Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models
Pan Evaporation (Ep) plays a pivotal role in water resource management, particularly in arid and semi-arid regions. This study assesses the predictive performance of a comprehensive range of advanced machine learning (ML) and deep learning (DL) algorithms for Ep prediction using readily available en...
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Elsevier
2025-03-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124004758 |
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| author | Khabat Khosravi Aitazaz A. Farooque Amir Naghibi Salim Heddam Ahmad Sharafati Javad Hatamiafkoueieh Soroush Abolfathi |
| author_facet | Khabat Khosravi Aitazaz A. Farooque Amir Naghibi Salim Heddam Ahmad Sharafati Javad Hatamiafkoueieh Soroush Abolfathi |
| author_sort | Khabat Khosravi |
| collection | DOAJ |
| description | Pan Evaporation (Ep) plays a pivotal role in water resource management, particularly in arid and semi-arid regions. This study assesses the predictive performance of a comprehensive range of advanced machine learning (ML) and deep learning (DL) algorithms for Ep prediction using readily available environmental sensing data. The models investigated include M5 Prime (M5P), M5Rule (M5R), Kstar, as well as their hybridized versions employing Bagging (BA), the adaptive neuro-fuzzy inference system (ANFIS), ANFIS-GA (genetic algorithm), and long short-term memory (LSTM) networks. A 30-year dataset of monthly meteorological observations (1988–2018) from the Kermanshah synoptic station in Iran served as the basis for this analysis, incorporating variables such as temperature, relative humidity, solar exposure, wind speed, and rainfall. Eight input scenarios were developed using both manual and automated feature selection techniques, including correlation-based subset selection evaluation (CfsSubsetEval or CSE), Principal Component Analysis (PCA), and the Relief Attribute Evaluator (RAE). The results demonstrate that the BA-Kstar ensemble model achieved superior performance (R2 = 0.91, RMSE = 1.60, NSE = 0.91, and RSR = 0.30). Notably, manually constructed input scenarios outperformed automated feature selection methods, with maximum temperature emerging as the most significant predictor of Ep variability. This study underscores the reliability and efficacy of hybrid ML models for Ep forecasting, with significant implications for their broader application in diverse climates and geographical regions. |
| format | Article |
| id | doaj-art-9997abb7b5e9467ebeca5dbd8aeca7e5 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-9997abb7b5e9467ebeca5dbd8aeca7e52025-01-19T06:24:34ZengElsevierEcological Informatics1574-95412025-03-0185102933Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning modelsKhabat Khosravi0Aitazaz A. Farooque1Amir Naghibi2Salim Heddam3Ahmad Sharafati4Javad Hatamiafkoueieh5Soroush Abolfathi6Canadian Center for Climate Change and Adaptation, University of Prince Edward Island, St Peters Bay, Canada; Corresponding author.Canadian Center for Climate Change and Adaptation, University of Prince Edward Island, St Peters Bay, Canada; Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A4P3, Canada; Corresponding author at: Canadian Center for Climate Change and Adaptation, University of Prince Edward Island, St Peters Bay, Canada.Department of Water Resources Engineering & Center for Advanced Middle Eastern Studies, Lund University, Lund, SwedenFaculty of Science, Agronomy Department, Hydraulics Division, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, AlgeriaDepartment of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran; New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah 64001, IraqDepartment of Mechanics and Control Processes, Academy of Engineering, Peoples' Friendship University of Russia, RUDN University, Miklukho-Maklaya Str. 6, Moscow 117198, Russian FederationSchool of Engineering, University of Warwick, CV4 7AL Coventry, UKPan Evaporation (Ep) plays a pivotal role in water resource management, particularly in arid and semi-arid regions. This study assesses the predictive performance of a comprehensive range of advanced machine learning (ML) and deep learning (DL) algorithms for Ep prediction using readily available environmental sensing data. The models investigated include M5 Prime (M5P), M5Rule (M5R), Kstar, as well as their hybridized versions employing Bagging (BA), the adaptive neuro-fuzzy inference system (ANFIS), ANFIS-GA (genetic algorithm), and long short-term memory (LSTM) networks. A 30-year dataset of monthly meteorological observations (1988–2018) from the Kermanshah synoptic station in Iran served as the basis for this analysis, incorporating variables such as temperature, relative humidity, solar exposure, wind speed, and rainfall. Eight input scenarios were developed using both manual and automated feature selection techniques, including correlation-based subset selection evaluation (CfsSubsetEval or CSE), Principal Component Analysis (PCA), and the Relief Attribute Evaluator (RAE). The results demonstrate that the BA-Kstar ensemble model achieved superior performance (R2 = 0.91, RMSE = 1.60, NSE = 0.91, and RSR = 0.30). Notably, manually constructed input scenarios outperformed automated feature selection methods, with maximum temperature emerging as the most significant predictor of Ep variability. This study underscores the reliability and efficacy of hybrid ML models for Ep forecasting, with significant implications for their broader application in diverse climates and geographical regions.http://www.sciencedirect.com/science/article/pii/S1574954124004758EvaporationMachine learningDeep learningBA-KstarUncertainty analysisKermanshah |
| spellingShingle | Khabat Khosravi Aitazaz A. Farooque Amir Naghibi Salim Heddam Ahmad Sharafati Javad Hatamiafkoueieh Soroush Abolfathi Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models Ecological Informatics Evaporation Machine learning Deep learning BA-Kstar Uncertainty analysis Kermanshah |
| title | Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models |
| title_full | Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models |
| title_fullStr | Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models |
| title_full_unstemmed | Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models |
| title_short | Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models |
| title_sort | enhancing pan evaporation predictions accuracy and uncertainty in hybrid machine learning models |
| topic | Evaporation Machine learning Deep learning BA-Kstar Uncertainty analysis Kermanshah |
| url | http://www.sciencedirect.com/science/article/pii/S1574954124004758 |
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