An Optimised Model to Forecast the Water Portability Using the Restricted Boltzmann Machine and Neural Network

Water quality is paramount for environmental health and human well-being, especially with global population growth and urbanization exacerbating pollution threats. This study delves into water quality prediction, employing advanced modeling, data analysis, and cutting-edge technologies such as sensor networks and machine learning. Datasets from the UCI Machine Learning Repository, EPA, Kaggle, and the Government of Canada form the basis for comprehensive research. Linear Regression, Decision Trees, Random Forest, SVM, Neural Networks, KNN, and Gradient Boosting Models are evaluated for suitability, each catering to specific data characteristics. AutoML tools and time series models like ARIMA are considered, emphasizing preprocessing and domain expertise. The research aims to advance water quality prediction methods, offering insights into complex relationships, reliable predictions, and a visionary sustainable water resource management framework. We used the Restricted Boltzmann Machine, a neural network model, to predict quality level. We got an accuracy of 0.69. This study addresses challenges, reviews methodologies, and proposes innovative approaches, contributing to improving water quality prediction and environmental conservation.