern Spain: Implications for global geomorphic change , 2013 .

[22]  Diego J. Chagas,et al.  Assessment of Climate Change over South America under RCP 4.5 and 8.5 Downscaling Scenarios , 2014 .

[23]  D. Lettenmaier,et al.  Changes in observed climate extremes in global urban areas , 2015 .

[24]  Christian Huggel,et al.  Introducing Linkages Between Climate Change, Extreme Events, and Disaster Risk Reduction , 2018 .

[25]  G. De’ath Boosted trees for ecological modeling and prediction. , 2007, Ecology.

[26]  B. Pradhan,et al.  A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility , 2017 .

[27]  Diogo J. Amore,et al.  The influence of land use/land cover variability and rainfall intensity in triggering landslides: a back-analysis study via physically based models , 2020, Natural Hazards.

[28]  Saro Lee,et al.  Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility , 2020, Remote. Sens..

[29]  S. L. Gariano,et al.  Assessing future changes in the occurrence of rainfall-induced landslides at a regional scale. , 2017, The Science of the total environment.

[30]  Sadhan Malik,et al.  Potential Landslide Vulnerability Zonation Using Integrated Analytic Hierarchy Process and GIS Technique of Upper Rangit Catchment Area, West Sikkim, India , 2019, Journal of the Indian Society of Remote Sensing.

[31]  B. Pradhan,et al.  The use of RUSLE and GCMs to predict potential soil erosion associated with climate change in a monsoon-dominated region of eastern India , 2020, Arabian Journal of Geosciences.

[32]  J. Wickham,et al.  Land cover and land use change , 2018 .

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

[34]  Sadhan Malik,et al.  Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya , 2021, Natural Hazards.

[35]  Subodh Chandra Pal,et al.  Simulating the impact of climate change on soil erosion in sub-tropical monsoon dominated watershed based on RUSLE, SCS runoff and MIROC5 climatic model , 2019, Advances in Space Research.

[36]  D. P. Shrestha,et al.  The influence of land use and land cover change on landslide susceptibility: a case study in Zhushan Town, Xuan'en County (Hubei, China) , 2019, Natural Hazards and Earth System Sciences.

[37]  D. Despain Vegetation of the Big Horn Mountains, Wyoming, in relation to substrate and climate , 1973 .

[38]  Wei Chen,et al.  Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. , 2018, The Science of the total environment.

[39]  Chao Gao,et al.  Impacts of climate change on characteristics of daily‐scale rainfall events based on nine selected GCMs under four CMIP5 RCP scenarios in Qu River basin, east China , 2019, International Journal of Climatology.

[40]  A. Erener,et al.  A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM) , 2016 .

[41]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[42]  Alok Choudhary,et al.  Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees , 2019, Molecular informatics.

[43]  Asish Saha,et al.  Climate and land use change induced future flood susceptibility assessment in a sub-tropical region of India , 2021, Soft Computing.

[44]  Sadhan Malik,et al.  Trend of extreme rainfall events using suitable Global Circulation Model to combat the water logging condition in Kolkata Metropolitan Area , 2020, Urban Climate.

[45]  Thomas Blaschke,et al.  Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms , 2021, Water.

[46]  S. Pal,et al.  GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India , 2019, SN Applied Sciences.

[47]  Le Wang,et al.  Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach , 2006 .

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

[49]  S. Mandal,et al.  Modeling and mapping landslide susceptibility zones using GIS based multivariate binary logistic regression (LR) model in the Rorachu river basin of eastern Sikkim Himalaya, India , 2018, Modeling Earth Systems and Environment.

[50]  D. Varnes Landslide hazard zonation: A review of principles and practice , 1984 .

[51]  T. Houet,et al.  Modelling landslide hazard under global change: the case of a Pyrenean valley , 2020 .

[52]  Mohammad M. Ghiasi,et al.  Modeling of gas hydrate phase equilibria: Extremely randomized trees and LSSVM approaches , 2017 .

[53]  Thomas Blaschke,et al.  Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility , 2020, Remote. Sens..

[54]  Giovanni Maria Farinella,et al.  Semantic segmentation of images exploiting DCT based features and random forest , 2016, Pattern Recognit..

[55]  Anna Roccati,et al.  GIS-Based Landslide Susceptibility Mapping for Land Use Planning and Risk Assessment , 2021, Land.

[56]  Taskin Kavzoglu,et al.  Machine Learning Techniques in Landslide Susceptibility Mapping: A Survey and a Case Study , 2018, Landslides: Theory, Practice and Modelling.

[57]  Pece V Gorsevski,et al.  Integrating multi-criteria evaluation techniques with geographic information systems for landfill site selection: a case study using ordered weighted average. , 2012, Waste management.

[58]  Alireza Gharagozlou,et al.  Predicting Urban Land Use Changes Using a CA–Markov Model , 2014 .

[59]  P. Reichenbach,et al.  The Influence of Land Use Change on Landslide Susceptibility Zonation: The Briga Catchment Test Site (Messina, Italy) , 2013, Environmental Management.

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

[61]  Subodh Chandra Pal,et al.  Assessing the Importance of Static and Dynamic Causative Factors on Erosion Potentiality Using SWAT, EBF with Uncertainty and Plausibility, Logistic Regression and Novel Ensemble Model in a Sub-tropical Environment , 2020, Journal of the Indian Society of Remote Sensing.

[62]  S. Pal,et al.  Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India , 2020 .

[63]  Asish Saha,et al.  Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility , 2020, Sensors.

[64]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[65]  Mustafa Neamah Jebur,et al.  Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia , 2014 .

[66]  Romulus Costache,et al.  Rainfall induced landslide susceptibility mapping using novel hybrid soft computing methods based on multi-layer perceptron neural network classifier , 2020, Geocarto International.

[67]  Sadhan Malik,et al.  Threats of climate and land use change on future flood susceptibility , 2020 .