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Integrating Machine Learning and AI for Improved Hydrological Modeling and Water Resource Management

Integrating Machine Learning and AI for Improved Hydrological Modeling and Water Resource Management

Djabeur Mohamed Seifeddine Zekrifa, Megha Kulkarni, A. Bhagyalakshmi, Nagamalleswari Devireddy, Shilpa Gupta, Sampath Boopathi
Copyright: © 2023 |Pages: 25
ISBN13: 9781668467916|ISBN10: 1668467917|ISBN13 Softcover: 9781668467923|EISBN13: 9781668467930
DOI: 10.4018/978-1-6684-6791-6.ch003
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MLA

Zekrifa, Djabeur Mohamed Seifeddine, et al. "Integrating Machine Learning and AI for Improved Hydrological Modeling and Water Resource Management." Artificial Intelligence Applications in Water Treatment and Water Resource Management, edited by Victor Shikuku, IGI Global, 2023, pp. 46-70. https://doi.org/10.4018/978-1-6684-6791-6.ch003

APA

Zekrifa, D. M., Kulkarni, M., Bhagyalakshmi, A., Devireddy, N., Gupta, S., & Boopathi, S. (2023). Integrating Machine Learning and AI for Improved Hydrological Modeling and Water Resource Management. In V. Shikuku (Ed.), Artificial Intelligence Applications in Water Treatment and Water Resource Management (pp. 46-70). IGI Global. https://doi.org/10.4018/978-1-6684-6791-6.ch003

Chicago

Zekrifa, Djabeur Mohamed Seifeddine, et al. "Integrating Machine Learning and AI for Improved Hydrological Modeling and Water Resource Management." In Artificial Intelligence Applications in Water Treatment and Water Resource Management, edited by Victor Shikuku, 46-70. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-6791-6.ch003

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Abstract

The hydrological cycle is an important process that controls how and where water is distributed on Earth. It includes processes including transpiration, evaporation, condensation, precipitation, runoff, and infiltration. However, there are obstacles to understanding and modelling the hydrological cycle, such as a lack of data, ambiguity, fluctuation, and the impact of human activity on the natural balance. Techniques for accurate modelling are essential for managing water resources and risk reduction. With potential uses in rainfall forecasting, streamflow forecasting, and flood modelling, machine learning and artificial intelligence (AI) are effective tools for hydrological modelling. Case studies and real-world examples show how solutions to problems like data quality, interpretability, and scalability may be applied in real-world situations. Discussions of future directions and challenges emphasise new developments and areas that need more investigation and cooperation.

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