Machine learning model for Water Quality evaluation: Systematic Review

Evaluating water quality is essential to maintaining healthy ecosystems and providing safe drinking water. There has been a rise in enthusiasm for creating models for evaluating water quality thanks to the development of machine learning methods. In order to highlight the most important results and trends in the field of water quality evaluation, this systematic review seeks to offer an overview of the most advanced machine learning models currently in use. Relevant papers published during [detailed time period] were included after a thorough search of electronic resources. The studies were chosen because of their comprehensive coverage of a wide variety of water types and characteristics, including pH, dissolved oxygen, turbidity, and nutrient concentrations, across a wide range of water sources (rivers, lakes, reservoirs, and groundwater). This overview sheds light on the wide variety of machine learning algorithms used in water quality assessment. These algorithms range from the more traditional support vector machines (SVM) to deep learning models like convolutional neural networks (CNN) and recurrent neural networks (RNN). Water quality metrics and pollution sources may now be correctly predicted using these models. This review also delves into the physicochemical characteristics, meteorological data, topographical qualities, and remote sensing data that go into making these models work. There has been encouraging progress toward better water quality prediction models thanks in part to the inclusion of more data sources. Accuracy, precision, recall, and correlation coefficients are only few of the performance evaluation criteria studied here. In particular, it highlights data shortages, data quality concerns, and model interpretability as bottlenecks in the creation and rollout of machine learning models for water quality evaluation. Future research recommendations are suggested in light of the findings of the systematic review. The creation of interpretable models, consistent data collecting and sharing processes, and improved stakeholder comprehension and confidence in the outcomes are all areas that need attention. This systematic review’s findings help further the use of machine learning techniques to the evaluation of water quality. They provide light on the present state of the art, point out research gaps, and offer advice to academics, policymakers, and water resource managers on how to best use machine learning to manage and conserve water quality.