Improved Ozone Level Detection through Feature Selection with Modified Whale Optimization Algorithm

Abstract

This study presents a new approach for ozone level detection through feature selection by the modified Whale Optimization Algorithm (mWOA). This study aims to enhance the accuracy and efficiency of ozone level prediction models by selecting the most informative features from the dataset. As air quality deterioration poses significant risks to both human health and ecological equilibrium, pinpointing relevant features becomes essential for boosting prediction accuracy. The scope of the research includes comparing the performance of mWOA with the original WOA in two feature selection techniques: filter-based and wrapper-based. The experiments run proposed approaches on a multivariate time-series dataset with 20 repetitions. The evaluation criteria include processing time, number of features selected, and classification accuracy obtained by the kNN classifier. The statistical results demonstrate the effectiveness of the proposed mWOA approach, outperforming WOA due to the modified control parameter that enables a more precise exploration of the search area. The findings of this study reveal the improved performance of mWOA in selecting informative features, resulting in better prediction on average: 93.75% for filter-based and 94.49% for wrapper-based. In conclusion, the wrapper-based feature selection using the mWOA approach proves to be a valuable asset in enhancing the accuracy and efficiency of ozone level detection models. In the future, the proposed technique can be used for more applications in environmental science and engineering research.