Impact of Machine Learning Applications on Water Quality Prediction

Water is necessary for survival. Without adequate water quality, living beings can be a target of many diseases to a level of death. Water Quality Indexing is a method being used for analysis of water quality. WQI comes with various disadvantages such as data dependency. Using Machine Learning algorithm lev erages such issues as ML algorithms can adapt according to data and learn from various sources. ML algorithms also increases accuracy by recognizing patterns and real time data prediction. There can be various combination of ML algorithms which can be applied such as regression and probability bayes algorithm. As the data to be dealt is huge it needs to be combined with various dimensionality reduction algorithms. Various Boosting algorithms such as XGBoost, LightGBM helps in iteratively building a series of weak learners. Boosting algorithms help in identification of important features which in turn increases the accuracy by specifically focusing on the parameters influencing the water quality.