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

Document identifier: oai:DiVA.org:ltu-77370
Access full text here:10.3390/rs12020266
Keyword: Engineering and Technology, Civil Engineering, Geotechnical Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Geoteknik, Flood, Machine learning, Remote sensing data, Goodness-of-fit, Overfitting, Haraz, Iran, Soil Mechanics
Publication year: 2020
Relevant Sustainable Development Goals (SDGs):
SDG 11 Sustainable cities and communities
The SDG label(s) above have been assigned by OSDG.ai

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.

Authors

Himan Shahabi

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
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Ataollah Shirzadi

Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran
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Kayvan Ghaderi

Department of Information Technology and Computer Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
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Ebrahim Omidvar

Department of Rangeland and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran
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Nadhir Al-Ansari

Luleå tekniska universitet; Geoteknologi
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John J. Clague

Department of Earth Sciences Simon Fraser University, Burnaby, BC, Canada
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Marten Geertsema

British Columbia, Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Prince George, BC, Canada
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Khabat Khosravi

School of Engineering, University of Guelph, Guelph, ON , Canada
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Ata Amini

Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
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Sepideh Bahrami

Department of Hydrological Sciences, University of Nevada, Reno, NV, USA
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Omid Rahmati

Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
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Kyoumars Habibi

Department of urban and regional planning, Faculty of Art and Architecture, University of Kurdistan, Sanandaj, Iran
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Ayub Mohammadi

Department of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia
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Hoang Nguyen

Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
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Assefa M. Melesse

Department of Earth and Environment, Florida International University, Miami, FL, USA
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Baharin Bin Ahmad

Department of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM),Johor Bahru, Malaysia
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Anuar Ahmad

Department of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM),Johor Bahru, Malaysia
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