Recent Trends in the Quality Module of Sustainable Water Management Models-A Systematic Review

Water resources are crucial natural assets that all living organisms rely upon. Water is essential for consumption, industrial processes, and farming activities. In recent years, human activities and natural disasters have contributed to water pollution. It is vital to manage water quality to achieve sustainable development. This article examines the water quality index and the use of machine learning and deep learning algorithms in managing various water resources. Machine learning models play a role in different systems for water treatment and management such as predicting water quality and developing technologies to address water-related challenges. Additionally, it suggests methods to reduce water pollution and enhance the quality of various water resources including lakes, rivers, groundwater. It also evaluates different models that provide insights into natural disasters (for instance, GIS offers data on water levels in different areas, which can help prevent floods and droughts in real-time scenarios). Issues related to water resource management is investigated using machine learning algorithms, deep learning algorithms, and hybrid algorithms, with metrics such as RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percent Error), R² (Coefficient of Determination), MSE (Mean Square Error), and RAE (Relative Absolute Error). Many studies focus on hybrid algorithms for analyzing water resource management. In this work, we have proved that water quality analysis achieved better results in ML and DL models. Over the next decade, the integration of machine learning tools is anticipated to accelerate the development of sustainable water resource management strategies.