Landslide susceptibility mapping using Forest by Penalizing Attributes (FPA) algorithm based machine learning approach
Keywords:Landslide susceptibility mapping, machine learning, AUC, ROC, GIS, Vietnam
Landslide susceptibility mapping is a helpful tool for assessment and management of landslides of an area. In this study, we have applied first time Forest by Penalizing Attributes (FPA) algorithm-based Machine Learning (ML) approach for mapping of landslide susceptibility at Muong Lay district (Vietnam). For this aim, 217 historical landslides locations were identified and analyzed for the development of FPA model and generation of susceptibility map. Nine landslide topographical and geo-environmental conditioning factors (curvature, geology/lithology, aspect, distance from faults, rivers and roads, weathering crust, slope, and deep division) were utilized to construct the training and validating datasets for landslide modeling. Different quantitative statistical indices including Area Under the Receiver Operating Characteristic (ROC) curve (AUC) were used to evaluate the performance of the model. The results indicate that the predictive capability of the FPA is very good for landslide susceptibility mapping on both training (AUC = 0.935) and validating (AUC = 0.882) datasets. Thus, the novel FPA based ML model can be utilized for the development of accurate landslide susceptibility map of the study area and this approach can also be applied in other landslide prone areas.
Achour Y., Pourghasemi H.R., 2019. How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geoscience Frontiers.
Adnan M.N., Islam M.Z., 2017. Forest PA: Constructing a decision forest by penalizing attributes used in previous trees. Expert Systems with Applications, 89, 389–403.
Aghdam I.N., Varzandeh M.H.M., Pradhan B., 2016. Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environmental Earth Sciences, 75(7), 553.
Chang K.-T., Merghadi A., Yunus A.P., Pham B.T., Dou J., 2019. Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques. Scientific reports, 9(1), 1–21.
Dao D.V., Ly H.-B., Vu H.-L.T., Le T.-T., Pham B.T., 2020. Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete. Materials, 13(5), 1072.
Dou J., Yunus A.P., Merghadi A., Shirzadi A., Nguyen H., Hussain Y., Avtar R., Chen Y., Pham B.T., Yamagishi H., 2020. Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. Science of The Total Environment, 720, 137320.
Ghasemain B., Asl D.T., Pham B.T., Avand M., Nguyen H.D., Janizadeh S., 2020. Shallow landslide susceptibility mapping: A comparison between classification and regression tree and reduced error pruning tree algorithms. Vietnam Journal of Earth Sciences.
Harmouzi H., Nefeslioglu H.A., Rouai M., Sezer E.A., Dekayir A., Gokceoglu C., 2019. Landslide susceptibility mapping of the Mediterranean coastal zone of Morocco between Oued Laou and El Jebha using artificial neural networks (ANN). Arabian Journal of Geosciences, 12(22), 696.
Hong H., Liu J., Zhu A.-X., 2020. Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble. Science of the total environment, 718, 137231.
Hu Q., Zhou Y., Wang S., Wang F., 2020. Machine learning and fractal theory models for landslide susceptibility mapping: Case study from the Jinsha River Basin. Geomorphology, 351, 106975.
Nguyen P.T., Ha D.H., Jaafari A., Nguyen H.D., Van Phong T., Al-Ansari N., Prakash I., Le H.V., Pham B.T., 2020. Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The Dak Nong Province Case-study, Vietnam. International Journal of Environmental Research and Public Health, 17(7), 2473.
Nguyen V.-T., Tran T.H., Ha N.A., Ngo V.L., Nadhir A.-A., Tran V.P., Duy Nguyen H., Malek M.A., Amini A., Prakash I., 2019. GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam. Sustainability, 11(24), 7118.
Nhu V.-H., Shirzadi A., Shahabi H., Chen W., Clague J.J., Geertsema M., Jaafari A., Avand M., Miraki S., Asl D.T., 2020a. Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and its Ensembles in a Semi-Arid Region of Iran. Forests, 11(4), 421.
Nhu V.-H., Shirzadi A., Shahabi H., Singh S.K., Al-Ansari N., Clague J.J., Jaafari A., Chen W., Miraki S., Dou J., 2020b. Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms. International Journal of Environmental Research and Public Health, 17(8), 2749.
Nohani E., Moharrami M., Sharafi S., Khosravi K., Pradhan B., Pham B.T., Lee S., M. Melesse A., 2019. Landslide susceptibility mapping using different GIS-based bivariate models. Water, 11(7), 1402.
Pham B.T., Bui D.T., Dholakia M., Prakash I., Pham H.V., 2016. A comparative study of least square support vector machines and multiclass alternating decision trees for spatial prediction of rainfall-induced landslides in a tropical cyclones area. Geotechnical and Geological Engineering, 34(6), 1807–1824.
Phong T.V., Phan T.T., Prakash I., Singh S.K., Shirzadi A., Chapi K., Ly H.-B., Ho L.S., Quoc N.K., Pham B.T., 2019. Landslide susceptibility modeling using different artificial intelligence methods: A case study at Muong Lay district, Vietnam. Geocarto International, 1–24.
Pourghasemi H.R., Kornejady A., Kerle N., Shabani F., 2020. Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping. Catena, 187, 104364.
Samat A., Liu S., Persello C., Li E., Miao Z., Abuduwaili J., 2019. Evaluation of ForestPA for VHR RS image classification using spectral and superpixel-guided morphological profiles. European journal of remote sensing, 52(1), 107–121.
Shirzadi A., Saro L., Joo O.H., Chapi K., 2012. A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran. Natural hazards, 64(2), 1639–656.
Shirzadi A., Shahabi H., Chapi K., Bui D.T., Pham B.T., Shahedi K., Ahmad B.B., 2017. A comparative study between popular statistical and machine learning methods for simulating volume of landslides. Catena, 157, 213–226.
Van Dao D., Jaafari A., Bayat M., Mafi-Gholami D., Qi C., Moayedi H., Van Phong T., Ly H.-B., Le T.-T., Trinh P.T., 2020. A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. Catena, 188, 104451.
Zhang G., Cai Y., Zheng Z., Zhen J., Liu Y., Huang K., 2016. Integration of the statistical index method and the analytic hierarchy process technique for the assessment of landslide susceptibility in Huizhou, China. Catena, 142, 233–244.
Zhong C., Liu Y., Gao P., Chen W., Li H., Hou Y., Nuremanguli T., Ma H., 2020. Landslide mapping with remote sensing: challenges and opportunities. International Journal of Remote Sensing, 41(4), 1555–1581.
Zhou C., Yin K., Cao Y., Ahmed B., Li Y., Catani F., Pourghasemi H.R., 2018. Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China. Computers & Geosciences, 112, 23–37.