Chapter 1 - Artificial intelligence and machine learning in water resources engineering

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@InCollection{DANISH:2022:WRMCT,

• author = "Mohd Danish",
• title = "Chapter 1 - Artificial intelligence and machine learning in water resources engineering",
• editor = "Mohammad Zakwan and Abdul Wahid and Majid Niazkar and Uday Chatterjee",
• series = "Current Directions in Water Scarcity Research",
• publisher = "Elsevier",
• volume = "7",
• pages = "3--14",
• year = "2022",
• booktitle = "Water Resource Modeling and Computational Technologies",
• ISSN = "2542-7946",
• DOI = "doi:10.1016/B978-0-323-91910-4.00001-7",
• URL = "https://www.sciencedirect.com/science/article/pii/B9780323919104000017",
• keywords = "genetic algorithms, genetic programming, Water resources engineering, Artificial intelligence, Machine learning, Artificial neural network, Gene expression programming, Group method of data handling, Support vector machines",
• abstract = "Artificial intelligence (AI) and machine learning (ML) technology are bringing new opportunities in water resources engineering. ML, a subset of AI, is a significant research area of interest contributing smartly to the planning and execution of water resources projects. Still, ML in water resources engineering can explore new applications such as automatic scour detection, flood prediction and mitigation, etc. The challenges faced by the researchers in applying ML are mainly due to the acquisition of quality data and the cost involved in computational resources. This chapter reviews the history of the development of AI and ML algorithm applied in water resources. This chapter also presents the scientometric review of shallow ML algorithms, viz., linear regression, logistic regression, artificial neural network, decision trees, gene expression programming, genetic programming, multigene genetic programming, support vector machines, k-nearest neighbor, k-means clustering algorithm, AdaBoost, random forest, hidden Markov model, spectral clustering, and group method of data handling. This chapter analyzes the articles related to the shallow learning algorithms mentioned above from 1989 to 2022 and their applications in various aspects of water resource engineering",

}

Genetic Programming entries for Mohd Danish

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