Water Quality Prediction Using CNN And BI-LSTM

Abstract

Assessment of water quality is needed to protect the ecosystem, promote human well-being, and manage water resources sustainably. The increasing pollution and variability caused by climate require smart predictive tools that have the ability to detect the complicated associations between various physicochemical indicators. In this paper, the authors propose a high-tech deep learning-based framework of water quality prediction, which incorporates Convolutional Neural Networks (CNN) models and Bidirectional Long Short-Term Memory (Bi-LSTM) networks to improve the precision of predicting the Water Quality Index (WQI). CNN module retrieves the spatial dependencies among the parameters pH, turbidity, dissolved oxygen, temperature, nitrate and ammonia, and the Bi-LSTM part assumes the temporal dynamics of historical water datasets. The model has an attention mechanism which gives precedence to the most influential features enhancing the interpretability and predictive performance. It will normalize the data and restructure the sequences to facilitate effective learning and reduce the data inconsistencies. Compared to the traditional analytical methods, the hybrid architecture provides the system with greater precision in identifying nonlinear interactions, seasonal trends, and sudden changes in the environment. The trained model is deployed as an interactive Streamlit interface and allows real time prediction, visualization, and generation of automated reports. This smart system provides a powerful means of environmental surveillance, aiding the timely decision-making process of the regulatory bodies, researchers and water management officials.