气候变化领域集成服务门户(CCPortal): Deep-learning GIS hybrid approach in precipitation modeling based on spatio-temporal variables in the coastal zone of Turkey

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DOI	10.3354/CR01612
	Deep-learning GIS hybrid approach in precipitation modeling based on spatio-temporal variables in the coastal zone of Turkey
	Apaydin H.; Sattari M.T.
发表日期	2020
ISSN	0936-577X
起始页码	149
结束页码	165
卷号	81
英文摘要	It is clearly known that precipitation is essential for fauna and flora. Studies have shown that location and temporal factors have an effect on precipitation. Accurate prediction of precipitation is very important for water resource management, and artificial intelligence methods are frequently used to make such predictions. In this study, the deep-learning and geographic information system (GIS) hybrid approach based on spatio-temporal variables was applied in order to model the amount of precipitation on Turkey’s coastline. Information about latitude, longitude, altitude, distance to the sea, and aspect was taken from meteorological stations, and these factors were utilized as spatial variables. The change in monthly precipitation was taken into account as a temporal variable. Artificial intelligence methods such as Gaussian process regression, support vector regression, the Broyden-Fletcher-Goldfarb-Shanno artificial neural network, M5, random forest, and long short-term memory (LSTM) were used. According to the results of the study, in which different input variable alternatives were also evaluated, LSTM was the most successful method for predicting precipitation with a value of 0.93 R. The study shows that the amount of precipitation can be estimated and a distribution map can be drawn by using spatio-temporal data and the deep-learning and GIS hybrid method at points where the measurement is not performed. © Inter-Research 2020.
英文关键词	Artificial intelligence; BFGS-ANN; Deep learning; East Mediterranean; GPR; LSTM; M5; Precipitation; Rainfall forecasting; RF; Spatio-temporal variables; SVR
语种	英语
scopus关键词	algorithm; artificial neural network; coastal zone; GIS; modeling; precipitation (climatology); spatial variation; spatiotemporal analysis; Turkey
来源期刊	Climate Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/146874
作者单位	Department of Agricultural Engineering, Faculty of Agriculture, Ankara University, Ankara, 06110, Turkey; Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, 51666, Iran; Institute of Research and Development, Duy Tan University, Danang, 550000, Viet Nam
推荐引用方式 GB/T 7714	Apaydin H.,Sattari M.T.. Deep-learning GIS hybrid approach in precipitation modeling based on spatio-temporal variables in the coastal zone of Turkey[J],2020,81.
APA	Apaydin H.,&Sattari M.T..(2020).Deep-learning GIS hybrid approach in precipitation modeling based on spatio-temporal variables in the coastal zone of Turkey.Climate Research,81.
MLA	Apaydin H.,et al."Deep-learning GIS hybrid approach in precipitation modeling based on spatio-temporal variables in the coastal zone of Turkey".Climate Research 81(2020).