Abstract
This paper presents a novel machine learning approach backed by ensembling machine learning algorithms to build landslide susceptibility maps. The results reveal that this approach outperforms prior machine learning-based approaches in terms of precision, recall, and F-score for landslide susceptibility modeling. In this research, three ensemble machine learning algorithms were tested for their applicability in landslide prediction domain, namely, random forest, rotation forest, and XGBoost. A comparison between these ensemble models and the machine learning algorithms used in previous researches was also performed. In order to evaluate the model’s ability to generalize results, two different study areas were used in this study, which are Ratnapura district in Sri Lanka and Glenmalure in Ireland. Several landslide conditioning features including land use, landform, vegetation index, elevation, overburden, aspect, curvature, catchment area, drainage density, distance to water streams, soil, bedrock condition, lithology and rainfall prepared by surveying, remote sensing, and deriving from Digital Elevation Model (DEM) were utilized in building the spatial database. Importantly, this study introduces new landslide conditioning factors like overburden and water catchment areas which have good importance values. Further, research applies dynamic factors like rainfall and vegetation index for susceptibility map building, by making use of remote sensing data which is updated periodically. The study emphasizes the capability of ensemble approaches in generalizing results well for both study areas which inherit completely different environmental properties, and its ability to provide a scalable map building mechanism. Also, useful insights and guidelines are also provided for fellow researchers who are interested in building susceptibility maps using machine learning approaches.
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Authors are thankful to Director, National Building Research Organization—Sri Lanka (NBRO) for their support for providing required spatial maps for the study area.
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Bandara, A., Hettiarachchi, Y., Hettiarachchi, K., Munasinghe, S., Wijesinghe, I., Thayasivam, U. (2020). A Generalized Ensemble Machine Learning Approach for Landslide Susceptibility Modeling. In: Sharma, N., Chakrabarti, A., Balas, V. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1016. Springer, Singapore. https://doi.org/10.1007/978-981-13-9364-8_6
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