Automatic Casting Control Method of Continuous Casting Based on Improved Soft Actor–Critic Algorithm

Wu, Xiaojun; Jiang, Wenze; Yuan, Sheng; Kang, Hongjia; Qi, Guo-Jun; Mi, Jinzhou

doi:10.3390/met13040820

Cited by 2 publications

(1 citation statement)

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“…River flow control, ecological preservation. [206] Machine learning and water allocation Twin delayed deep deterministic policy gradient (TD3)…”

Section: Soft Actor-critic (Sac)mentioning

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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“…River flow control, ecological preservation. [206] Machine learning and water allocation Twin delayed deep deterministic policy gradient (TD3)…”

Section: Soft Actor-critic (Sac)mentioning

confidence: 99%