Water Quality Analysis and Prediction Techniques Using Artificial Intelligence

Abstract

Water is one of the most valuable natural resources and the most important commodity for human consumption, with a valuable role to play in determining the portability of drinking water. The everlasting elixir is water. Clean and hygienic drinking water is the need of the civilizations, and the trend is showing that the water quality and availability is becoming a big issue for many societies. In this study, water quality prediction model used in various studies was analyzed. The proposed article uses the past researches on water quality prediction and after deep study proposes the work on future improvements. This research explores the applications of artificial intelligence (AI), which help achieve water quality parameters (WQPs). Current AI applications in the water sector include (a) predictive water infrastructure management, (b) water demand and usage forecasting, (c) water reservoir and dam monitoring, (d) water quality tracking, and (e) water-related disaster monitoring and prediction. The literature review shows: (a) The rate of adoption of AI-based solutions in water infrastructure predictive maintenance has increased as AI becomes increasingly available, and data analytics and smart sensors are becoming more effective and affordable. (b) Deep learning technology has created a new generation of water management systems, capable of producing short (everyday) and long (yearly) forecasts. C) Countries worldwide are witnessing a rise in water reservoir and dam building, and AI-based techniques are being successfully applied in the production and operation of reservoirs. D) Control of water quality has become the most dramatically influenced by AI in comparison to other activities. AI is used in test-based water quality control, large body of water, and real-time monitoring of water quality. (E) AI could be used to predict water-related hazards with greater precision, frequency, and lead time, enabling focused post-disaster recovery. The research ends by illustrating the difficulties of achieving water-related WQPs by implementing AI.