International Journal of Advances in Applied Computational Intelligence
Journal DOI
2833-5600ISSN (Online)
Volume 1 , Issue 2 , PP: 08-29, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Khder Alakkari 1 * , Mostafa Abotaleb 2 , Amr Badr 3 , Ammar Kadi 4 , A. M. Ghazi Al khatib 5 , Bayan Mohamad Alshaib 6 , El-Sayed M. El-kenawy 7
Doi: https://doi.org/10.54216/IJAACI.010201
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.
Smart Cities , Weather , Time series , Forecasting , (Encoder-decoder) LSTM , Attention LSTM.
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