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

Marten Geertsema¹  Nadhir Al-Ansari²  JohnJ. Clague³  AssefaM. Melesse⁴  Ayub Mohammadi⁵  BaharinBin Ahmad⁵  Anuar Ahmad⁵  Himan Shahabi⁶  Sepideh Bahrami⁷  Kayvan Ghaderi⁸  Ebrahim Omidvar⁹  Ataollah Shirzadi^1,10  Kyoumars Habibi^1,11  Hoang Nguyen^1,12  Ata Amini^1,13  Omid Rahmati^1,13  Khabat Khosravi^1,14 

[1] British Columbia, Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Prince George, BC V2L 1R5, Canada;Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden;Department of Earth Sciences Simon Fraser University 8888 University Drive Burnaby, Burnaby, BC V5A 1S6, Canada;Department of Earth and Environment, Florida International University, Miami, FL 33199, USA;Department of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Malaysia;Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran;Department of Hydrological Sciences, University of Nevada, 89557-02601-775-685-8040, Reno, NV 89557, USA;Department of Information Technology and Computer Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj 66177-15175, Iran;Department of Rangeland and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan 87317-53153, Iran;Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, Iran;Department of urban and regional planning, Faculty of Art and Architecture, University of Kurdistan, Sanandaj 66177-15175, Iran;Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam;Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj 66177-15175, Iran;School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada;

关键词: flood;    machine learning;    remote sensing data;    goodness-of-fit;    overfitting;    haraz;    iran;   

DOI  :  10.3390/rs12020266

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown