Universiti Teknologi Malaysia Institutional Repository

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

Shahabi, H. and Shirzadi, A. and Ghaderi, K. and Omidvar, E. and Al-Ansari, N. and Clague, J. J. and Geertsema, M. and Khosravi, K. and Amini, A. and Bahrami, S. and Rahmati, O. and Habibi, K. and Mohammadi, A. and Nguyen, H. and Melesse, A. M. and Ahmad, B. B. and Ahmad, A. M. (2020) 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. Remote Sensing, 12 (2). ISSN 2072-4292

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Official URL: https://dx.doi.org/10.3390/rs12020266

Abstract

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 spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging-Cubic-KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic-KNN model (AUC=0.660). We therefore recommend that the Bagging-Cubic-KNN model be more widely applied for the sustainable management of flood-prone areas.

Item Type:Article
Uncontrolled Keywords:haraz, Iran, flood
Subjects:G Geography. Anthropology. Recreation > G Geography (General) > G70.39-70.6 Remote sensing
Divisions:Built Environment
ID Code:86710
Deposited By: Narimah Nawil
Deposited On:30 Sep 2020 17:09
Last Modified:30 Sep 2020 17:09

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