The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Photo of Amir Naghibi

Amir Naghibi

Researcher

Photo of Amir Naghibi

Enhancing Pan evaporation predictions : Accuracy and uncertainty in hybrid machine learning models

Author

  • Khabat Khosravi
  • Aitazaz A. Farooque
  • Amir Naghibi
  • Salim Heddam
  • Ahmad Sharafati
  • Javad Hatamiafkoueieh
  • Soroush Abolfathi

Summary, in English

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.

Department/s

  • Division of Water Resources Engineering
  • LTH Profile Area: Water
  • Centre for Advanced Middle Eastern Studies (CMES)
  • MECW: The Middle East in the Contemporary World

Publishing year

2025-03

Language

English

Publication/Series

Ecological Informatics

Volume

85

Document type

Journal article

Publisher

Elsevier

Topic

  • Geotechnical Engineering and Engineering Geology

Keywords

  • BA-Kstar
  • Deep learning
  • Evaporation
  • Kermanshah
  • Machine learning
  • Uncertainty analysis

Status

Published

ISBN/ISSN/Other

  • ISSN: 1574-9541