Optimizing Reservoir Operation with Artificial Intelligence: A State-of-the-Art Review

AUTHOR(S)

Astha Yadav, Prof. Vijay K. Minocha, Prof. Rakesh Kumar

DOI: https://doi.org/10.46647/ijetms.2023.v07i05.043

ABSTRACT
Reservoir operation is an important aspect of water resource management since it involves the prudent distribution of water for a variety of reasons such as water supply, irrigation, hydropower generation, flood control, and environmental preservation. Historically, reservoir operation has depended on heuristic and rule-based approaches, which frequently struggle to adapt to the dynamic and variable character of water arrangements. Artificial intelligence (AI) technologies have grown in popularity in recent years to increase reservoir operation efficiency and adaptability. This cutting-edge review investigates the use of AI in reservoir optimization. We offer a complete overview of several AI methods used in reservoir operations, such as machine learning (ML), reinforcement learning (RL), artificial neural networks (ANNs), fuzzy logic (FL), evolutionary algorithms (EA), optimization algorithms (OA), and hybrid models (HM). We analyze the advantages and limits of AI-driven reservoir operating models on their potential to optimize water resource management, alleviate climate change impacts, and improve reservoir system sustainability using a comprehensive review of the literature. This review paper aims to provide an overview of the current level of reservoir operation optimization with AI as well as insights into future approaches. As the globe faces increasing difficulties related to water scarcity, climate unpredictability, and environmental protection, utilizing the potential of artificial intelligence in reservoir operation becomes critical for building efficient, adaptive, and sustainable water management approaches.
Page No: 368 - 373

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How to Cite This Article:
S R Rahul,T Sekar, Daniel Antony Arokiyasamy . Optimizing Reservoir Operation with Artificial Intelligence: A State-of-the-Art Review . ijetms;7(5):368-373. DOI: 10.46647/ijetms.2023.v07i05.043