Probability of SDS Days Prediction in Iran’s Eastern Region Using Spatio-Temporal Indicator Kriging model
One of the most important environmental challenges in the Middle East and Iran in recent years is the increasing SDS phenomenon. In order to forecast the probability of SDS days, wind speed and Horizontal view data in the eastern regions of Iran was investigated using Kriging model of Spatial-Tempor...
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| Format: | Article |
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University of Sistan and Baluchestan
2019-05-01
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| Series: | مخاطرات محیط طبیعی |
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| Online Access: | https://jneh.usb.ac.ir/article_3774_e6230ba3dd95cb799cd673b2ffe9f60c.pdf |
| _version_ | 1797805315749576704 |
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| author | Ahmad Hosseini Bohloul Alijani Yadollah Waghei |
| author_facet | Ahmad Hosseini Bohloul Alijani Yadollah Waghei |
| author_sort | Ahmad Hosseini |
| collection | DOAJ |
| description | One of the most important environmental challenges in the Middle East and Iran in recent years is the increasing SDS phenomenon. In order to forecast the probability of SDS days, wind speed and Horizontal view data in the eastern regions of Iran was investigated using Kriging model of Spatial-Temporal indicator,and R software, in which indicators one and zero were considered for a SDS and for a day without SDS, respectively. Then the SP Data array (Spatial Temporal Data) was constructed with a combination of the matrix and vector in the STFDF class (Spatial Temporal Function Data Frame), and STF class (Spatial Temporal Function). After fitting all the separable and non-separable models, the sum metric variogram with the least average of sum of squares was selected as the best model for fitting data. The output of the model showed that the data enjoy a spatial-temporal dependence to 5 days, so from the last day of the statistical period we can forecast the probability of occurrence of the SDS day for the next 5 days. On the first forecast able day, i.e. 2017/04/01, the critical points of Sarakhs and Fariman stations in Razavi Khorasan province with a probability of 16 and 20 percent, respectively, Zabol, Zahak, Mirjawa, Nosrat Abad, Zahedan and Khash stations in Sistan and Baluchestan provice with 17, 13.13, 19.24 and 17 percent, respectively, and finally Abarkuh, Bafgh and Behabad stations in Yazd province with 20, 16 and 35 percent, respectively, enjoyed the highest probability of occurrence of SDS days. Keywords:Spatial-Temporal Variogram, Predict, SDS Days, Eastern Rregions of Iran, Kriging Indicator, Sand and Dust Storm(SDS), Region, Indicator Kriging Method, SDS. |
| first_indexed | 2024-03-13T05:49:13Z |
| format | Article |
| id | doaj.art-cc1c73f0f19841ce8139fe4054fd1a35 |
| institution | Directory Open Access Journal |
| issn | 2676-4377 2676-4385 |
| language | fas |
| last_indexed | 2024-03-13T05:49:13Z |
| publishDate | 2019-05-01 |
| publisher | University of Sistan and Baluchestan |
| record_format | Article |
| series | مخاطرات محیط طبیعی |
| spelling | doaj.art-cc1c73f0f19841ce8139fe4054fd1a352023-06-13T19:54:32ZfasUniversity of Sistan and Baluchestanمخاطرات محیط طبیعی2676-43772676-43852019-05-0182019721610.22111/jneh.2018.23205.13583774Probability of SDS Days Prediction in Iran’s Eastern Region Using Spatio-Temporal Indicator Kriging modelAhmad Hosseini0Bohloul Alijani1Yadollah Waghei2PHD Student of climatology, Faculty of Geographical Sciences, Kharazmi University, Tehran, IranProfessor of Climatology, Faculty of Geographical Sciences, Kharazmi University, Tehran, IranAssociate Professor of Statistics, Faculty of Statistics Sciences, Birjand University, Birjand, IranOne of the most important environmental challenges in the Middle East and Iran in recent years is the increasing SDS phenomenon. In order to forecast the probability of SDS days, wind speed and Horizontal view data in the eastern regions of Iran was investigated using Kriging model of Spatial-Temporal indicator,and R software, in which indicators one and zero were considered for a SDS and for a day without SDS, respectively. Then the SP Data array (Spatial Temporal Data) was constructed with a combination of the matrix and vector in the STFDF class (Spatial Temporal Function Data Frame), and STF class (Spatial Temporal Function). After fitting all the separable and non-separable models, the sum metric variogram with the least average of sum of squares was selected as the best model for fitting data. The output of the model showed that the data enjoy a spatial-temporal dependence to 5 days, so from the last day of the statistical period we can forecast the probability of occurrence of the SDS day for the next 5 days. On the first forecast able day, i.e. 2017/04/01, the critical points of Sarakhs and Fariman stations in Razavi Khorasan province with a probability of 16 and 20 percent, respectively, Zabol, Zahak, Mirjawa, Nosrat Abad, Zahedan and Khash stations in Sistan and Baluchestan provice with 17, 13.13, 19.24 and 17 percent, respectively, and finally Abarkuh, Bafgh and Behabad stations in Yazd province with 20, 16 and 35 percent, respectively, enjoyed the highest probability of occurrence of SDS days. Keywords:Spatial-Temporal Variogram, Predict, SDS Days, Eastern Rregions of Iran, Kriging Indicator, Sand and Dust Storm(SDS), Region, Indicator Kriging Method, SDS.https://jneh.usb.ac.ir/article_3774_e6230ba3dd95cb799cd673b2ffe9f60c.pdf"spatio-temporal variogram""prediction"" dust storm days"" iran’s eastern region""indicator kriging method""sds" |
| spellingShingle | Ahmad Hosseini Bohloul Alijani Yadollah Waghei Probability of SDS Days Prediction in Iran’s Eastern Region Using Spatio-Temporal Indicator Kriging model مخاطرات محیط طبیعی "spatio-temporal variogram" "prediction" " dust storm days" " iran’s eastern region" "indicator kriging method" "sds" |
| title | Probability of SDS Days Prediction in Iran’s Eastern Region Using Spatio-Temporal Indicator Kriging model |
| title_full | Probability of SDS Days Prediction in Iran’s Eastern Region Using Spatio-Temporal Indicator Kriging model |
| title_fullStr | Probability of SDS Days Prediction in Iran’s Eastern Region Using Spatio-Temporal Indicator Kriging model |
| title_full_unstemmed | Probability of SDS Days Prediction in Iran’s Eastern Region Using Spatio-Temporal Indicator Kriging model |
| title_short | Probability of SDS Days Prediction in Iran’s Eastern Region Using Spatio-Temporal Indicator Kriging model |
| title_sort | probability of sds days prediction in iran s eastern region using spatio temporal indicator kriging model |
| topic | "spatio-temporal variogram" "prediction" " dust storm days" " iran’s eastern region" "indicator kriging method" "sds" |
| url | https://jneh.usb.ac.ir/article_3774_e6230ba3dd95cb799cd673b2ffe9f60c.pdf |
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