Title: Wetland‐Inundated Area Modeling and Monitoring Using Supervised and Machine Learning Classifiers
Abstract: Chapter 17 Wetland-Inundated Area Modeling and Monitoring Using Supervised and Machine Learning Classifiers Swapan Talukdar, Swapan Talukdar Department of Geography, University of Gour Banga, Mokdumpur, Malda, IndiaSearch for more papers by this authorSakshi Mankotia, Sakshi Mankotia Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, IndiaSearch for more papers by this authorMd Shamimuzzaman, Md Shamimuzzaman Department of Disaster Management, Begum Rokeya University, Rangpur, BangladeshSearch for more papers by this author Shahfahad, Shahfahad Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, IndiaSearch for more papers by this authorSusanta Mahato, Susanta Mahato Department of Geography, University of Gour Banga, Mokdumpur, Malda, IndiaSearch for more papers by this author Swapan Talukdar, Swapan Talukdar Department of Geography, University of Gour Banga, Mokdumpur, Malda, IndiaSearch for more papers by this authorSakshi Mankotia, Sakshi Mankotia Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, IndiaSearch for more papers by this authorMd Shamimuzzaman, Md Shamimuzzaman Department of Disaster Management, Begum Rokeya University, Rangpur, BangladeshSearch for more papers by this author Shahfahad, Shahfahad Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, IndiaSearch for more papers by this authorSusanta Mahato, Susanta Mahato Department of Geography, University of Gour Banga, Mokdumpur, Malda, IndiaSearch for more papers by this author Book Editor(s):Prem C. Pandey, Prem C. Pandey Center for Environmental Sciences and Engineering, School of Natural Sciences, Greater Noida, 201314 Uttar Pradesh, IndiaSearch for more papers by this authorLaxmi K. Sharma, Laxmi K. Sharma Department of Environmental Science, School of Earth Sciences, Kishangarh (Ajmer), 305817 Rajasthan, IndiaSearch for more papers by this author First published: 22 January 2021 https://doi.org/10.1002/9781119616016.ch17Citations: 4 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Summary The wetlands have major importance in environments, as they provide several goods and services. But the wetlands are in deep crisis and are claiming the attention of people around the world who are calling for sustainability. The Sunamganj district of Bangladesh is known as haor basin, because it is the home of many big wetlands, but they are facing several problems such surface water crisis, groundwater depletion, and anthropogenic interference. Therefore, the present study was designed to study the mapping of the wetlands inundation area for the year 2018 using artificial neural network (ANN), Random Forest (RF), Support Vector Machine (SVM), Maximum likelihood classifier(MLC), parallelepiped (PP), and Spectral Information Divergence (SID). Then, the best classifier was selected based on the accuracy assessment by κappa coefficient and index-based validation. The wetland inundation areas for 2000–2015 were modeled using best classifier. Then the wetlands fragmentation analysis was performed to understand the changing pattern of the wetlands. The finding of the wetland inundation area using six classifiers showed that SVM classified the wetlands area with better accuracy than the other methods, which validated by overall accuracy by κappa and index-based validation. The spatiotemporal study reported that wetland-inundated areas have decreased by 46.17% within the last 18 years. The fragmentation analysis showed that the large core areas have been lessened by 82% over time. Therefore, the overall study reported that the wetland-inundation areas have decreased at an alarming rate, which will affect the whole ecosystem. Citing Literature Advances in Remote Sensing for Natural Resource Monitoring RelatedInformation
Publication Year: 2021
Publication Date: 2021-01-22
Language: en
Type: other
Indexed In: ['crossref']
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Cited By Count: 18
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