气候变化领域集成服务门户(CCPortal): Monthly precipitation assessments in association with atmospheric circulation indices by using tree-based models

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DOI	10.1007/s11069-020-03946-5
	Monthly precipitation assessments in association with atmospheric circulation indices by using tree-based models
	Sattari M.T.; Shaker Sureh F.; Kahya E.
发表日期	2020
ISSN	0921030X
起始页码	1077
结束页码	1094
卷号	102 期号:3
英文摘要	The Urmia Lake basin is one of the most important basins in Iran, facing many problems due to poor water management and rainfall reduction. Under current circumstances, it becomes critical to have an advanced understanding of rainfall patterns in the basin, setting the motivation of this study. In this research, the mean monthly meteorological data of six synoptic stations of Urmia Lake basin were used (including relative humidity, temperature, minimum–maximum temperature and pressure) and large-scale atmospheric circulation indices (Southern Oscillation Index, North Atlantic Oscillation, Western Mediterranean Oscillation, Mediterranean Oscillation-Gibraltar/Israel and Mediterranean Oscillation-Algiers/Cairo) and sea surface temperatures of the Mediterranean, Black, Caspian, Red seas and Persian Gulf in the period 1988–2016. Various combinations of these variables used as input to the M5 tree and random forest models were selected by Relief algorithm for each month in three scenarios including atmospheric circulation indices, meteorological variables and combination of both. After the implementation of two models with three different scenarios, the evaluation criteria including correlation coefficient (R), mean absolute error and root-mean-square error were calculated and the Taylor diagram for each model was plotted. Our results showed that the M5 tree model performed superior in January, February, March, April, June, September, November and December, while the random forest model did in the remaining months. In addition, the indications of this study showed that the combination of atmospheric circulation indices and meteorological variables used as input to the models mostly constituted improved results. © 2020, Springer Nature B.V.
关键词	Atmospheric circulations Iran M5 tree model Meteorological variables Monthly precipitation Random forest
英文关键词	atmospheric circulation; hydrological modeling; machine learning; precipitation assessment; rainfall; sea surface temperature; Iran; Lake Urmia
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/205924
作者单位	Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran; Department of Agricultural Engineering, Faculty of Agriculture, Ankara University, Ankara, 06110, Turkey; Hydraulics and Water Resources Division, Hydrology Civil Engineering Department, Istanbul Technical University, Istanbul, Turkey
推荐引用方式 GB/T 7714	Sattari M.T.,Shaker Sureh F.,Kahya E.. Monthly precipitation assessments in association with atmospheric circulation indices by using tree-based models[J],2020,102(3).
APA	Sattari M.T.,Shaker Sureh F.,&Kahya E..(2020).Monthly precipitation assessments in association with atmospheric circulation indices by using tree-based models.Natural Hazards,102(3).
MLA	Sattari M.T.,et al."Monthly precipitation assessments in association with atmospheric circulation indices by using tree-based models".Natural Hazards 102.3(2020).