Abstract
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.
Highlights
In 2019, the United Nations [1] reported that world population had reached 7.7 billion
The results showed that the artificial intelligence model outperformed the conventional HS model
This review has outlined the pitfalls of conventional models based on energy balance which included the high dependency on climatic parameters
Summary
In 2019, the United Nations [1] reported that world population had reached 7.7 billion. Organisation (FAO) of the United Nations, in their publication “Crop evapotranspiration—Guidelines for computing crop water requirements—FAO Irrigation and Drainage Paper 56” (FAO56 in short), had followed up and revised the calculation of ET0 and PET based on the PM model [13] This indirectly made the PM model as a standard in estimating ET0 , and it was used in a number of research works as a standard for comparison [14,15,16]. A review article that focuses on the discussion of the application of artificial intelligence modeling is found to be absent in current literature. The future prospect of the use of artificial intelligence in ET estimation is presented in Section 5 as a prelude to the concluding remarks of this review paper
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