Comparing the accuracy of regression and artificial intelligence methods in estimating daily speed of wind in the Sistan region
This paper aims at comparing the accuracy of regression methods, artificial intelligence methods, and phase-neurotic interpretation method in estimating wind speed in the Sistan region. To this end, we used the daily weather information obtained from Zabol synoptic stations during a five-year period...
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| Format: | Article |
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Iranian Scientific Association of Desert Management and Control (ISADMC)
2017-02-01
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| Series: | مدیریت بیابان |
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| Online Access: | http://www.jdmal.ir/article_24664_e53e2b0c1bdc4b361e0808f55caf8d13.pdf |
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| author | Afrooz Abtin Hossein Piri Sahragard Ahmad Pahlavanravi Jamshid Piri |
| author_facet | Afrooz Abtin Hossein Piri Sahragard Ahmad Pahlavanravi Jamshid Piri |
| author_sort | Afrooz Abtin |
| collection | DOAJ |
| description | This paper aims at comparing the accuracy of regression methods, artificial intelligence methods, and phase-neurotic interpretation method in estimating wind speed in the Sistan region. To this end, we used the daily weather information obtained from Zabol synoptic stations during a five-year period (2010-2015). MATLAB software was used for modeling based on artificial neural network. On the other hand, DATA FIT software was used for modeling based on regression methods. Methods’ accuracies were estimated using error square mean statistics, comparison indexes, and error mean. Based on sensitivity analysis results; variables such as daily temperature mean, mean relative humidity, sunshine hours, and evaporation from pool were regarded as input variables of regression and artificial intelligence methods. Wind speed was considered as output variable. Based on the results, mean daily temperature and mean relative humidity had the most and the least effect on wind speed in Sistan (0.42 and 0.25 respectively). Neurophasic method with Gaussian function was the most accurate method in estimating wind speed (error squares mean of 2.56). The same statistic for regression method is 4.44. The correlation of regression method (0.45 and 0.51) is less than those of multilayer Perceptron method and Neuro-phasic method (0.51 and 0.52). So, it is suggested that Neurophasic method be used for more accurate estimating wind speed in Sistan region. With accurate estimation of this variable, we can hinder the devastative effects of wind and use it as an effective source of energy. |
| first_indexed | 2024-04-10T06:16:50Z |
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| id | doaj.art-fd8fa7601ae04b6f9d70d0734bba29f0 |
| institution | Directory Open Access Journal |
| issn | 2476-3985 2476-3721 |
| language | fas |
| last_indexed | 2024-04-10T06:16:50Z |
| publishDate | 2017-02-01 |
| publisher | Iranian Scientific Association of Desert Management and Control (ISADMC) |
| record_format | Article |
| series | مدیریت بیابان |
| spelling | doaj.art-fd8fa7601ae04b6f9d70d0734bba29f02023-03-02T06:44:12ZfasIranian Scientific Association of Desert Management and Control (ISADMC)مدیریت بیابان2476-39852476-37212017-02-0148849510.22034/jdmal.2017.2466424664Comparing the accuracy of regression and artificial intelligence methods in estimating daily speed of wind in the Sistan regionAfrooz Abtin0Hossein Piri Sahragard1Ahmad Pahlavanravi2Jamshid Piri3MSc Graduate, College of Soil and Water, University of Zabol, Zabol, IranAssistant Professor, College of Soil and Water, University of Zabol, Zabol, IranAssociate Professor, College of Soil and Water, University of Zabol, Zabol, IranMSc Graduate, College of Soil and Water, University of Zabol, Zabol, IranThis paper aims at comparing the accuracy of regression methods, artificial intelligence methods, and phase-neurotic interpretation method in estimating wind speed in the Sistan region. To this end, we used the daily weather information obtained from Zabol synoptic stations during a five-year period (2010-2015). MATLAB software was used for modeling based on artificial neural network. On the other hand, DATA FIT software was used for modeling based on regression methods. Methods’ accuracies were estimated using error square mean statistics, comparison indexes, and error mean. Based on sensitivity analysis results; variables such as daily temperature mean, mean relative humidity, sunshine hours, and evaporation from pool were regarded as input variables of regression and artificial intelligence methods. Wind speed was considered as output variable. Based on the results, mean daily temperature and mean relative humidity had the most and the least effect on wind speed in Sistan (0.42 and 0.25 respectively). Neurophasic method with Gaussian function was the most accurate method in estimating wind speed (error squares mean of 2.56). The same statistic for regression method is 4.44. The correlation of regression method (0.45 and 0.51) is less than those of multilayer Perceptron method and Neuro-phasic method (0.51 and 0.52). So, it is suggested that Neurophasic method be used for more accurate estimating wind speed in Sistan region. With accurate estimation of this variable, we can hinder the devastative effects of wind and use it as an effective source of energy.http://www.jdmal.ir/article_24664_e53e2b0c1bdc4b361e0808f55caf8d13.pdfsensitivity analysiserror squares meanneuro-phasic methoddetection coefficient |
| spellingShingle | Afrooz Abtin Hossein Piri Sahragard Ahmad Pahlavanravi Jamshid Piri Comparing the accuracy of regression and artificial intelligence methods in estimating daily speed of wind in the Sistan region مدیریت بیابان sensitivity analysis error squares mean neuro-phasic method detection coefficient |
| title | Comparing the accuracy of regression and artificial intelligence methods in estimating daily speed of wind in the Sistan region |
| title_full | Comparing the accuracy of regression and artificial intelligence methods in estimating daily speed of wind in the Sistan region |
| title_fullStr | Comparing the accuracy of regression and artificial intelligence methods in estimating daily speed of wind in the Sistan region |
| title_full_unstemmed | Comparing the accuracy of regression and artificial intelligence methods in estimating daily speed of wind in the Sistan region |
| title_short | Comparing the accuracy of regression and artificial intelligence methods in estimating daily speed of wind in the Sistan region |
| title_sort | comparing the accuracy of regression and artificial intelligence methods in estimating daily speed of wind in the sistan region |
| topic | sensitivity analysis error squares mean neuro-phasic method detection coefficient |
| url | http://www.jdmal.ir/article_24664_e53e2b0c1bdc4b361e0808f55caf8d13.pdf |
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