Novel Regression and Least Square Support Vector Machine Learning Technique for Air Pollution Forecasting
This addresses air pollution forecasting for environmental and health monitoring in metropolitan cities, but appears incremental as it builds on existing regression and support vector machine methods.
The authors tackled air pollution forecasting by proposing a novel DR-LSSV technique, which outperformed conventional machine learning methods in accuracy, time, and false positive rate.
Air pollution is the origination of particulate matter, chemicals, or biological substances that brings pain to either humans or other living creatures or instigates discomfort to the natural habitat and the airspace. Hence, air pollution remains one of the paramount environmental issues as far as metropolitan cities are concerned. Several air pollution benchmarks are even said to have a negative influence on human health. Also, improper detection of air pollution benchmarks results in severe complications for humans and living creatures. To address this aspect, a novel technique called, Discretized Regression and Least Square Support Vector (DR-LSSV) based air pollution forecasting is proposed. The results indicate that the proposed DR-LSSV Technique can efficiently enhance air pollution forecasting performance and outperforms the conventional machine learning methods in terms of air pollution forecasting accuracy, air pollution forecasting time, and false positive rate.