A systematic review on machine learning algorithms used for forecasting lake‐water level fluctuations

Chakravarthy, S. R. Sannasi; Bharanidharan, N.; Rajaguru, Harikumar

doi:10.1002/cpe.7231

Cited by 7 publications

(2 citation statements)

References 103 publications

(296 reference statements)

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“…Recent advancements in ML algorithms have significantly enhanced the ability to forecast the complex and dynamic process of lake water level fluctuations, which are challenging to predict accurately due to their nonlinear and stochastic nature [237]. In a related context, a real-time data analysis platform uses ML to predict water consumption.…”

Section: Using ML For Water Activitiesmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management

Drogkoula,

Kokkinos,

Samaras

2023

Applied Sciences

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This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain of water resource management. Environmental issues, such as climate change and ecosystem destruction, pose significant threats to humanity and the planet. Addressing these challenges necessitates sustainable resource management and increased efficiency. Artificial intelligence (AI) and ML technologies present promising solutions in this regard. By harnessing AI and ML, we can collect and analyze vast amounts of data from diverse sources, such as remote sensing, smart sensors, and social media. This enables real-time monitoring and decision making in water resource management. AI applications, including irrigation optimization, water quality monitoring, flood forecasting, and water demand forecasting, enhance agricultural practices, water distribution models, and decision making in desalination plants. Furthermore, AI facilitates data integration, supports decision-making processes, and enhances overall water management sustainability. However, the wider adoption of AI in water resource management faces challenges, such as data heterogeneity, stakeholder education, and high costs. To provide an overview of ML applications in water resource management, this research focuses on core fundamentals, major applications (prediction, clustering, and reinforcement learning), and ongoing issues to offer new insights. More specifically, after the in-depth illustration of the ML algorithmic taxonomy, we provide a comparative mapping of all ML methodologies to specific water management tasks. At the same time, we include a tabulation of such research works along with some concrete, yet compact, descriptions of their objectives at hand. By leveraging ML tools, we can develop sustainable water resource management plans and address the world’s water supply concerns effectively.

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Section: Using ML For Water Activitiesmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management

Drogkoula,

Kokkinos,

Samaras

2023

Applied Sciences

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“…The quality variations in river water are indicative of gradual changes, and uncertain and non-linear factors. This makes the process of accurate water quality prediction more difficult [2]. At the same time, predicting water quality is highly significant for managing and planning of water resources and its environment.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Water Quality Using SoftMax-ELM Optimized Using Adaptive Crow-Search Algorithm

Chakravarthy,

Bharanidharan,

Venkatesan

et al. 2023

IEEE Access

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Water is a predominant source in the survival and development of all human lives. On top of all, predicting water quality is a significant one since water is essential in regulating our human body. In recent days, the advent of machine learning techniques has been supporting a lot in water quality prediction. Accordingly, Adaptive Crow Search Optimized SoftMax-Extreme Learning Machine (AdCSO-sELM) is proposed to improve the ELM performance by making the flight length adaptively with respect to the iterations. Here, the research novelty lies in making the CSOA parameters as a dynamic one which in turn provides promising ELM performance. Finally, the proposed AdCSO-sELM provides a superior accuracy of 96.54% for classifying water potability using the Kaggle dataset.INDEX TERMS crow search algorithm, extreme learning machine, neural network, optimization, water quality.

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Comparative assessment of advanced machine learning techniques for simulation of lake water level fluctuations based on different dimensionality reduction methods

et al. 2023

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A systematic review on machine learning algorithms used for forecasting lake‐water level fluctuations

Cited by 7 publications

References 103 publications

A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management

A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management

Prediction of Water Quality Using SoftMax-ELM Optimized Using Adaptive Crow-Search Algorithm

Comparative assessment of advanced machine learning techniques for simulation of lake water level fluctuations based on different dimensionality reduction methods

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