1
Department of Statistics and Programming, Faculty of Economics, University of Tishreen, Tartous P.O. Box 7 2230, Syria
(khderalakkari1990@gmail.com)
2
Department of System Programming, South Ural State University, Chelyabinsk 454080, Russia
(abotalebmostafa@bk.ru)
3
Faculty of Science, School of Science and Technology University of New England, NSW Armidale, Australia
(Amr.Mostafa@live.com)
4
Department of Food and Biotechnologies, South Ural State University, Chelyabinsk
(ammarka89@gmail.com)
5
Department of Banking and Insurance, Faculty of Economics, Damascus University, Damascus, Syrian Arab Republic
(abdullah1991.alkhatib@damascusuniversity.edu.sy)
6
Department of Banking and Insurance, Faculty of Economics, Damascus University, Damascus, Syrian Arab Republic
(bayan1992.alshaib@damascusuniver-sity.edu.sy)
7
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, 16 Mansoura, 35111, Egypt
(skenawy@ieee.org)
Abstract :
In the rapidly evolving field of smart cities, the accurate prediction of weather patterns plays a crucial role in various industries such as agriculture, tourism, and socioeconomic development. This study utilizes Artificial Intelligence (AI) and Machine Learning (ML) through advanced machine learning techniques, including Encoder-Decoder LSTM and Attention LSTM models, to analyze daily climatic weather data in the Narmadapuram district. The research investigated the future patterns of key weather parameters, including maximum temperature, minimum temperature, morning relative humidity, evening relative humidity, and bright sunshine hours. The study analyzed daily data collected between November 1, 1977 and April 30, 2022, with 80% used for training and 20% for testing. Results showed that the Encoder-Decoder LSTM model outperformed the Attention LSTM model in forecasting maximum temperature, morning relative humidity, evening relative humidity, and bright sunshine hours, while the Attention LSTM model had better results in predicting minimum temperature. The findings provide valuable insights into climatic patterns and variability and have implications for the development of more precise weather forecasting models. This study demonstrates the potential of AI and ML in addressing the challenges of smart cities and highlights the significance of machine learning techniques in weather forecasting, a critical aspect of urban operations and decision-making.
Keywords :
Smart Cities; Weather; Time series; Forecasting; (Encoder-decoder) LSTM; Attention LSTM.
References :
[1] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.
[2] R. F. Engle, "Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation," Econometrica: Journal of the econometric society, pp. 987-1007, 1982.
[3] T. Bollerslev, "Generalized autoregressive conditional heteroskedasticity," Journal of econometrics, vol. 31, no. 3, pp. 307-327, 1986.
[4] T. H. Naylor, T. G. Seaks and D. W. Wichern, "Box-Jenkins methods: An alternative to econometric models," International Statistical Review/Revue Internationale de Statistique, pp. 123-137, 1972.
[5] Z. Shen, Q. Wan and D. J. Leatham, "Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN," Journal of Risk and Financial Management, vol. 14, no. 7, p. 337, 2021.
[6] H. Y. Kim and C. H. Won, "Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models," Expert Systems with Applications, vol. 103, pp. 25-37, 2018.
[7] R. Zhang, Z. Guo, Y. Meng, S. Wang, S. Li, R. Niu and Y. Li, "Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China," International journal of environmental research and public health, vol. 16, no. 11, p. 6174, 2021.
[8] A. Kochhar, H. Singh, S. Sahoo, P. K. Litoria and B. Pateriya, "Prediction and forecast of pre-monsoon and post-monsoon groundwater level: using deep learning and statistical modelling," Modeling Earth Systems and Environment, vol. 8, no. 2, pp. 2317-2329, 2022.
[9] A. Moghar and M. Hamiche, "Stock market prediction using LSTM recurrent neural network," Procedia Computer Science, vol. 170, pp. 1168-1173, 2020.
[10] B. Yang, S. Sun, J. Li, X. Lin and Y. Tian, "Traffic flow prediction using LSTM with feature enhancement," Neurocomputing, vol. 332, pp. 320-327, 2019.
[11] R. A. Rajagukguk, R. A. Ramadhan and H. J. Lee, "A review on deep learning models for forecasting time series data of solar irradiance and photovoltaic power," Energies, vol. 13, no. 24, p. 6623, 2020.
[12] P. Liu, J. Wang, A. K. Sangaiah, Y. Xie and X. Yin, "Analysis and prediction of water quality using LSTM deep neural networks in IoT environment," Sustainability, vol. 11, no. 7, p. 2058, 2019.
[13] M. Mele, A. R. Gurrieri, G. Morelli and C. Magazzino, "Nature and climate change effects on economic growth: an LSTM experiment on renewable energy resources," Environmental Science and Pollution Research, vol. 28, pp. 41127-41134, 2021.
[14] M. A. R. Suleman and S. Shridevi, "Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model," IEEE Access, vol. 10, pp. 82456-82468, 2022.
[15] Y. Liu, D. Li, S. Wan, F. Wang, W. Dou, X. Xu and L. Qi, "A long short‐term memory‐based model for greenhouse climate prediction," International Journal of Intelligent Systems, vol. 37, no. 1, pp. 135-151, 2022.
[16] D. Lee, G. Lee, S. Kim and S. Jung, "Future runoff analysis in the Mekong river basin under a climate change scenario using deep learning," Water, vol. 12, no. 6, p. 1556, 2020.
[17] M. Gao, J. Li, F. Hong and D. Long, "Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM," Energy, vol. 187, p. 115838, 2019.
[18] X. Qing and Y. Niu, "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, vol. 148, pp. 461-468, 2018.
[19] Z. Karevan and J. A. Suykens, "Transductive LSTM for time-series prediction: An application to weather forecasting," Neural Networks, vol. 125, pp. 1-9, 2020.
[20] Y. Wu, Y. Ding, Y. Zhu, J. Feng and S. Wang, "Complexity to forecast flood: Problem definition and spatiotemporal attention LSTM solution," Complexity, 2020.
[21] M. A. Zaytar and C. El Amrani, "Sequence to sequence weather forecasting with long short-term memory recurrent neural networks," International Journal of Computer Applications, vol. 143, no. 11, pp. 7-11, 2016.
[22] I. Sutskever, O. Vinyals and Q. V. Le, "Sequence to sequence learning with neural networks," Advances in neural information processing systems, p. 27, 2014.
[23] Z. Xiang, J. Yan and I. Demir, "A rainfall‐runoff model with LSTM‐based sequence‐to‐sequence learning," Water resources research, vol. 56, no. 1, p. e2019WR025326, 2020.
[24] J. H. Bappy, C. Simons, L. Nataraj, B. S. Manjunath and . A. K. Roy-Chowdhury, "Hybrid lstm and encoder–decoder architecture for detection of image forgeries," IEEE Transactions on Image Processing, vol. 28, no. 7, pp. 3286-3300, 2019.
[25] Y. Wang, M. Huang, X. Zhu and L. Zhao, "Attention-based LSTM for aspect-level sentiment classification," In Proceedings of the 2016 conference on empirical methods in natural language processing, pp. 606-615, 2016.
[26] J. R. Jiang, J. E. Lee and Y. M. Zeng, "Time series multiple channel convolutional neural network with attention-based long short-term memory for predicting bearing remaining useful life," Sensors, vol. 20, no. 1, p. 166, 2019.
[27] S. Hochreiter and J. Hochreiter, "Long short-term memory," Neural computation, vol. 8, no. 9, pp. 1735-1780, 1977.
[28] F. A. Gers, D. Eck and J. Schmidhuber, "Applying LSTM to time series predictable through time-window approaches," Neural Nets WIRN Vietri-01, pp. 193-200, 2002.
[29] H. Huynh, L. M. Dang and . D. Duong, "A new model for stock price movements prediction using deep neural network," In Proceedings of the Eighth International Symposium on Information and Communication Technology, pp. 57-62, 2017.
[30] X. Zhang, X. Liang, A. Zhiyuli, S. Zhang, R. Xu, Z. Cheng and et al, "AT-LSTM: An attention-based LSTM model for financial time series prediction," In IOP Conference Series: Materials Science and Engineering, vol. 569, no. 5, p. 052037, july 2019.
[31] X. Song, Y. Liu, L. Xue, J. Wang, J. Zhang, J. Wang, Z. Cheng and et al, "Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model," Journal of Petroleum Science and Engineering, vol. 186, 2020.
[32] G. Van Houdt, C. Mosquera, G. Nápoles and et al, "A review on the long short-term memory model," Artif Intell Rev, vol. 53, p. 5929–5955, 2020.
[33] D. S. Reddy and P. Prasad, "Prediction of vegetation dynamics using NDVI time series data and LSTM," Modeling Earth Systems and Environment, vol. 4, no. 1, pp. 409-419, 2018.
[34] N. Zeydalinejad, "Artificial neural networks vis-à-vis MODFLOW in the simulation of groundwater: A review.," Modeling Earth Systems and Environment, pp. 1-22, 2022.
[35] S. Wang, L. Mu and D. Liu, "A hybrid approach for El Niño prediction based on Empirical Mode Decomposition and convolutional LSTM Encoder-Decoder," Computers & Geosciences, vol. 149, p. 104695, 2021.
[36] S. H. Park, B. Kim, C. M. Kang, C. C. Chung and J. W. Choi, "Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture," IEEE Intelligent Vehicles Symposium, pp. 1672-1678, 2018.
[37] M. J. Ellis and V. Chinde, "An encoder–decoder LSTM-based EMPC framework applied to a building HVAC system," Chemical Engineering Research and Design, vol. 160, pp. 508-520, 2020.
[38] B. Zhang, G. Zou, D. Qin, Y. Lu, Y. Jin and H. Wang, "A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction," Science of The Total Environment, vol. 765, p. 144507, 2021.
[39] S. Kim and M. Kang, "Financial series prediction using Attention LSTM," arXiv preprint arXiv, pp. 1902-10877, 2019.
[40] G. Liu and J. Guo, "Bidirectional LSTM with attention mechanism and convolutional layer for text classification," Neurocomputing, pp. 325-338, 2019.
[41] S. Biswas and M. Sinha, "Performances of deep learning models for Indian Ocean wind speed prediction," Modeling Earth Systems and Environment, vol. 7, no. 2, pp. 809-831, 2021.
[42] Y. Li, Z. Zhu, D. Kong, H. Han and Y. Zhao, "EA-LSTM: Evolutionary attention-based LSTM for time series prediction," Knowledge-Based Systems, vol. 181, p. 104785, 2019.
| Style | # |
|---|---|
| MLA | Khder Alakkari , Mostafa Abotaleb , Amr Badr, Ammar Kadi, A. M. Ghazi Al khatib, Bayan Mohamad Alshaib, El-Sayed M. El-kenawy. "Modelling Weather Conditions Using Encoder-Decoder and Attention Based on LSTM Deep Regression Model." International Journal of Advances in Applied Computational Intelligence, Vol. 1, No. 2, 2022 ,PP. 08-29 (Doi : https://doi.org/10.54216/IJAACI.010201) |
| APA | Khder Alakkari , Mostafa Abotaleb , Amr Badr, Ammar Kadi, A. M. Ghazi Al khatib, Bayan Mohamad Alshaib, El-Sayed M. El-kenawy. (2022). Modelling Weather Conditions Using Encoder-Decoder and Attention Based on LSTM Deep Regression Model. Journal of International Journal of Advances in Applied Computational Intelligence, 1 ( 2 ), 08-29 (Doi : https://doi.org/10.54216/IJAACI.010201) |
| Chicago | Khder Alakkari , Mostafa Abotaleb , Amr Badr, Ammar Kadi, A. M. Ghazi Al khatib, Bayan Mohamad Alshaib, El-Sayed M. El-kenawy. "Modelling Weather Conditions Using Encoder-Decoder and Attention Based on LSTM Deep Regression Model." Journal of International Journal of Advances in Applied Computational Intelligence, 1 no. 2 (2022): 08-29 (Doi : https://doi.org/10.54216/IJAACI.010201) |
| Harvard | Khder Alakkari , Mostafa Abotaleb , Amr Badr, Ammar Kadi, A. M. Ghazi Al khatib, Bayan Mohamad Alshaib, El-Sayed M. El-kenawy. (2022). Modelling Weather Conditions Using Encoder-Decoder and Attention Based on LSTM Deep Regression Model. Journal of International Journal of Advances in Applied Computational Intelligence, 1 ( 2 ), 08-29 (Doi : https://doi.org/10.54216/IJAACI.010201) |
| Vancouver | Khder Alakkari , Mostafa Abotaleb , Amr Badr, Ammar Kadi, A. M. Ghazi Al khatib, Bayan Mohamad Alshaib, El-Sayed M. El-kenawy. Modelling Weather Conditions Using Encoder-Decoder and Attention Based on LSTM Deep Regression Model. Journal of International Journal of Advances in Applied Computational Intelligence, (2022); 1 ( 2 ): 08-29 (Doi : https://doi.org/10.54216/IJAACI.010201) |
| IEEE | Khder Alakkari, Mostafa Abotaleb, Amr Badr, Ammar Kadi, A. M. Ghazi Al khatib, Bayan Mohamad Alshaib, El-Sayed M. El-kenawy, Modelling Weather Conditions Using Encoder-Decoder and Attention Based on LSTM Deep Regression Model, Journal of International Journal of Advances in Applied Computational Intelligence, Vol. 1 , No. 2 , (2022) : 08-29 (Doi : https://doi.org/10.54216/IJAACI.010201) |