Using artificial intelligence to predict aquatic pollution: A comprehensive review

Water contamination, or aquatic pollution, is a significant problem that endangers ecosystems, people's health, and the global economy. Laborious, expensive, and inadequate for real-time evaluations, traditional approaches to tracking and forecasting aquatic contamination include substantial manual sampling and laboratory testing. Pollutants and their origins are already complex, and the wide variety of chemical structures they contain further complicates matters. Because of these constraints, there is an ongoing, intense need for reliable predictions of pollution levels. The most promising method is artificial intelligence (AI), which can effectively interpret noisy data and handle nonlinear systems. When it comes to forecasting different types of water contamination, Artificial Neural Networks (ANNs) are among the most promising advanced AI models. To improve the forecasting accuracy of artificial neural networks (ANNs) for certain classes of pollutants, hybrid techniques, including radial basis function networks and small-world networks, have been developed. The field of water pollution research may also find success with Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). The purpose of this study is to shed light on research into how AI, and more specifically sophisticated models such as artificial neural networks (ANNs) and deep neural networks (DNNs), might improve the precision and effectiveness of pollution prediction.