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.

Proton exchange membrane for direct methanol fuel cell was developed by the incorporation of silicotungstic acid supported on silica (SiO₂-SiWA) into SPEEK polymer. The properties of composite membranes with various silica/SiWA ratios were investigated and compared in this study. It was found that high silica to SiWA weight ratio (2:1) resulted in porous membrane with high proton conductivity and moderate methanol permeability. Reduction in the aforementioned ratio to 1:1 resulted in a more compact structure with better thermal stability, however, leaching of SiWA from the membrane occurred due to insufficient support. SPEEK membrane incorporated with 10 wt% SiO₂ and 5 wt% SiWA showed the highest selectivity of 6.44 × 10⁴ S.s/cm³ and performed better than that of the pure SPEEK membrane. These results suggested that these membranes have potential to be considered for DMFCs applications.