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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
Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran. Board Member of Department of Zrebar Lake Environmental Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj, Iran.
Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran.
Department of Information Technology and Computer Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran.
Department of Rangeland and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran.
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2020 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 12, no 2, p. 1-30, article id 266Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Switzerland: MDPI , 2020. Vol. 12, no 2, p. 1-30, article id 266
Keywords [en]
flood, machine learning, remote sensing data, goodness-of-fit, overfitting, Haraz, Iran
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-77370DOI: 10.3390/rs12020266ISI: 000515569800065OAI: oai:DiVA.org:ltu-77370DiVA, id: diva2:1385194
Note

Validerad;2020;Nivå 2;2020-01-24 (johcin)

Available from: 2020-01-13 Created: 2020-01-13 Last updated: 2020-04-01Bibliographically approved

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