Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{183811,
        author = {Manwendra Kumar Satyam and Prof. Anurag Srivastava},
        title = {A Review on Deep Learning Prediction for Water Quality Monitoring},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {3104-3107},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183811},
        abstract = {The increasing scarcity and contamination of freshwater resources have made water quality monitoring an essential global concern. Traditional approaches to water quality assessment, which often rely on manual sampling and laboratory testing, are time-consuming, labor-intensive, and lack real-time responsiveness. In recent years, the emergence of deep learning (DL) has offered a transformative potential for predictive analytics in environmental monitoring. This review paper presents a comprehensive overview of the current research landscape on deep learning techniques applied to water quality prediction. Various models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and hybrid architectures, are analyzed for their performance in forecasting key water quality parameters such as pH, turbidity, dissolved oxygen, and chemical contaminants. The review also explores datasets, sensor integration, feature engineering methods, and evaluation metrics used across studies. Furthermore, it highlights the challenges related to data scarcity, model interpretability, and deployment in resource-constrained environments. By synthesizing recent advances, this paper aims to guide researchers and practitioners toward developing efficient, accurate, and scalable DL-based solutions for real-time water quality monitoring, with implications for smart water management and public health protection.},
        keywords = {Water quality, Machine learning models, Deep learning, Water quality index, Water quality classification},
        month = {August},
        }