Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier

• Main Authors: Shahabi, H., Shirzadi, A., Ghaderi, K., Omidvar, E., Al-Ansari, N., Clague, J. J., Geertsema, M., Khosravi, K., Amini, A., Bahrami, S., Rahmati, O., Habibi, K., Mohammadi, A., Nguyen, H., Melesse, A. M., Ahmad, B. B., Ahmad, A. M.
• Format: Article
• Language: English
• Published: MDPI AG 2020
• Subjects: G70.39-70.6 Remote sensing
• Online Access: http://eprints.utm.my/id/eprint/86710/1/HimanShahabi2020_FloodDetectionandSusceptibilityMappingusingSentinelRemoteSensing.pdf; http://eprints.utm.my/id/eprint/86710/; https://dx.doi.org/10.3390/rs12020266
• Institution: Universiti Teknologi Malaysia

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to s...