Intellectual Technologies on Transport

Интеллектуальные технологии на транспорте

2413-2527

81646

10.20295/2413-2527-2024-137-26-31

Математическое моделирование, численные методы и комплексы программ

Mathematical modeling, counting methods and software complexes

Математическое моделирование, численные методы и комплексы программ

COOT Bird-Inspired Algorithm for Daily Fine Particulate Matter Concentration Prediction Statistical Study

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

Валид

Ахмед Хассен Аль-Нуами

Valid

Ahmed Hassen Al'-Nuami

waleed.hassan@uodiyala.edu.iq

Университет Диялы Баакуба Ирак Diyala University Baquba Iraq

14 04 2024

1 26 31 11 04 2024

https://itt-pgups.ru/en/nauka/article/81646/view

Мелкодисперсные твердые частицы (PM2,5) представляют значительный риск для здоровья населения и окружающей среды. Точное прогнозирование концентрации PM2,5 имеет решающее значение для эффективного управления окружающей средой. В этом исследовании мы представляем новую гибридную модель, модель естественной жизни COOT, вдохновленную птицами, в сочетании с искусственной нейросетью (COOT-ANN) для прогнозирования ежедневной концентрации PM2,5 в Хайдарабаде и Дели с 2014 по 2022 год. Производительность модели COOTANN сравнивается с моделью ANN и гибридной моделью Dragonfly-ANN (DA-ANN). Используя диаграмму Тейлора, мы видим, что модель COOT-ANN демонстрирует наибольшую близость к точке наблюдения, что приводит к снижению ошибок прогнозирования на 13,94 % и 11,42 % по сравнению с моделью ANN в Хайдарабаде и в Дели соответственно. Более того, усиковая диаграмма модели COOT-ANN очень похожа на фактическое распределение данных. Следовательно, модель COOT-ANN превосходит модели ANN и DA-ANN на обеих станциях мониторинга. Этот инновационный подход к прогнозированию качества воздуха может значительно повысить точность программ защиты окружающей среды.

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.

модель естественной жизни COOT алгоритм DragonFly мелкодисперсные твердые частицы прогнозирование

COOT bird-inspired natural life model Dragonfly algorithm fine particulate matter prediction

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