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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Veröffentlicht in:Ecological informatics Jg. 85; S. 102933
Hauptverfasser: Khosravi, Khabat, Farooque, Aitazaz A., Naghibi, Amir, Heddam, Salim, Sharafati, Ahmad, Hatamiafkoueieh, Javad, Abolfathi, Soroush
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.03.2025
Elsevier
Schlagworte:
algorithms
automation
BA-Kstar
Civil Engineering
data collection
Deep learning
Engineering and Technology
Evaporation
fuzzy logic
Geotechnical Engineering and Engineering Geology
Geoteknik och teknisk geologi
hybrids
Iran
Kermanshah
Machine learning
neural networks
prediction
principal component analysis
rain
relative humidity
Samhällsbyggnadsteknik
Teknik
temperature
uncertainty
Uncertainty analysis
water management
wind speed
Iran
Deep learning
Evaporation
BA-Kstar
Kermanshah
Uncertainty analysis
Machine learning
ISSN:1574-9541
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Zusammenfassung: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. •Ep prediction by tree-, rule-, lazy- and neuron-based models is investigated.•Predictive performance of Decision Tree, ANFIS, LSTM and their ensemble models are examined.•Optimal input scenarios were investigated using feature selection and manual approaches.•BA-Kstar ensemble model exhibited superior performance in Ep prediction.•The uncertainty analysis highlights that input selection impacts Ep more than model selection.
Bibliographie:ObjectType-Article-1
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ISSN:1574-9541
DOI:10.1016/j.ecoinf.2024.102933