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- W3170834577 abstract "Decision making and planning for the prediction of flood events and conditions requires the use of adequate models and methods. In recent years, appropriate models and algorithms such as machine learning (ML) and deep learning (DL) have been developed and used in many research projects dealing with flood mapping. In this regard, the main purpose of this chapter is to present a review of applying ML/DL methods to process satellite images for generating flood maps. Moreover, the basic concepts of some ML data driven methods are presented. Three case studies with different ML algorithms have been selected and are illustrated to provide a better understanding of their application in flood studies. The findings of these case studies show that ML models are mostly applied to identify and predict flooded versus non-flooded pixels in images but important challenges remain. These challenges need to be solved if ML is to be valuable for decision making processes related to flood management." @default.
- W3170834577 created "2021-06-22" @default.
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- W3170834577 date "2021-01-01" @default.
- W3170834577 modified "2023-09-27" @default.
- W3170834577 title "Emerging Techniques in Machine Learning for Processing Satellite Images of Floods" @default.
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- W3170834577 doi "https://doi.org/10.1016/b978-0-12-819412-6.00015-8" @default.
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