Climate Forecasting in Sulaymaniyah City Using Deep Learning Techniques
A neural network model was used to categories and predict the weather in Sulaymaniyah, which is located in the Kurdistan Region of Iraq. The use of neural network models (NNM) for climate data analysis has advanced; the accuracy and application of neural network models for climate data analysis have...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | Arabic |
| Published: |
University of Sulaimania
2025-08-01
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| Series: | Sulaimani Journal for Engineering Sciences |
| Subjects: | |
| Online Access: | https://sjes.univsul.edu.iq/article?id=356 |
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| Summary: | A neural network model was used to categories and predict the weather in Sulaymaniyah, which is located in the Kurdistan Region of Iraq. The use of neural network models (NNM) for climate data analysis has advanced; the accuracy and application of neural network models for climate data analysis have significantly advanced in recent years. Using NNM affected the prediction; the dataset-based forecasting approach uses a single-column time series to depict the future. A real-time weather station dataset from the Sulaimani Meteorological and Seismological Directorate is used in the implementation model. The data collection includes implementation data from the weather station for an iterative neural network model that forecasts future climate and displays historical propagation. From 1993 to 2023, the dataset includes daily data on average temperature, humidity percentage, and precipitation for each of the twelve months from January to December. It was gathered every day for thirty years and includes information about Suleimani City's maximum and minimum temperatures, average temperatures, humidity levels, and rainfall over that time. The models were used to predict relative average temperatures. As the results indicate that Bi-LSTM and GRU outperformed GBT in both training and testing. |
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| ISSN: | 2410-1699 2415-6655 |