干旱区生态环境知识资源中心(Arid): Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate

Arid

DOI	10.3390/w15213822
	Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate
	Raza, Ali; Fahmeed, Romana; Syed, Neyha Rubab; Katipoglu, Okan Mert; Zubair, Muhammad; Alshehri, Fahad; Elbeltagi, Ahmed
通讯作者	Raza, A
来源期刊	WATER
EISSN	2073-4441
出版年	2023
卷号	15 期号:21
英文摘要	The Food and Agriculture Organization recommends that the Penman-Monteith Method contains Equation 56 (PMF) as a widely accepted standard for reference evapotranspiration (ETo) calculation. Despite this, the PMF cannot be employed when meteorological variables are constrained; therefore, alternative models for ETo estimation requiring fewer variables must be chosen, which means that they perform at least as well as, if not better than, the PMF in terms of accuracy and efficiency. This study evaluated five machine learning (ML) algorithms to estimate ETo and compared their results with the standardized PMF. For this purpose, ML models were trained using monthly time series climatic data. The created ML models underwent testing to determine ETo under varying meteorological input combinations. The results of ML models were compared to assess their accuracy and validate their performance using several statistical indicators, errors (root-mean-square (RMSE), mean absolute error (MAE)), model efficiency (NSE), and determination coefficient (R2). The process of evaluating ML models involved the utilization of radar charts, Smith graphs, heatmaps, and bullet charts. Based on our findings, satisfactory results have been obtained using RBFFNN based on M12 input combinations (mean temperature (Tmean), mean relative humidity (RHmean), sunshine hours (Sh)) for ETo estimation. The RBFFNN model exhibited the most precise estimation as RMSE obtained values of 0.30 and 0.22 during the training and testing phases, respectively. In addition, during training and testing, the MAE values for this model were recorded as 0.15 and 0.17, respectively. The highest R2 and NSE values were noted as 0.98 and 0.99 for the RBFNN during performance analysis, respectively. The scatter plots and spatial variations of the RBFNN and PMF in the studied region indicated that the RBFNN had the highest efficacy (R2, NSE) and lowest errors (RMSE, MAE) as compared with the other four ML models. Overall, our study highlights the potential of ML models for ETo estimation in the arid region (Jacobabad), providing vital insights for improving water resource management, helping climate change research, and optimizing irrigation scheduling for optimal agricultural water usage in the region.
英文关键词	reference evapotranspiration artificial intelligence techniques Sindh province prediction comparative assessment limited climatic data
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:001099419800001
WOS关键词	MODELING DAILY REFERENCE ; EQUATIONS ; RADIATION ; ANN ; SYSTEM
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/399098
推荐引用方式 GB/T 7714	Raza, Ali,Fahmeed, Romana,Syed, Neyha Rubab,et al. Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate[J],2023,15(21).
APA	Raza, Ali.,Fahmeed, Romana.,Syed, Neyha Rubab.,Katipoglu, Okan Mert.,Zubair, Muhammad.,...&Elbeltagi, Ahmed.(2023).Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate.WATER,15(21).
MLA	Raza, Ali,et al."Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate".WATER 15.21(2023).