Research Trends in Smart Cost-Effective Water Quality Monitoring and Modeling: Special Focus on Artificial Intelligence

Abstract

Numerous conventional methods are available for analyzing various water quality parameters to determine the water quality index. However, ongoing surveillance is necessary for large bodies of water. A water quality monitoring system supports a robust surface and groundwater ecosystem. Various tactics are used to improve aquatic habitats: identification of the precise chemical pollutants released into the aquatic environment; advancements in assessing ecological effects; and working on ways to enhance water quality through informing the public, communities, businesses, etc. In order to save the marine ecosystem and those who entirely depend on these enormous bodies of water, it is also crucial to continuously handle many data sets of water quality metrics. To predict the water quality index, this review paper provides an overview of water quality monitoring, the modeling and numerous sensors employed, and various artificial intelligence approaches. Various water quality models were proposed to assess pH, a few components, and alkalinity. Additionally, handling raw information for surface and groundwater quality metrics was studied using artificial intelligence techniques like neural networks.