Prediction of dust storm using artificial neural networks in Kermanshah
Dust is a phenomenon with significant environmental impacts across various aspects of human life, including agriculture, economy, health, and more. The purpose of this study is to investigate and predict the dust phenomenon in Kermanshah. Meteorological data with a 3-hour resolution for the statisti...
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| Format: | Article |
| Language: | fas |
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Kharazmi University
2025-09-01
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| Series: | تحقیقات کاربردی علوم جغرافیایی |
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| Online Access: | http://jgs.khu.ac.ir/article-1-4007-en.pdf |
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| author | Toba Alizadeheh Majid rezaie banafsh Gholamreza Goodarzi Hashem Rostamzadeh |
| author_facet | Toba Alizadeheh Majid rezaie banafsh Gholamreza Goodarzi Hashem Rostamzadeh |
| author_sort | Toba Alizadeheh |
| collection | DOAJ |
| description | Dust is a phenomenon with significant environmental impacts across various aspects of human life, including agriculture, economy, health, and more. The purpose of this study is to investigate and predict the dust phenomenon in Kermanshah. Meteorological data with a 3-hour resolution for the statistical period (2000–2020) from the Kermanshah station was obtained from the Meteorological Organization. First, the dust data were normalized, and then Artificial Neural Network (ANN) models were used to predict dust concentration, while the Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed to analyze and predict the time series of dust occurrence in MATLAB software. The findings revealed that the maximum predicted dust concentration, related to the minimum dew point with the highest Pearson correlation with dust, was estimated at 3451.23 µg/m³. Additionally, the results of the time series prediction using the ANFIS model showed that the linear bell membership function with grade 3, during both the training and testing stages, was the most effective input function among other membership functions. According to the forecasting models, the highest probability of maximum dust occurrence in the next 20 years in Kermanshah is 94%. Based on the aforementioned studies, sufficient information was gathered to conduct this research. The phenomenon of dust, particularly in western Iran and the city of Kermanshah, has consistently posed significant challenges for the residents of these areas. This phenomenon is influenced by specific atmospheric conditions that cause irreparable damage annually, leading to respiratory issues and deteriorating air quality. Therefore, it is essential to pay serious attention to the issue of dust. |
| format | Article |
| id | doaj-art-db70d05ba10c456b8eb8953d1b9283a8 |
| institution | Kabale University |
| issn | 2228-7736 2588-5138 |
| language | fas |
| publishDate | 2025-09-01 |
| publisher | Kharazmi University |
| record_format | Article |
| series | تحقیقات کاربردی علوم جغرافیایی |
| spelling | doaj-art-db70d05ba10c456b8eb8953d1b9283a82025-08-20T03:52:52ZfasKharazmi Universityتحقیقات کاربردی علوم جغرافیایی2228-77362588-51382025-09-012578278297Prediction of dust storm using artificial neural networks in KermanshahToba Alizadeheh0Majid rezaie banafsh1Gholamreza Goodarzi2Hashem Rostamzadeh3 Dust is a phenomenon with significant environmental impacts across various aspects of human life, including agriculture, economy, health, and more. The purpose of this study is to investigate and predict the dust phenomenon in Kermanshah. Meteorological data with a 3-hour resolution for the statistical period (2000–2020) from the Kermanshah station was obtained from the Meteorological Organization. First, the dust data were normalized, and then Artificial Neural Network (ANN) models were used to predict dust concentration, while the Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed to analyze and predict the time series of dust occurrence in MATLAB software. The findings revealed that the maximum predicted dust concentration, related to the minimum dew point with the highest Pearson correlation with dust, was estimated at 3451.23 µg/m³. Additionally, the results of the time series prediction using the ANFIS model showed that the linear bell membership function with grade 3, during both the training and testing stages, was the most effective input function among other membership functions. According to the forecasting models, the highest probability of maximum dust occurrence in the next 20 years in Kermanshah is 94%. Based on the aforementioned studies, sufficient information was gathered to conduct this research. The phenomenon of dust, particularly in western Iran and the city of Kermanshah, has consistently posed significant challenges for the residents of these areas. This phenomenon is influenced by specific atmospheric conditions that cause irreparable damage annually, leading to respiratory issues and deteriorating air quality. Therefore, it is essential to pay serious attention to the issue of dust.http://jgs.khu.ac.ir/article-1-4007-en.pdfdustforecastannanfiskermanshah. |
| spellingShingle | Toba Alizadeheh Majid rezaie banafsh Gholamreza Goodarzi Hashem Rostamzadeh Prediction of dust storm using artificial neural networks in Kermanshah تحقیقات کاربردی علوم جغرافیایی dust forecast ann anfis kermanshah. |
| title | Prediction of dust storm using artificial neural networks in Kermanshah |
| title_full | Prediction of dust storm using artificial neural networks in Kermanshah |
| title_fullStr | Prediction of dust storm using artificial neural networks in Kermanshah |
| title_full_unstemmed | Prediction of dust storm using artificial neural networks in Kermanshah |
| title_short | Prediction of dust storm using artificial neural networks in Kermanshah |
| title_sort | prediction of dust storm using artificial neural networks in kermanshah |
| topic | dust forecast ann anfis kermanshah. |
| url | http://jgs.khu.ac.ir/article-1-4007-en.pdf |
| work_keys_str_mv | AT tobaalizadeheh predictionofduststormusingartificialneuralnetworksinkermanshah AT majidrezaiebanafsh predictionofduststormusingartificialneuralnetworksinkermanshah AT gholamrezagoodarzi predictionofduststormusingartificialneuralnetworksinkermanshah AT hashemrostamzadeh predictionofduststormusingartificialneuralnetworksinkermanshah |