An extreme rainfall-induced landslide susceptibility assessment using autoencoder combined with random forest in Shimane Prefecture, Japan

Background Landslide-affecting factors are uncorrelated or non-linearly correlated, limiting the predictive performance of traditional machine learning methods for landslide susceptibility assessment. Deep learning methods can take advantage of the high-level representation and reconstruction of information from landslide-affecting factors. In this paper, a novel deep learning-based algorithm that combine classifiers of both deep learning and machine learning is proposed for landslide susceptibility assessment. A stacked autoencoder (StAE) and a sparse autoencoder (SpAE) both consist of an input layer for raw data, hidden layer for feature extraction, and output layer for classification and prediction. As a study case, Oda City and Gotsu City in Shimane Prefecture, southwestern Japan, were used for susceptibility assessment and prediction of landslides triggered by extreme rainfall. Results The prediction performance was compared by analyzing real landslide and non-landslide data. The prediction performance of random forest (RF) was evaluated as better than that of a support vector machine (SVM) in traditional machine learning, so RF was combined with both StAE and SpAE. The results show that the prediction ratio of the combined classifiers was 93.2% for StAE combined with RF model and 92.5% for SpAE combined with RF model, which were higher than those of the SVM (87.4%), RF (89.7%), StAE (84.2%), and SpAE (88.2%). Conclusions This study provides an example of combined classifiers giving a better predictive ratio than a single classifier. The asymmetric and unsupervised autoencoder combined with RF can exploit optimal non-linear features from landslide-affecting factors successfully, outperforms some conventional machine learning methods, and is promising for landslide susceptibility assessment.

[1]  D. Montgomery,et al.  Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China , 2015 .

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

[3]  Diofantos G. Hadjimitsis,et al.  Integrated use of GIS and remote sensing for monitoring landslides in transportation pavements: the case study of Paphos area in Cyprus , 2014, Natural Hazards.

[4]  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 .

[5]  B. Faybishenko,et al.  Permeability variations within mining-induced fractured rock mass and its influence on groundwater inrush , 2016, Environmental Earth Sciences.

[6]  Fotis Foukalas,et al.  Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems , 2018, Sensors.

[7]  Lu Huang,et al.  Method for Meteorological Early Warning of Precipitation-Induced Landslides Based on Deep Neural Network , 2018, Neural Processing Letters.

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

[9]  Tamer Topal,et al.  GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey) , 2012, Environmental Earth Sciences.

[10]  Francisco Charte,et al.  A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines , 2018, Inf. Fusion.

[11]  Veronica Tofani,et al.  Combination of Rainfall Thresholds and Susceptibility Maps for Dynamic Landslide Hazard Assessment at Regional Scale , 2018, Front. Earth Sci..

[12]  Saro Lee,et al.  Probabilistic landslide susceptibility and factor effect analysis , 2005 .

[13]  Zenghui Sun,et al.  Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models , 2019, Applied Sciences.

[14]  Babajide O Ayinde,et al.  Regularizing Deep Neural Networks by Enhancing Diversity in Feature Extraction , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[15]  Jui-Yi Ho,et al.  Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression , 2017 .

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

[17]  Zohre Sadat Pourtaghi,et al.  Landslide susceptibility assessment in Lianhua County (China); a comparison between a random forest data mining technique and bivariate and multivariate statistical models , 2016 .

[18]  Yang Hong,et al.  Use of satellite remote sensing data in the mapping of global landslide susceptibility , 2007 .

[19]  José Carlos Príncipe,et al.  Understanding Autoencoders with Information Theoretic Concepts , 2018, Neural Networks.

[20]  Manfred F. Buchroithner,et al.  A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses , 2010, Comput. Environ. Urban Syst..

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

[22]  Alexei A. Efros,et al.  Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Domenico Calcaterra,et al.  Landslide detection integrated system (LaDIS) based on in-situ and satellite SAR interferometry measurements , 2016 .

[24]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[25]  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.

[26]  Ryuichi Yatabe,et al.  Effect of Landslide Factor Combinations on the Prediction Accuracy of Landslide Susceptibility Maps in the Blue Nile Gorge of Central Ethiopia , 2015, Geoenvironmental Disasters.

[27]  L. Ayalew,et al.  The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan , 2005 .

[28]  Saro Lee,et al.  Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping , 2012, Comput. Geosci..

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

[30]  R. Bai,et al.  Fractal dimension analysis of higher-order mode shapes for damage identification of beam structures , 2012 .

[31]  Cristiano Ballabio,et al.  Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy , 2012, Mathematical Geosciences.

[32]  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.

[33]  Biswajeet Pradhan,et al.  A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison , 2016 .

[34]  Se-Yeong Hamm,et al.  Performance Evaluation of the GIS-Based Data-Mining Techniques Decision Tree, Random Forest, and Rotation Forest for Landslide Susceptibility Modeling , 2019, Sustainability.

[35]  Zhigang Zeng,et al.  Landslide Deformation Prediction Based on Recurrent Neural Network , 2013, Neural Processing Letters.

[36]  Nicola Casagli,et al.  Remote sensing as tool for development of landslide databases: The case of the Messina Province (Italy) geodatabase , 2015 .

[37]  W. Z. Savage,et al.  Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning , 2008 .

[38]  Sunil Saha,et al.  Landslide susceptibility mapping using knowledge driven statistical models in Darjeeling District, West Bengal, India , 2019, Geoenvironmental Disasters.

[39]  Luigi Borrelli,et al.  Shallow landslide susceptibility assessment in granitic rocks using GIS-based statistical methods: the contribution of the weathering grade map , 2018, Landslides.

[40]  R. Soeters,et al.  Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment , 2003 .

[41]  Wei Chen,et al.  A hybrid fuzzy weight of evidence method in landslide susceptibility analysis on the Wuyuan area, China , 2017 .

[42]  M. Becht,et al.  Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows , 2014 .

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

[44]  Soyoung Park,et al.  Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance , 2019, Applied Sciences.

[45]  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.

[46]  H. A. Nefeslioglu,et al.  Modification of seed cell sampling strategy for landslide susceptibility mapping: an application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey) , 2016, Bulletin of Engineering Geology and the Environment.

[47]  Gongzhuang Peng,et al.  Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway , 2018, Sensors.

[48]  A. Bobrov,et al.  Testate amoebae communities from some freshwater and soil habitats in China (Hubei and Shandong Provinces) , 2012, Frontiers of Earth Science.

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

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

[51]  B. Pradhan,et al.  Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models , 2012 .

[52]  Mengliang Yu,et al.  Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors , 2019, PloS one.

[53]  K. Yin,et al.  Time series analysis and long short-term memory neural network to predict landslide displacement , 2019, Landslides.

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

[55]  Young-Kwang Yeon,et al.  Landslide susceptibility mapping in Injae, Korea, using a decision tree , 2010 .

[56]  O. Igwe.  The geotechnical characteristics of landslides on the sedimentary and metamorphic terrains of South-East Nigeria, West Africa , 2015, Geoenvironmental Disasters.

[57]  T. Kavzoglu,et al.  Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression , 2016 .

[58]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[59]  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.

[60]  Chang-Jo Chung,et al.  Mapping landslide susceptibility from small datasets: A case study in the Pays de Herve (E Belgium) , 2007 .

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

[62]  Tetsuya Kubota,et al.  Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia , 2018, Geomorphology.

[63]  Three-dimensional seismic slope stability assessment with the application of Scoops3D and GIS: a case study in Atsuma, Hokkaido , 2019, Geoenvironmental Disasters.

[64]  H. Saito,et al.  Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: The Akaishi Mountains, Japan , 2009 .

[65]  Alexander Brenning,et al.  Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling , 2015, Comput. Geosci..

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

[67]  Antonio Francipane,et al.  Effect of raster resolution and polygon-conversion algorithm on landslide susceptibility mapping , 2016, Environ. Model. Softw..

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

[69]  Majid Shadman Roodposhti,et al.  Landslide susceptibility mapping using geographically-weighted principal component analysis , 2014 .