A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm
Spatial distribution of meteorological stations has a significant role in hydrological research. The meteorological data play a significant role in drought monitoring; in this regard, accurate and suitable provision of meteorological stations is becoming crucial to improve and strengthen the skill o...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2020/5014280 |
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| author | Rizwan Niaz Ibrahim M. Almanjahie Zulfiqar Ali Muhammad Faisal Ijaz Hussain |
| author_facet | Rizwan Niaz Ibrahim M. Almanjahie Zulfiqar Ali Muhammad Faisal Ijaz Hussain |
| author_sort | Rizwan Niaz |
| collection | DOAJ |
| description | Spatial distribution of meteorological stations has a significant role in hydrological research. The meteorological data play a significant role in drought monitoring; in this regard, accurate and suitable provision of meteorological stations is becoming crucial to improve and strengthen the skill of drought prediction. In this perspective, the choice of meteorological stations in a specific region has substantial importance for accurate estimation and continuous monitoring of drought hazards at the regional level. However, installation and data mining on a large number of meteorological stations require high cost and resources. Therefore, it is necessary to rank and find dependencies among existing meteorological stations in a particular region for further climatological analysis and reanalysis of databases. In this paper, the Monte Carlo feature selection and interdependency discovery (MCFS-ID) algorithm-based framework is proposed to identify the important meteorological station in a particular region. We applied the proposed framework on 12 meteorological stations situated in varying climatological regions of Punjab (Pakistan). We employed the drought index SPTI on 1-, 3-, 6-, 9-, 12-, 24-, and 48-month time-scale data to find the interdependencies among meteorological stations at various locations. We found that Sialkot has significance regional importance for studying SPTI-3, SPTI-6, and SPTI-48 indices. This regional importance is based on scores of relative importance (RI); for example, the RI values for SPTI-3, SPTI-6, and SPTI-48 indices are 0.1570, 0.1080, and 0.0270, respectively. Furthermore, the Jhelum station has more relative importance (RI = 0.1410 and 0.1030) for SPTI-1 and SPTI-9 indices, while varying concentration behaviour is observed in the remaining time scales. |
| format | Article |
| id | doaj-art-f961a31119b44b06a2a6b012a467ff2a |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-f961a31119b44b06a2a6b012a467ff2a2025-02-03T01:27:25ZengWileyAdvances in Meteorology1687-93091687-93172020-01-01202010.1155/2020/50142805014280A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) AlgorithmRizwan Niaz0Ibrahim M. Almanjahie1Zulfiqar Ali2Muhammad Faisal3Ijaz Hussain4Department of Statistics, Quaid-I-Azam University, Islamabad, PakistanStatistical Research and Studies Support Unit, King Khalid University, Abha, Saudi ArabiaDepartment of Statistics, Quaid-I-Azam University, Islamabad, PakistanFaculty of Health Studies, University of Bradford, Bradford, UKDepartment of Statistics, Quaid-I-Azam University, Islamabad, PakistanSpatial distribution of meteorological stations has a significant role in hydrological research. The meteorological data play a significant role in drought monitoring; in this regard, accurate and suitable provision of meteorological stations is becoming crucial to improve and strengthen the skill of drought prediction. In this perspective, the choice of meteorological stations in a specific region has substantial importance for accurate estimation and continuous monitoring of drought hazards at the regional level. However, installation and data mining on a large number of meteorological stations require high cost and resources. Therefore, it is necessary to rank and find dependencies among existing meteorological stations in a particular region for further climatological analysis and reanalysis of databases. In this paper, the Monte Carlo feature selection and interdependency discovery (MCFS-ID) algorithm-based framework is proposed to identify the important meteorological station in a particular region. We applied the proposed framework on 12 meteorological stations situated in varying climatological regions of Punjab (Pakistan). We employed the drought index SPTI on 1-, 3-, 6-, 9-, 12-, 24-, and 48-month time-scale data to find the interdependencies among meteorological stations at various locations. We found that Sialkot has significance regional importance for studying SPTI-3, SPTI-6, and SPTI-48 indices. This regional importance is based on scores of relative importance (RI); for example, the RI values for SPTI-3, SPTI-6, and SPTI-48 indices are 0.1570, 0.1080, and 0.0270, respectively. Furthermore, the Jhelum station has more relative importance (RI = 0.1410 and 0.1030) for SPTI-1 and SPTI-9 indices, while varying concentration behaviour is observed in the remaining time scales.http://dx.doi.org/10.1155/2020/5014280 |
| spellingShingle | Rizwan Niaz Ibrahim M. Almanjahie Zulfiqar Ali Muhammad Faisal Ijaz Hussain A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm Advances in Meteorology |
| title | A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm |
| title_full | A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm |
| title_fullStr | A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm |
| title_full_unstemmed | A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm |
| title_short | A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm |
| title_sort | novel framework for selecting informative meteorological stations using monte carlo feature selection mcfs algorithm |
| url | http://dx.doi.org/10.1155/2020/5014280 |
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