Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area

Abstract This research aims at investigating the capability of Keras’s deep learning models with three robust optimization algorithms (stochastic gradient descent, root mean square propagation, and adaptive moment optimization) and two-loss functions for spatial modeling of landslide hazard at a regional scale. Shallow landslides at the Ha Long area (Vietnam) were selected as a case study. For this regard, set of ten influencing factors (slope, aspect, curvature, topographic wetness index, landuse, distance to road, distance to river, soil type, distance to fault, and lithology) and 193 landslide polygons were prepared to construct a Geographic Information System (GIS) database for the study area. Using the collected database, the DNN with its potential of realizing complex functional mapping hidden in the data is used to generalize a decision boundary that separates the learning space into two distinct categories: landslide (a positive class) and non-landslide (a negative class). Experimental results point out that the utilized the Keras’s deep learning model with the Adam optimization and the mean squared error lost function is the best with the prediction performance of 84.0%. The performance is better than those of the employed benchmark approaches of random forest, J48 decision tree, classification tree, and logistic model tree. We conclude that the Keras’s deep learning model is a new tool for shallow susceptibility mapping at landslide-prone areas.

[1]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[2]  Wei Chen,et al.  GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China , 2015, Journal of Earth System Science.

[3]  Amir Mosavi,et al.  Deep Learning for Detecting Building Defects Using Convolutional Neural Networks , 2019, Sensors.

[4]  Wei Chen,et al.  A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment , 2018 .

[5]  M. McHugh.  Interrater reliability: the kappa statistic , 2012, Biochemia medica.

[6]  J. Grzybowski,et al.  Artificial neural network ensembles applied to the mapping of landslide susceptibility , 2020 .

[7]  Yang Yang,et al.  Instance-level recognition and quantification for concrete surface bughole based on deep learning , 2019, Automation in Construction.

[8]  Pijush Samui,et al.  A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. , 2019, The Science of the total environment.

[9]  Yu Wang,et al.  Remote Sensing Landslide Recognition Based on Convolutional Neural Network , 2019, Mathematical Problems in Engineering.

[10]  Eibe Frank,et al.  Logistic Model Trees , 2003, Machine Learning.

[11]  Mazlan Hashim,et al.  Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment , 2015, Scientific Reports.

[12]  François Chollet,et al.  Deep Learning with Python , 2017 .

[13]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[14]  Dieu Tien Bui,et al.  Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility , 2019, CATENA.

[15]  Seyed Amir Naghibi,et al.  A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China , 2018, Bulletin of Engineering Geology and the Environment.

[16]  Wei Chen,et al.  A comparative study of statistical index and certainty factor models in landslide susceptibility mapping: a case study for the Shangzhou District, Shaanxi Province, China , 2015, Arabian Journal of Geosciences.

[17]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[18]  Sepp Hochreiter,et al.  The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[19]  P. Pattynama,et al.  Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. , 1998, European journal of radiology.

[20]  Stuart W. D. Grieve,et al.  Colluvium supply in humid regions limits the frequency of storm-triggered landslides , 2016, Scientific Reports.

[21]  Himan Shahabi,et al.  Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. , 2019, The Science of the total environment.

[22]  Feng-Ping An,et al.  Pedestrian Re-Recognition Algorithm Based on Optimization Deep Learning-Sequence Memory Model , 2019, Complex..

[23]  Sandro Skansi,et al.  Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence , 2018 .

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

[25]  Nhat-Duc Hoang,et al.  A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides , 2018, Remote. Sens..

[26]  Hamid Reza Pourghasemi,et al.  Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China , 2018, Comput. Geosci..

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

[28]  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.

[29]  Biswajeet Pradhan,et al.  Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree , 2016, Landslides.

[30]  Simone Scardapane,et al.  Kafnets: kernel-based non-parametric activation functions for neural networks , 2017, Neural Networks.

[31]  K. Takara,et al.  The 28 July 2015 rapid landslide at Ha Long City, Quang Ninh, Vietnam , 2017, Landslides.

[32]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[33]  M. Ada,et al.  Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey , 2017, Natural Hazards.

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

[35]  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 .

[36]  Umi Kalthum Ngah,et al.  Modeling and Testing Landslide Hazard Using Decision Tree , 2014, J. Appl. Math..

[37]  Nguyen Quoc Thanh,et al.  Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization , 2017, Landslides.

[38]  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.

[39]  Ismail Colkesen,et al.  Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping , 2019, Geocarto International.

[40]  Poonam,et al.  Identification of landslide-prone zones in the geomorphically and climatically sensitive Mandakini valley, (central Himalaya), for disaster governance using the Weights of Evidence method , 2017 .

[41]  Lal Samarakoon,et al.  Landslide susceptibility mapping using logistic regression model (a case study in Badulla District, Sri Lanka) , 2018 .

[42]  A-Xing Zhu,et al.  A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods , 2019 .

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

[44]  A. Whittaker,et al.  Lithological controls on hillslope sediment supply: insights from landslide activity and grain size distributions , 2018 .

[45]  Konstantinos G. Nikolakopoulos,et al.  Developing a landslide susceptibility map based on remote sensing, fuzzy logic and expert knowledge of the Island of Lefkada, Greece , 2018, Environmental Earth Sciences.

[46]  Lei Zhang,et al.  Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal , 2019, Applied Energy.

[47]  Nhat-Duc Hoang,et al.  Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with a GIS-based random forest classifier , 2019, Bulletin of Engineering Geology and the Environment.

[48]  Mohammad Shukri Salman,et al.  Back-propagation algorithm with variable adaptive momentum , 2016, Knowl. Based Syst..

[49]  B. Pradhan,et al.  Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg–Marquardt and Bayesian regularized neural networks , 2012 .

[50]  P. Janvier,et al.  Devonian fish (Placodermi, Antiarcha) from Tra Ban Island (Bai Tu Long Bay, Quang Ninh Province, Vietnam) and the question of the age of the Dô Son Formation , 2003 .

[51]  Christos Chalkias,et al.  Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area , 2019, Bulletin of Engineering Geology and the Environment.

[52]  K. Sarkar,et al.  Landslide Susceptibility Mapping in Darjeeling Himalayas, India , 2018, Advances in Civil Engineering.

[53]  Tri Dev Acharya,et al.  Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China) , 2018 .

[54]  Meryem Elmoulat,et al.  Landslides susceptibility mapping using GIS and weights of evidence model in Tetouan-Ras-Mazari area (Northern Morocco) , 2018 .

[55]  Iman Nasiri Aghdam,et al.  A new hybrid model using Step-wise Weight Assessment Ratio Analysis (SWARA) technique and Adaptive Neuro-fuzzy Inference System (ANFIS) for regional landslide hazard assessment in Iran , 2015 .

[56]  Saro Lee,et al.  Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping , 2018, Remote. Sens..

[57]  David J. Hill,et al.  Short-Term Residential Load Forecasting Based on Resident Behaviour Learning , 2018, IEEE Transactions on Power Systems.

[58]  Biswajeet Pradhan,et al.  Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment , 2020, CATENA.

[59]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[60]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

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

[62]  Philipp Geyer,et al.  Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction , 2018, Adv. Eng. Informatics.

[63]  B. Pham,et al.  Bagging based Support Vector Machines for spatial prediction of landslides , 2018, Environmental Earth Sciences.

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

[65]  Yuanyuan Qiao,et al.  A Survey on Machine Learning-Based Mobile Big Data Analysis: Challenges and Applications , 2018, Wirel. Commun. Mob. Comput..