Prediction of Environmental Pollution Using Hybrid PSO-K-Means Approach

Abstract

Air pollution is increasing day by day, decreasing the world economy, degrading the quality of life, and resulting in a major productivity loss. At present, this is one of the most critical problems. It has a significant impact on human health and ecosystem. Reliable air quality prediction can reduce the impact it has on the nearby population and ecosystem; hence, improving air quality prediction is the prime objective for the society. The air quality data collected from sensors usually contains deviant values called outliers which have a significant detrimental effect on the quality of prediction and need to be detected and eliminated prior to decision making. The effectiveness of the outlier detection method and the clustering methods in turn depends on the effective and efficient choice of parameters like initial centroids and number of clusters, etc. The authors have explored the hybrid approach combining k-means clustering optimized with particle swarm optimization (PSO) to optimize the cluster formation, thereby improving the efficiency of the prediction of the environmental pollution.