River quality classification using different distances in k-nearest neighbors algorithm

The practice of river quality classification usually uses Water Quality Index (WQI) to evaluate the WQI values river. However, due huge data collection on pollution with uncertain water parameter values, need a different approach classify quality. One supervised algorithms known as K-Nearest Neighbors (KNN) seems give new for where each points are classified according k number or closest neighbors. Therefore, purpose this paper is apply distances and distance-weighted in KNN finding most accurate classification. accuracy results compared Support Vector Machine (SVM) Decision Tree (DT) algorithms. This algorithm will