Parameter Optimisation-Based Hybrid Reference Evapotranspiration Prediction Models: A Systematic Review of Current Implementations and Future Research Directions

A hybrid machine learning (ML) model is becoming a common trend in predicting reference evapotranspiration (ETo) research. This study aims to systematically review ML models that are integrated with meta-heuristic algorithms (i.e., parameter optimisation-based hybrid models, OBH) for predicting ETo...

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Bibliographic Details
Published in:Atmosphere 2023-01, Vol.14 (1), p.77
Main Authors: Khairan, Hadeel E., Zubaidi, Salah L., Muhsen, Yousif Raad, Al-Ansari, Nadhir
Format: Article
Language:English
Subjects:
Accuracy
Agricultural production
Algorithms
Artificial intelligence
Bibliometrics
Computation
Evapotranspiration
Evapotranspiration models
Evapotranspiration trends
Evolutionary algorithms
Geoteknik
Heuristic
Heuristic methods
hybrid model
Hydrologic cycle
Machine learning
Mathematical models
meta-heuristic algorithms
Modelling
Neural networks
Optimization
Parameters
Physics
Prediction models
Problem solving
reference evapotranspiration
Soil Mechanics
Support vector machines
Swarm intelligence
systematic review
Taxonomy
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Summary:A hybrid machine learning (ML) model is becoming a common trend in predicting reference evapotranspiration (ETo) research. This study aims to systematically review ML models that are integrated with meta-heuristic algorithms (i.e., parameter optimisation-based hybrid models, OBH) for predicting ETo data. Over five years, from 2018–2022, the articles published in three reliable databases, including Web of Science, ScienceDirect, and IEEE Xplore, were considered. According to the protocol search, 1485 papers were selected. After three filters were applied, the final set contained 33 papers related to the nominated topic. The final set of papers was categorised into five groups. The first group, swarm intelligence-based algorithms, had the highest proportion of papers, (23/33) and was superior to all other algorithms. The second group (evolution computation-based algorithms), third group (physics-based algorithms), fourth group (hybrid-based algorithms), and fifth group (reviews and surveys) had (4/33), (1/33), (2/33), and (3/33), respectively. However, researchers have not treated OBH models in much detail, and there is still room for improvement by investigating both newly single and hybrid meta-heuristic algorithms. Finally, this study hopes to assist researchers in understanding the options and gaps in this line of research.
ISSN:2073-4433
2073-4433
DOI:10.3390/atmos14010077