Recent Advances in Evapotranspiration Estimation Using Artificial Intelligence Approaches with a Focus on Hybridization Techniques—A Review

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.

Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework

“…Chia et al [73] noted that T a and R s are the most critical climate variables to estimate ET in semi-arid regions. Our results in Figure 5a agree with their statement; however, RH also needs to be included for enhanced prediction accuracy of ET o .…”

“…At the extreme edge, T a was proposed to be the best alternative to ET o [16] or used as a proxy for ET o [17] in hydroclimatic studies. simulate the complex nonlinear behavior of ET o , E p , and ET a [14,61,[64][65][66][67][68][69][70][71][72][73][74][75]. However, a critical challenge with these existing ML models is that the nonlinear relationship between climatic variables and the ET makes it difficult to account for inherent uncertainties [76].…”

Kerangka Kerja Sistem Kecerdasan Buatan dalam Meningkatkan Kompetensi Sumber Daya Manusia Indonesia

“…Berbagai aspek dan sisi kehidupan sudah menjadi bagian yang tidak terpisahkan. Beberapa sisi kehidupan yang telah diisi dengan kecerdasan buatan, antara lain: industri, ekonomi, pendidikan, social bahkan hingga kedokteran dan kesehatan [3]. Perkiraanya, semua sisi kehidupan kemanusiaan akan menjadi bagian yang tidak terpisahkan dengan kecerdasan buatan [4].…”

Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula

“…When calculating ET using LEO satellites, external input data, such as meteorological data, are generally necessary [28]. In particular, because LEO satellites observe the Earth's surface at specific local times, it is difficult for the instantaneous observation to monitor the environmental conditions all day and all weather [29]. Therefore, due to the high temporal variability of ET, LEO satellite-derived ET has inevitable limitations for routine monitoring of daily ET and surface energy fluxes [11,30].…”

“…Using geostationary orbit (GEO) satellites data can compensate for the limitations associated with the temporal resolution of LEO satellite data. However, it is difficult to resolve the uncertainties of external input data and the data contaminated by weather conditions, including clouds and aerosols [29].…”