Artificial Intelligence Based Statistical Process Control for Monitoring and Quality Control of Water Resources: A Complete Digital Solution
Keywords:
Artificial intelligence, Machine Learning, data analysis, statistical process control, SPC charts, DAM projectsAbstract
In order to monitor water resource projects, this study examines the use of Statistical Process Control (SPC) charts in the context of dam projects. A survey of the body of knowledge on the subject of water resource project monitoring methods is the first stage in the research project. In this research, we'll focus on the advantages of use SPC charts to achieve that objective. Water resource projects are crucial pieces of infrastructure, and as such, they need constant supervision to ensure that they continue to operate properly and efficiently. SPC, or statistical process control, is a technique used for quality control and process monitoring across a wide range of industries. On the other hand, traditional SPC methodologies might not be suitable for real-time monitoring of water resource projects due to the complexity and unpredictability of water systems. Artificial intelligence (AI)-based methods have lately gained attention as a potential solution to these issues. In this paper, we provide an artificial intelligence-based SPC framework for real-time monitoring and quality control of projects involving water resources using a case study of a dam building project. The framework that has been proposed combines SPC with machine learning techniques to automatically detect anomalies and predict how the system will behave in the future. The results show that the artificial intelligence-based SPC framework outperforms the traditional SPC techniques in terms of timeliness, accuracy, and efficiency. The framework has the potential to improve the management and long-term profitability of water resource projects, which would ultimately aid in preserving the environment and the general public's health.
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