Water Quality Analysis using Artificial Intelligence Algorithms

Drinking enough water every day is extremely important for the body to prevent the body from overheating, dehydration, kidney stones, etc., Thus Improving water quality prevents constipation, regulates body temperature, and normalizes B.P, etc., If the quality of water is predicted earlier, deadly risks such as typhoid, diarrhea, dysentery, etc., can be averted. In this paper, Artificial Intelligence techniques are used to predict Water Quality Index (WQI) and Water Quality Classification (WQC). The Indian Water Quality data set was used in this paper. For WQI prediction, neural network models like Long Short-Term Memory (LSTM) and regression models such as Ridge Regression, Random Forest Regressor with Randomized search CV have been developed. For WQC forecasting Machine Learning models like KNN, Logistic Regression, Logistic Regression Using GridSearchCV, XGBoost, SVM, and SVM Using Grid SearchCV for train-test splits like 70–30, 80–20 have been applied. For WQI prediction results demonstrated that Ridge regression has achieved the best R^2 of 95.21% with MSE 0.11. For WQC forecasting, XGBoost has achieved the highest accuracy (97.48%).