Determination Of Water Quality by Using Artificial Intelligent
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Determination Of Water Quality by Using Artificial Intelligent
Manjunatha M Katti, Gowtham S, Kirankumar, Raju, Usha M R.
Department of civil engineering CIT. Gubbi, Tumkur-572216
Abstract: Water quality assessment plays a crucial role in ensuring public health and the sustainability of aquatic ecosystems. Traditional water quality analysis methods are often time-consuming, labor-intensive, and require expensive equipment. To overcome these limitations, this project aims to develop a using artificial intelligence (AI) and machine learning (ML) techniques. The proposed methodology involves the collection of water samples from various sources, including rivers, and the subsequent analysis of multiple physicochemical parameters. These parameters may include pH, solids, sulfate, turbidity and conductivity. The collected data will be used to train and validate AI and ML models. Initially, feature engineering techniques will be applied to extract relevant features from the acquired water quality dataset. These features will serve as input variables for the AI and ML models. Multiple algorithms, such as decision trees, random forests, support vector machines, K-nearest neighbor, and XGBoost will be employed to develop predictive models for water quality assessment. The developed AI and ML models will be implemented into a user-friendly software interface, allowing users to input water quality parameters and obtain instant assessments.
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