Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate
Author:
Raza Ali12ORCID, Fahmeed Romana3, Syed Neyha Rubab4, Katipoğlu Okan Mert5ORCID, Zubair Muhammad6, Alshehri Fahad2, Elbeltagi Ahmed7ORCID
Affiliation:
1. School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China 2. Abdullah Alrushaid Chair for Earth Science Remote Sensing Research, Geology and Geophysics Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia 3. Department of Political Science, Bahauddin Zakariya University, Multan 60000, Pakistan 4. School of Energy & Environment, Power Engineering & Engineering Thermophysics, Southeast University, Nanjing 210096, China 5. Department of Civil Engineering, Faculty of Engineering and Architecture, Erzincan Binali Yıldırım University, 24002 Erzincan, Türkiye 6. School of Transportation, Southeast University, Nanjing 210096, China 7. Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt
Abstract
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.
Funder
Abdullah Alrushaid Chair for Earth Science Remote Sensing Research
Subject
Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry
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