Water Quality Data Outlier Detection Method Based on Spatial Series Features

Outlier detection is one of the major branch in data mining which has been applied in different fields. Researchers have focused on the outlier detection in time series, but rarely spatial series. In this paper, we propose a new outlier detection method based on k-nearest neighbour (KNN) and Mahalanobis distance, which is first applied to the water field. Experimental results verify that the algorithm has good accuracy and effectiveness in outlier detection for water quality spatial series dataset.