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@article{174072,
        author = {Dr.K.N.S Lakshmi and A.Akshaya and Ch.Pavan Saketh and T.Manikanta and B.Jyothi},
        title = {Godavari River Water Quality Prediction Using Machine Learning Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2531-2536},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174072},
        abstract = {Water quality is critical for public health and environmental safety, necessitating effective monitoring methods. This study presents a methodology for predicting river water quality using integrated geospatial and statistical approaches. High-resolution satellite imagery, turbidity data from USGS Earth Explorer, and hydrological features extracted from QGIS, such as NDWI, were used to train a Random Forest model. The model demonstrated high accuracy in predicting water quality across diverse conditions. Spatial distribution of predictions was visualized in QGIS for intuitive interpretation. This framework showcases the effectiveness of combining remote sensing, GIS, and machine learning for scalable and efficient water quality assessment.},
        keywords = {Water quality, Remote sensing, GIS, Machine learning, Random Forest, Turbidity, NDWI, QGIS, Environmental monitoring},
        month = {March},
        }