干旱区生态环境知识资源中心(Arid): Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)

Arid

DOI	10.1016/j.agwat.2020.106386
	Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)
	Yin, Juan; Deng, Zhen; Ines, Amor V. M.; Wu, Junbin; Rasu, Eeswaran
通讯作者	Ines, AVM
来源期刊	AGRICULTURAL WATER MANAGEMENT
ISSN	0378-3774
EISSN	1873-2283
出版年	2020
卷号	242
英文摘要	As the standard method to compute reference evapotranspiration (ET0), Penman-Monteith (PM) method requires eight meteorological input variables, which makes it difficult to apply in data scarce regions. To overcome this problem, a hybrid bi-directional long short-term memory (Bi-LSTM) model was developed to forecast short-term (1-7-day lead time) daily ET0. The model was trained, validated and tested using three meteorological variables for the period of 2006-2018 at selected three meteorological stations located in the semi-arid region of central Ningxia, China. The performance of the hybrid Bi-LSTM model to forecast short-term daily ET0 was evaluated against daily ET0 calculated by the Penman-Monteith method using the statistical metrics namely, mean absolute error (MAE), root mean square error (RMSE), Pearson's correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE). The results showed that the hybrid Bi-LSTM model with a combination of three meteorological inputs (maximum temperature, minimum temperature and sunshine duration) provides the best forecast performance for short-term daily ET0 at the selected meteorological stations. When averaged across stations, the statistical performance at different forecast lead time were as follows; 1-day lead time: RMSE = 0.159 mm day(-1), MAE = 0.039 mm day(-1), R = 0.992, NSE = 0.988; 4-day lead time: RMSE = 0.247 mm day(-1), MAE = 0.075 mm day(-1), R = 0.972, NSE = 0.985 and 7-day lead time: RMSE = 0.323 mm day(-1), MAE = 0.089 mm day(-1), R = 0.943, NSE = 0.982. Moreover, the hybrid Bi-LSTM model consistently improved the forecast performance of short-term daily ET0 compared to the adjusted Hargreaves-Samani (HS) method and the general Bi-LSTM model. The hybrid Bi-LSTM model developed in this study is currently integrated into the modern intelligent irrigation system of 30 ha of Lycium barbarum plantation in central Ningxia in China, a region with limited meteorological data. It is recommended however that the hybrid Bi-LSTM should be evaluated across a wide range of climatic conditions in different regions of the world.
英文关键词	Artificial neural network Hybrid Bi-LSTM Penman-Monteith (PM) method Reference evapotranspiration (ET0) Sunshine duration Temperature
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000571967500002
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; CROP EVAPOTRANSPIRATION ; PENMAN-MONTEITH ; POTENTIAL EVAPOTRANSPIRATION ; EMPIRICAL EQUATIONS ; SPEED PREDICTION ; PAN-EVAPORATION ; HARGREAVES ; COEFFICIENTS ; PERFORMANCE
WOS类目	Agronomy ; Water Resources
WOS研究方向	Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/326451
作者单位	[Yin, Juan; Deng, Zhen; Wu, Junbin] Ningxia Univ, Coll Civil & Hydraul Engn, Yinchuan 750021, Ningxia, Peoples R China; [Deng, Zhen] Ningxia Univ, Coll Informat Engn, Yinchuan 750021, Ningxia, Peoples R China; [Yin, Juan] Engn Res Ctr Efficient Utilizat Water Resources M, Yinchuan 750021, Ningxia, Peoples R China; [Yin, Juan] Ningxia Res Ctr Technol Water Saving Irrigat & Wa, Yinchuan 750021, Ningxia, Peoples R China; [Yin, Juan; Ines, Amor V. M.; Rasu, Eeswaran] Michigan State Univ, Dept Plant Soil & Microbial Sci, E Lansing, MI 48824 USA; [Ines, Amor V. M.] Michigan State Univ, Dept Biosyst & Agr Engn, E Lansing, MI 48824 USA
推荐引用方式 GB/T 7714	Yin, Juan,Deng, Zhen,Ines, Amor V. M.,et al. Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)[J],2020,242.
APA	Yin, Juan,Deng, Zhen,Ines, Amor V. M.,Wu, Junbin,&Rasu, Eeswaran.(2020).Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM).AGRICULTURAL WATER MANAGEMENT,242.
MLA	Yin, Juan,et al."Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)".AGRICULTURAL WATER MANAGEMENT 242(2020).