A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm
المؤلفون المشاركون
Ali, Zulfiqar
Hussain, Ijaz
Faisal, Muhammad
Almanjahie, Ibrahim M.
Niaz, Rizwan
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-02-17
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Niaz, Rizwan& Almanjahie, Ibrahim M.& Ali, Zulfiqar& Faisal, Muhammad& Hussain, Ijaz. 2020. A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm. Advances in Meteorology،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1126887
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Niaz, Rizwan…[et al.]. A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm. Advances in Meteorology No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1126887
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Niaz, Rizwan& Almanjahie, Ibrahim M.& Ali, Zulfiqar& Faisal, Muhammad& Hussain, Ijaz. A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm. Advances in Meteorology. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1126887
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1126887
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر