The Integration of AI and ML in Water and Wastewater Engineering for Sustainable Infrastructure - Paniteja Madala, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
Advanced digital technologies provide invaluable opportunities for sustainable water management infrastructure. AI and ML are buzzing technologies for esteem data analytics with capabilities to notice complicated patterns and correlations. These are insightful for understanding water treatment and management process elements. Leveraging AI and ML logic for analyzing data generated from water engineering treatment processes, enabling esteem plans, implementing predictive maintenance, and optimizing operational procedures are the core aspects of this study. Extracting applicable insights using complicated data trends facilitates AI and ML to develop initiative-taking water treatment strategies. ML modeling possesses capabilities to predict water demand accurately. This represents anomalies in quality due to potential system failures. Involving technical paradigms in handling wastewater treatment processes is important to mitigate the influence of climate and consumption surge. These processes are critical for managing climate/urbanization pressures on processes with future research directions. AI and ML are capable of resiliently treating wastewater with initiative-taking monitoring and implementing predictive maintenance mechanisms. This involves capabilities to oversee climate fluctuations. This paper depicts about ambivalent capabilities of implementing these technologies by mentioning practical challenges in process. This study is an attempt to explore research directions toward integrating wastewater treatment solutions with AI and ML systems. The motive of this research is to extend the aspects and investigate literature available regarding technical and data analytics potential in managing water resources. This paper underscores capabilities of AI and ML to motivate utilization of advanced technology paradigms for resilient wastewater treatment. Recommendations are included with future research motives for contributing to sustainable water management domain.
Keywords: Artificial Intelligence; Machine Learning; Water Engineering; Wastewater Engineering; Smart Water; Environmental Sustainability