Abstract
Implementing the various machine learning algorithms for landslide susceptibility mapping has been researched by number of authors and is worth considering the issue. In the present study, the effectiveness of decision tree and its bagging and boosting-based ensemble model techniques (like random forest, extra tree, rotation forest, XGBoost, and AdaBoost) has been evaluated via generating the landslide susceptibility map (LSM). Both threshold-based, i.e., overall accuracy, and rank-based, i.e., area under receiver operating characteristics (AUROC), measures have been used as the criteria for evaluating the various model’s performances. The result concluded that the XGBoost model has outperformed the other implemented algorithms after performing hyper-parameters tuning for each algorithm. The study area considered for the present study is Rishikesh to Gangotri axis with a buffer area of 3 km on each side. It is the first time that these algorithms have been implemented and compared for this study area.
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Saxena, V., Singh, U., Sinha, L.K. (2023). Landslide Susceptibility Mapping Using J48 Decision Tree and Its Ensemble Methods for Rishikesh to Gangotri Axis. In: Sharma, N., Goje, A., Chakrabarti, A., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. ICDMAI 2023. Lecture Notes in Networks and Systems, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-99-1414-2_13
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