Machine learning in dam water research: an overview of applications and approaches

• Main Authors: Farashazillah Yahya, Bashirah Mohd Fazli, Hasimi Sallehudin, Izham Jaya
• Format: Article
• Language: English
• Published: 2020
• Subjects: QC Physics
• Online Access: https://eprints.ums.edu.my/id/eprint/25593/1/Machine%20learning%20in%20dam%20water%20research.pdf; https://eprints.ums.edu.my/id/eprint/25593/; https://doi.org/10.30534/ijatcse/2020/56922020

Dam plays a crucial role in water security. A sustainable dam intends to balance a range of resources involves within a dam operation. Among the factors to maintain sustainability is to maintain and manage the water assets in dams. Water asset management in dams includes a process to ensure the planned maintenance can be conducted and assets such as pipes, pumps and motors can be mended, substituted, or upgraded when needed within the allocated budgetary. Nowadays, most water asset management systems collect and process data for data analysis and decision-making. Machine learning (ML) is an emerging concept applied to fulfill the requirement in engineering applications such as dam water researches. ML can analyze vast volumes of data and through an ML model built from algorithms, ML can learn, recognize and produce accurate results and analysis. The result brings meaningful insights for water asset management specifically to strategize the optimal solution based on the forecast or prediction. For example, a preventive maintenance for replacing water assets according to the prediction from the ML model. We will discuss the approaches of machine learning in recent dam water research and review the emerging issues to manage water assets in dams in this paper.