Machine Learning in Water Management

Water management is a major issue already addressed in most international forums. Water harvesting and recycling are critical criteria for meeting the per capita availability of water (Krishna et.al., 2008). In this regard, we must focus on water management approaches that can be easily implemented across a wide range of applications (Benos et.al., 2021). Amid population increase and varied challenges, there is an urgent need to establish intelligent water management mechanisms for the effective distribution, conservation, and maintenance of water (Safder et.al., 2022). The present work highlights a few important application areas that are essential for precision water management in which artificial neural network (ANN), recurrent neural network (RNN) and random Forest (RF) are some of the most useful developments in machine learning (ML) models (Mokhtari et.al.,2020) in different aspects of water such as wastewater recycling, water distribution, rainfall estimation and irrigation water management that can be used to predict the future scenario. As a result, there is an urgent need to generate datasets and models/algorithms that can be used to deliver solutions for the above-mentioned applications. Machine learning architecture can aid in the development of a smart model for the sustainable use of natural resources (Lowe et.al.,2022), as well as the usage of AI/ML in conjunction with different neural network models and simple statistical analysis to create an effective water management framework to deal all water-related problems.