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On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification
 
 
Article

Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping

1
Environmental Change Institute, School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX13QY, UK
2
Department of Urban and Regional Planning, Chittagong University of Engineering and Technology (CUET), Chittagong 4349, Bangladesh
3
Department of Civil Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong 4349, Bangladesh
4
Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh
5
Institute for Risk and Disaster Reduction (IRDR), University College London (UCL), Gower Street, London WC1E 6BT, UK
6
Department of Disaster Science and Management, Faculty of Earth and Environmental Sciences, University of Dhaka, Dhaka 1000, Bangladesh
7
Department of Geography and Environmental Studies, University of Chittagong, Chittagong 4331, Bangladesh
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(20), 3347; https://doi.org/10.3390/rs12203347
Received: 6 September 2020 / Revised: 28 September 2020 / Accepted: 12 October 2020 / Published: 14 October 2020
(This article belongs to the Special Issue Remote Sensing of Natural Hazards)
Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions. View Full-Text
Keywords: landslides; remote sensing; uncertainty; K-Nearest Neighbor; Multi-Layer Perceptron; Random Forest; Support Vector Machine landslides; remote sensing; uncertainty; K-Nearest Neighbor; Multi-Layer Perceptron; Random Forest; Support Vector Machine
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MDPI and ACS Style

Adnan, M.S.G.; Rahman, M.S.; Ahmed, N.; Ahmed, B.; Rabbi, M.F.; Rahman, R.M. Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping. Remote Sens. 2020, 12, 3347. https://doi.org/10.3390/rs12203347

AMA Style

Adnan MSG, Rahman MS, Ahmed N, Ahmed B, Rabbi MF, Rahman RM. Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping. Remote Sensing. 2020; 12(20):3347. https://doi.org/10.3390/rs12203347

Chicago/Turabian Style

Adnan, Mohammed Sarfaraz Gani, Md Salman Rahman, Nahian Ahmed, Bayes Ahmed, Md. Fazleh Rabbi, and Rashedur M. Rahman. 2020. "Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping" Remote Sensing 12, no. 20: 3347. https://doi.org/10.3390/rs12203347

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