干旱区生态环境知识资源中心(Arid): Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header

Arid

DOI	10.3390/agriculture12121971
	Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header
	Pino-Vargas, Edwin; Taya-Acosta, Edgar; Ingol-Blanco, Eusebio; Torres-Rua, Alfonso
通讯作者	Pino-Vargas, E
来源期刊	AGRICULTURE-BASEL
EISSN	2077-0472
出版年	2022
卷号	12 期号:12
英文摘要	Accurately estimating and forecasting evapotranspiration is one of the most important tasks to strengthen water resource management, especially in desert areas such as La Yarada, Tacna, Peru, a region located at the head of the Atacama Desert. In this study, we used temperature, humidity, wind speed, air pressure, and solar radiation from a local weather station to forecast potential evapotranspiration (ETo) using machine learning. The Feedforward Neural Network (Multi-Layered Perceptron) algorithm for prediction was used under two approaches: direct and indirect. In the first one, the ETo is predicted based on historical records, and the second one predicts the climate variables upon which the ETo calculation depends, for which the Penman-Monteith, Hargreaves-Samani, Ritchie, and Turc equations were used. The results were evaluated using statistical criteria to calculate errors, showing remarkable precision, predicting up to 300 days of ETo. Comparing the performance of the approaches and the machine learning used, the results obtained indicate that, despite the similar performance of the two proposed approaches, the indirect approach provides better ETo forecasting capabilities for longer time intervals than the direct approach, whose values of the corresponding metrics are MAE = 0.033, MSE = 0.002, RMSE = 0.043 and RAE = 0.016.
英文关键词	evapotranspiration forecasting machine learning deep learning arid zones
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000900219400001
WOS关键词	EVAPORATION ; CLIMATES ; CROP
WOS类目	Agronomy
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/391714
推荐引用方式 GB/T 7714	Pino-Vargas, Edwin,Taya-Acosta, Edgar,Ingol-Blanco, Eusebio,et al. Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header[J],2022,12(12).
APA	Pino-Vargas, Edwin,Taya-Acosta, Edgar,Ingol-Blanco, Eusebio,&Torres-Rua, Alfonso.(2022).Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header.AGRICULTURE-BASEL,12(12).
MLA	Pino-Vargas, Edwin,et al."Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header".AGRICULTURE-BASEL 12.12(2022).