Journal
AGRONOMY-BASEL
Volume 10, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/agronomy10010101
Keywords
hydrological process; hybrid model; data fusion; ensemble modeling; data decomposition; remote sensing; bootstrap aggregating; Bayesian modeling; boosting algorithm; nonlinear neural ensemble
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Funding
- Universiti Tunku Abdul Rahman (UTAR), Malaysia through Universiti Tunku Abdul Rahman Research Fund [IPSR/RMC/UTARRF/2018-C2/K03]
- UTAR
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Difficulties are faced when formulating hydrological processes, including that of evapotranspiration (ET). Conventional empirical methods for formulating these possess some shortcomings. The artificial intelligence approach emerges as the best possible solution to map the relationships between climatic parameters and ET, even with limited knowledge of the interactions between variables. This review presents the state-of-the-art application of artificial intelligence models in ET estimation, along with different types and sources of data. This paper discovers the most significant climatic parameters for different climate patterns. The characteristics of the basic artificial intelligence models are also explored in this review. To overcome the pitfalls of the individual models, hybrid models which use techniques such as data fusion and ensemble modeling, data decomposition as well as remote sensing-based hybridization, are introduced. In particular, the principles and applications of the hybridization techniques, as well as their combinations with basic models, are explained. The review covers most of the related and excellent papers published from 2011 to 2019 to keep its relevancy in terms of time frame and field of study. Guidelines for the future prospects of ET estimation in research are advocated. It is anticipated that such work could contribute to the development of agriculture-based economy.
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