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DOI	10.1002/met.1797
	Modelling long term monthly rainfall using geographical inputs: assessing heuristic and geostatistical models
	Kisi, Ozgur; Karimi, Sahar Mohsenzadeh; Shiri, Jalal; Keshavarzi, Ali
通讯作者	Karimi, SM (corresponding author), Univ Tabriz, Water Engn Dept, Fac Agr, Tabriz, Iran.
来源期刊	METEOROLOGICAL APPLICATIONS
ISSN	1350-4827
EISSN	1469-8080
出版年	2019
卷号	26 期号:4 页码:698-710
英文摘要	Modelling rainfall, an important element of the hydrological cycle, is of crucial importance in hydrology, water resources engineering, irrigation scheduling and environmental issues. In the current study, the monthly rainfall amounts were simulated through the least squares support vector machine (LSSVM), the model tree (MT) and the geostatistical kriging approaches using geographical inputs. So, the latitude, longitude, altitude and the periodicity component were introduced as model inputs for simulating monthly rainfall values. Long-term rainfall records from 73 weather stations covering different climatic contexts of Iran were used for developing and evaluating the proposed methodology. Two modelling scenarios, namely the at station and pooled scenarios, were carried out for assessing the capability of the applied models in modelling rainfall values. First, two different heuristic methods, the LSSVM and MT methods, were compared with each other. The results obtained revealed that the LSSVM model produced more accurate results than the MT for the at station scenario. Both methods gave better results in arid/semi-arid regions than humid stations. In the pooled scenario, the rainfall data of humid and arid training stations were pooled and used for calibration and then tested at each station. In this scenario, the LSSVM was superior to the MT in modelling long-term monthly rainfall. The LSSVM and MT models were then compared with the geostatistical kriging method in both scenarios. It was observed that the kriging method generally performed better than the heuristic methods in spatial modelling of rainfall.
英文关键词	kriging LSSVM MT rainfall
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000525751800015
WOS关键词	SPATIAL INTERPOLATION ; AIR-TEMPERATURE ; PRECIPITATION ; PREDICTION ; WAVELET ; VARIABILITY ; UNCERTAINTY ; GIS
WOS类目	Meteorology & Atmospheric Sciences
WOS研究方向	Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/369595
作者单位	[Kisi, Ozgur] Ilia State Univ, Fac Nat Sci & Engn, Tbilisi, Georgia; [Karimi, Sahar Mohsenzadeh; Shiri, Jalal] Univ Tabriz, Water Engn Dept, Fac Agr, Tabriz, Iran; [Keshavarzi, Ali] Univ Tehran, Lab Remote Sensing & GIS, Dept Soil Sci, Karaj, Iran
推荐引用方式 GB/T 7714	Kisi, Ozgur,Karimi, Sahar Mohsenzadeh,Shiri, Jalal,et al. Modelling long term monthly rainfall using geographical inputs: assessing heuristic and geostatistical models[J],2019,26(4):698-710.
APA	Kisi, Ozgur,Karimi, Sahar Mohsenzadeh,Shiri, Jalal,&Keshavarzi, Ali.(2019).Modelling long term monthly rainfall using geographical inputs: assessing heuristic and geostatistical models.METEOROLOGICAL APPLICATIONS,26(4),698-710.
MLA	Kisi, Ozgur,et al."Modelling long term monthly rainfall using geographical inputs: assessing heuristic and geostatistical models".METEOROLOGICAL APPLICATIONS 26.4(2019):698-710.