Advancing water quality monitoring through artificial neural networks : present insights and future opportunities in scientific exploration

The increasing demand for clean water resources and the challenges posed by rapid urbanization, industrialization, and climate change have intensified the need for effective water quality monitoring and management. This paper delves into the application of Artificial Neural Networks (ANNs) as an innovative tool for assessing water quality, highlighting current issues and prospects for the development of scientific research. ANNs, with their inherent ability to learn complex patterns, offer a promising solution to address non-linear relationships between water quality parameters, allowing for accurate predictions and modeling. We provide an overview of the existing literature on ANN applications in water quality assessment and discuss the benefits and limitations of these models. Key factors contributing to the success of ANNs in this field include appropriate selection of input features, model architecture, and training methodologies. We also examine recent advancements in hybrid and deep learning models that can further improve the accuracy and efficiency of water quality predictions. The paper concludes by identifying potential areas for future research, such as the integration of remote sensing data, the implementation of real-time monitoring systems, and the development of decision support tools for water resource management. By fostering collaboration between researchers, engineers, and policymakers, we can harness the power of ANNs to safeguard our water resources and ensure sustainable development.