TY - JOUR
T1 - Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region
T2 - A comparative study
AU - Elzain, Hussam Eldin
AU - Abdalla, Osman A.
AU - Abdallah, Mohammed
AU - Al-Maktoumi, Ali
AU - Eltayeb, Mohamed
AU - Abba, Sani I.
N1 - Copyright © 2024 Elsevier Ltd. All rights reserved.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - Accurate and reliable estimation of Reference Evapotranspiration (ETo) is crucial for water resources management, hydrological processes, and agricultural production. The FAO-56 Penman-Monteith (FAO-56PM) approach is recommended as the standard model for ETo estimation; nevertheless, the absence of comprehensive meteorological variables at many global locations frequently restricts its implementation. This study compares shallow learning (SL) and deep learning (DL) models for estimating daily ETo against the FAO-56PM approach based on various statistic metrics and graphic tool over a coastal Red Sea region, Sudan. A novel approach of the SL model, the Catboost Regressor (CBR) and three DL models: 1D-Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were adopted and coupled with a semi-supervised pseudo-labeling (PL) technique. Six scenarios were developed regarding different input combinations of meteorological variables such as air temperature (Tmin, Tmax, and Tmean), wind speed (U2), relative humidity (RH), sunshine hours duration (SSH), net radiation (Rn), and saturation vapor pressure deficit (es-ea). The results showed that the PL technique reduced the systematic error of SL and DL models during training for all the scenarios. The input combination of Tmin, Tmax, Tmean, and RH reflected higher performance than other combinations for all employed models. The CBR-PL model demonstrated good generalization abilities to predict daily ETo and was the overall superior model in the testing phase according to prediction accuracy, stability analysis, and less computation cost compared to DL models. Thus, the relatively simple CBR-PL model is highly recommended as a promising tool for predicting daily ETo in coastal regions worldwide which have limited climate data.
AB - Accurate and reliable estimation of Reference Evapotranspiration (ETo) is crucial for water resources management, hydrological processes, and agricultural production. The FAO-56 Penman-Monteith (FAO-56PM) approach is recommended as the standard model for ETo estimation; nevertheless, the absence of comprehensive meteorological variables at many global locations frequently restricts its implementation. This study compares shallow learning (SL) and deep learning (DL) models for estimating daily ETo against the FAO-56PM approach based on various statistic metrics and graphic tool over a coastal Red Sea region, Sudan. A novel approach of the SL model, the Catboost Regressor (CBR) and three DL models: 1D-Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were adopted and coupled with a semi-supervised pseudo-labeling (PL) technique. Six scenarios were developed regarding different input combinations of meteorological variables such as air temperature (Tmin, Tmax, and Tmean), wind speed (U2), relative humidity (RH), sunshine hours duration (SSH), net radiation (Rn), and saturation vapor pressure deficit (es-ea). The results showed that the PL technique reduced the systematic error of SL and DL models during training for all the scenarios. The input combination of Tmin, Tmax, Tmean, and RH reflected higher performance than other combinations for all employed models. The CBR-PL model demonstrated good generalization abilities to predict daily ETo and was the overall superior model in the testing phase according to prediction accuracy, stability analysis, and less computation cost compared to DL models. Thus, the relatively simple CBR-PL model is highly recommended as a promising tool for predicting daily ETo in coastal regions worldwide which have limited climate data.
KW - Coastal region
KW - FAO-56Penman-Monteith approach
KW - Reference evapotranspiration
KW - Semi-supervised PL
KW - SL and DL models
KW - Neural Networks, Computer
KW - Wind
KW - Climate
KW - Temperature
KW - Deep Learning
UR - http://www.scopus.com/inward/record.url?scp=85184989216&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85184989216&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/cde4f5d2-6be6-3cd2-87cf-176d702ad3c6/
U2 - 10.1016/j.jenvman.2024.120246
DO - 10.1016/j.jenvman.2024.120246
M3 - Article
C2 - 38359624
AN - SCOPUS:85184989216
SN - 0301-4797
VL - 354
SP - 120246
JO - Journal of Environmental Management
JF - Journal of Environmental Management
M1 - 120246
ER -