Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway

The China-Nepal Highway is a vital land route in the Kush-Himalayan region. The occurrence of mountain hazards in this area is a matter of serious concern. Thus, it is of great importance to perform hazard assessments in a more accurate and real-time way. Based on temporal and spatial sensor data, this study tries to use data-driven algorithms to predict landslide susceptibility. Ten landslide instability factors were prepared, including elevation, slope angle, slope aspect, plan curvature, vegetation index, built-up index, stream power, lithology, precipitation intensity, and cumulative precipitation index. Four machine learning algorithms, namely decision tree (DT), support vector machines (SVM), Back Propagation neural network (BPNN), and Long Short Term Memory (LSTM) are implemented, and their final prediction accuracies are compared. The experimental results showed that the prediction accuracies of BPNN, SVM, DT, and LSTM in the test areas are 62.0%, 72.9%, 60.4%, and 81.2%, respectively. LSTM outperformed the other three models due to its capability to learn time series with long temporal dependencies. It indicates that the dynamic change course of geological and geographic parameters is an important indicator in reflecting landslide susceptibility.

[1]  Yunpeng Wang,et al.  Long short-term memory neural network for traffic speed prediction using remote microwave sensor data , 2015 .

[2]  Cem Kincal,et al.  Using advanced InSAR time series techniques to monitor landslide movements in Badong of the Three Gorges region, China , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[3]  Helmi Zulhaidi Mohd Shafri,et al.  Optimized Neural Architecture for Automatic Landslide Detection from High‐Resolution Airborne Laser Scanning Data , 2017 .

[4]  Fangling Pu,et al.  Early Warning of Abrupt Displacement Change at the Yemaomian Landslide of the Three Gorge Region, China , 2015 .

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

[6]  Paraskevas Tsangaratos,et al.  Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size , 2016 .

[7]  T. Blaschke,et al.  A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping , 2018, Natural Hazards.

[8]  Saro Lee,et al.  A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea , 2017 .

[9]  A. Shakoor,et al.  A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses , 2010 .

[10]  Abbas Alimohammadi,et al.  A GIS-based neuro-fuzzy procedure for integrating knowledge and data in landslide susceptibility mapping , 2010, Comput. Geosci..

[11]  Christos Chalkias,et al.  GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method , 2014, ISPRS Int. J. Geo Inf..

[12]  S. Bai,et al.  GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China , 2010 .

[13]  Yanchun Liang,et al.  Hybrid intelligent algorithm and its application in geological hazard risk assessment , 2015, Neurocomputing.

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

[15]  Bahareh Kalantar,et al.  Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) , 2018 .

[16]  M. Marjanović,et al.  Landslide susceptibility assessment using SVM machine learning algorithm , 2011 .

[17]  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..

[18]  Biswajeet Pradhan,et al.  Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia , 2011, Expert Syst. Appl..

[19]  A-Xing Zhu,et al.  Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. , 2018, The Science of the total environment.

[20]  Yifei Zhang,et al.  Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery , 2018, Sensors.

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

[22]  Dieu Tien Bui,et al.  Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS , 2017 .

[23]  Changjiang Li,et al.  An effective antecedent precipitation model derived from the power-law relationship between landslide occurrence and rainfall level , 2014 .

[24]  David J. Hill,et al.  Intelligent Time-Adaptive Transient Stability Assessment System , 2016, IEEE Transactions on Power Systems.

[25]  Biswajeet Pradhan,et al.  Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (FR and WOE) and adaptive neuro-fuzzy inference system (ANFIS) at southern Zagros Mountains in Iran , 2017, Environmental Earth Sciences.

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

[27]  H. Pourghasemi,et al.  Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran , 2016, Environmental Earth Sciences.

[28]  D. Rozos,et al.  Physical and Anthropogenic Factors Related to Landslide Activity in the Northern Peloponnese, Greece , 2018, Land.

[29]  H. Pourghasemi,et al.  Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. , 2018, The Science of the total environment.

[30]  J. Nichol,et al.  Application of high-resolution stereo satellite images to detailed landslide hazard assessment , 2006 .

[31]  Biswajeet Pradhan,et al.  A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS , 2013, Comput. Geosci..

[32]  Hamid Reza Pourghasemi,et al.  Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms , 2018, Sustainability.

[33]  Wei Li,et al.  The power–law relationship between landslide occurrence and rainfall level , 2011 .