Abstract
The main aim of this study is to propose a new ensemble framework model for the prediction of snow avalanche prone regions. The proposed approach is based on the following pillars: Support vector machine classifier (SVM) and recent state-of-the-art ensemble learning techniques, Bagging and MultiBoost. On the basis of current literature, it has been observed that such techniques have been rarely used in this field. The study was conducted on the surrounding region of Gomukh, Uttarakhand, India. Several parameters were computed on which the base classifier was trained, viz., slope, aspect, surface curvature, precipitation, surface temperature, and snowfall. It was observed that the proposed ensemble frameworks outperformed with the current state-of-the-art SVM classifier. The highest classification accuracy was observed by the MultiBoost ensemble framework (92.61%), followed by Bagging (88.93%), while the lowest classification accuracy of (78.98%) was produced by artificial neural network (ANN) classifier. Accuracy assessment was performed and the proposed models were evaluated using receiver operating characteristics (ROC) curves and statistical measures.
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Nijhawan, R., Das, J. (2020). A Proposed Framework Approach for Mapping Glacier Hazard Zones. In: Ghosh, J., da Silva, I. (eds) Applications of Geomatics in Civil Engineering. Lecture Notes in Civil Engineering , vol 33. Springer, Singapore. https://doi.org/10.1007/978-981-13-7067-0_44
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DOI: https://doi.org/10.1007/978-981-13-7067-0_44
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