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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| Format: | Article |
| Language: | Arabic |
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University of Sulaimania
2025-08-01
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| Series: | Sulaimani Journal for Engineering Sciences |
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| Online Access: | https://sjes.univsul.edu.iq/article?id=356 |
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| author | Rebin Abdulkareem Hamaamin Omar Mohammed Amin Ali Shahab Wahhab Kareemc |
| author_facet | Rebin Abdulkareem Hamaamin Omar Mohammed Amin Ali Shahab Wahhab Kareemc |
| author_sort | Rebin Abdulkareem Hamaamin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5e1d1dc487a145669c600e26643bd033 |
| institution | DOAJ |
| issn | 2410-1699 2415-6655 |
| language | Arabic |
| publishDate | 2025-08-01 |
| publisher | University of Sulaimania |
| record_format | Article |
| series | Sulaimani Journal for Engineering Sciences |
| spelling | doaj-art-5e1d1dc487a145669c600e26643bd0332025-08-20T03:03:03ZaraUniversity of SulaimaniaSulaimani Journal for Engineering Sciences2410-16992415-66552025-08-011135474https://doi.org/10.17656/sjes.10196Climate Forecasting in Sulaymaniyah City Using Deep Learning TechniquesRebin Abdulkareem Hamaamin0Omar Mohammed Amin Ali1Shahab Wahhab Kareemc2Charmo University- Computer department- Sulaymaniyah - IraqDepartment of Information Technology, Chamchamal Technical Institute, Sulaimani Polytechnic University, Sulaymaniyah, IraqDepartment of Technical Information Systems Engineering, Technical Engineering College, Erbil Polytechnic University, Erbil, IraqA 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.https://sjes.univsul.edu.iq/article?id=356weathertemperatureprediction models weatherbi-lstm modelgru modelgbt modeldeep learning |
| spellingShingle | Rebin Abdulkareem Hamaamin Omar Mohammed Amin Ali Shahab Wahhab Kareemc Climate Forecasting in Sulaymaniyah City Using Deep Learning Techniques Sulaimani Journal for Engineering Sciences weather temperature prediction models weather bi-lstm model gru model gbt model deep learning |
| title | Climate Forecasting in Sulaymaniyah City Using Deep Learning Techniques |
| title_full | Climate Forecasting in Sulaymaniyah City Using Deep Learning Techniques |
| title_fullStr | Climate Forecasting in Sulaymaniyah City Using Deep Learning Techniques |
| title_full_unstemmed | Climate Forecasting in Sulaymaniyah City Using Deep Learning Techniques |
| title_short | Climate Forecasting in Sulaymaniyah City Using Deep Learning Techniques |
| title_sort | climate forecasting in sulaymaniyah city using deep learning techniques |
| topic | weather temperature prediction models weather bi-lstm model gru model gbt model deep learning |
| url | https://sjes.univsul.edu.iq/article?id=356 |
| work_keys_str_mv | AT rebinabdulkareemhamaamin climateforecastinginsulaymaniyahcityusingdeeplearningtechniques AT omarmohammedaminali climateforecastinginsulaymaniyahcityusingdeeplearningtechniques AT shahabwahhabkareemc climateforecastinginsulaymaniyahcityusingdeeplearningtechniques |