Survey on DL Methods for Flood Prediction in Smart Cities

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Author(s): Roohi Sille (University of Petroleum and Energy Studies, India), Bhumika Sharma (University of Petroleum and Energy Studies, India), Tanupriya Choudhury (University of Petroleum and Energy Studies, India), Teoh Teik Toe (Nanyang Technological University, Singapore)and Jung-Sup Um (Kyungpook National University, South Korea)
Copyright: 2023
Pages: 19
Source title: Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities
Source Author(s)/Editor(s): Sabyasachi Pramanik (Haldia Institute of Technology, India)and K. Martin Sagayam (Karunya Institute of Technology and Sciences, India)
DOI: 10.4018/978-1-6684-6408-3.ch020

Keywords: Data Analysis and Statistics / Engineering Science Reference / Knowledge Management and Library Science / Mathematics & Statistics

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Abstract

The government has focused to maintain the needs of the populace's health and hygienic standards; numerous initiatives are involved, such as flood forecasting, water management, and sewage management. To prevent damage throughout the city, flood prediction must be done early on. “Smart” refers to artificial intelligence or machine learning methods, either directly or indirectly. To comprehend the general pattern and depth of the rainfall and to forecast the occurrence of floods, artificial intelligence techniques like deep learning are applied. To extract key properties for forecasting heavy rains and floods, many deep learning approaches, including CNN and deep belief networks, are applied. As a result, there is less harm done to both city infrastructure and human life. The study done on flood forecasting utilizing AI, ML, and deep learning techniques will be covered in this chapter. This review research will provide a thorough analysis based on the many types of deep learning models, the input datatypes for forecasting, the model effectiveness, real-time application, etc.

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Survey on DL Methods for Flood Prediction in Smart Cities

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