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Pak, Abbas, Nematollahi, Mohammad. (1405). Benchmarking Recurrent Neural Network Architectures for Sequential Prediction with Application to Air Pollution Forecasting. فصلنامه علمی زیست فناوری گیاهان زراعی, (), -. doi: 10.30473/jaslda.2026.13501
Abbas Pak; Mohammad Javad Nematollahi. "Benchmarking Recurrent Neural Network Architectures for Sequential Prediction with Application to Air Pollution Forecasting". فصلنامه علمی زیست فناوری گیاهان زراعی, , , 1405, -. doi: 10.30473/jaslda.2026.13501
Pak, Abbas, Nematollahi, Mohammad. (1405). 'Benchmarking Recurrent Neural Network Architectures for Sequential Prediction with Application to Air Pollution Forecasting', فصلنامه علمی زیست فناوری گیاهان زراعی, (), pp. -. doi: 10.30473/jaslda.2026.13501
Pak, Abbas, Nematollahi, Mohammad. Benchmarking Recurrent Neural Network Architectures for Sequential Prediction with Application to Air Pollution Forecasting. فصلنامه علمی زیست فناوری گیاهان زراعی, 1405; (): -. doi: 10.30473/jaslda.2026.13501

Benchmarking Recurrent Neural Network Architectures for Sequential Prediction with Application to Air Pollution Forecasting

Journal of Advances in Statistical Learning and Data Analysis
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 13 مهر 1405
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.30473/jaslda.2026.13501
نویسندگان
Abbas Pak* 1؛ Mohammad Javad Nematollahi2
1Associate Professor of Statistics, Computational Statistician & Data Scientist, Department of Computer Sciences, Shahrekord University
2Urmia University
چکیده
This study presents a comprehensive benchmarking analysis of state-of-the-art recurrent neural network (RNN) architectures for sequential prediction tasks. A wide range of recurrent models, including Simple RNN, Independently Recurrent Neural Network (IndRNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), Residual BiLSTM (ResBiLSTM) are systematically investigated under a unified experimental framework. The proposed benchmarking framework evaluates all models on a real-world air pollution prediction task using multivariate environmental and meteorological data. Performance is assessed using multiple standard metrics, including root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²), enabling a comprehensive evaluation of predictive accuracy and generalization capability. Experimental results demonstrate that IndRNN achieves the best overall performance, providing superior accuracy while maintaining a relatively simple and computationally efficient architecture compared to other deep recurrent variants. The actual-versus-predicted analysis further supports this conclusion and IndRNN exhibits the closest alignment between predicted and observed values, achieving the highest coefficient of determination (R² = 0.6375) and the lowest prediction error (RMSE = 17.0203) among the evaluated recurrent architectures. These findings highlight the effectiveness of IndRNN as a strong candidate for practical sequential forecasting applications.
کلیدواژه‌ها
Sequential prediction؛ Deep learning؛ Recurrent neural network؛ Air pollution forecasting
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تعداد مشاهده مقاله: 2


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