Алгоритм COOT Bird для ежедневного прогнозирования концентрации мелкодисперсных твердых частиц. Статистическое исследование

Abstract

Fine particulate matter (PM2.5) poses significant risks to public health and the natural environment. Accurate prediction of PM2.5 concentration is crucial for effective environmental management. In this study, we present a novel hybrid model, the COOT bird-inspired natural life model combined with Artificial Neural Network (COOT-ANN), for predicting daily PM2.5 concentration in hydier abad and Delhi from 2014 to 2022. The performance of the COOT-ANN model is compared with stand-alone ANN and Dragonfly-ANN (DA-ANN) hybrid models. Using the Taylor diagram, we demonstrate that the COOT-ANN model exhibits the closest proximity to the observation point, resulting in a 13.94 % and 11.42 % reduction in prediction errors compared to the ANN model in Hyderabad and Delhi, respectively. Furthermore, the box-plot of the COOT-ANN model closely resembles the actual data distribution. Consequently, the COOT-ANN model outperforms both the ANN and DA-ANN models at both monitoring stations. This innovative approach to air quality prediction can significantly enhance the accuracy of environmental protection programs.