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DOI	10.1007/s12665-023-11191-9
	Forecasting precipitation based on teleconnections using machine learning approaches across different precipitation regimes
	Helali, Jalil; Nouri, Milad; Ghaleni, Mehdi Mohammadi; Hosseni, Seyed Asaad; Safarpour, Farshad; Shirdeli, Azim; Paymard, Parisa; Kalantari, Zahra
通讯作者	Nouri, M
来源期刊	ENVIRONMENTAL EARTH SCIENCES
ISSN	1866-6280
EISSN	1866-6299
出版年	2023
卷号	82 期号:21
英文摘要	Precipitation forecasts are of high significance for different disciplines. In this study, precipitation was forecasted using a wide range of teleconnection signals across different precipitation regimes. For this purpose, four sophisticated machine learning algorithms, i.e., the Generalized Regression Neural Network (GRNN), the Multi-Layer Perceptron (MLP), the Multi-Linear Regression (MLR), and the Least Squares Support Vector Machine (LSSVM), were applied to forecast seasonal and annual precipitation in 1- to 6-months lead times. To classify precipitation regimes, precipitation was clustered using percentiles. The indices quantifying El Nino-Southern Oscillation (ENSO) phasing showed the highest association with autumn, spring, and annual precipitation over the studied areas. The MLP and LSSVM algorithms provided satisfactory forecasts for almost all cases. However, our results indicated that the performance of LSSVM decreased in testing data, implying the tendency of this algorithm towards overfitting. The MLP showed a more balanced performance for the training and testing sets. Consequently, MLP seems best suited to be used for forecasting precipitation in our study area. The modeling algorithms provided less reliable forecasts for the regions corresponding to the 10-40th percentiles, mostly located in hyper-arid and arid environments. This underscores the inherent difficulty of precipitation forecasting in the hyper-arid and arid areas, wherein precipitation is very erratic and sparsely distributed. Our findings illustrate that clustering precipitation regimes to consider microclimate seems vital for reliable precipitation forecasting. Moreover, the results seem useful to design preventive drought/flood risk management strategies and to improve food-water security in Iran.
英文关键词	Drought ENSO Microclimate Spatial inconsistency
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001076552000005
WOS关键词	ARTIFICIAL NEURAL-NETWORKS ; SUPPORT VECTOR REGRESSION ; CLIMATE-CHANGE ; RIVER-BASIN ; EXTREME PRECIPITATION ; DISSOLVED-OXYGEN ; SOIL-MOISTURE ; DANUBE RIVER ; EL-NINO ; ENSO
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396133
推荐引用方式 GB/T 7714	Helali, Jalil,Nouri, Milad,Ghaleni, Mehdi Mohammadi,et al. Forecasting precipitation based on teleconnections using machine learning approaches across different precipitation regimes[J],2023,82(21).
APA	Helali, Jalil.,Nouri, Milad.,Ghaleni, Mehdi Mohammadi.,Hosseni, Seyed Asaad.,Safarpour, Farshad.,...&Kalantari, Zahra.(2023).Forecasting precipitation based on teleconnections using machine learning approaches across different precipitation regimes.ENVIRONMENTAL EARTH SCIENCES,82(21).
MLA	Helali, Jalil,et al."Forecasting precipitation based on teleconnections using machine learning approaches across different precipitation regimes".ENVIRONMENTAL EARTH SCIENCES 82.21(2023).