Droughts are prolonged precipitation-deficient periods, resulting in inadequate water availability and adverse repercussions to crops, animals and humans. Drought forecasting is vital to water resources planning and management in minimizing the negative consequences. Many models have been developed for this purpose and, indeed, it would be a long process for researchers to select the best suited model for their research. A timely, thorough and informative overview of the models' concepts and historical applications would be helpful in preventing researchers from overlooking the potential selection of models and saving them considerable amounts of time on the problem. Thus, this paper aims to review drought forecasting approaches including their input requirements and performance measures, for 2007–2017. The models are categorized according to their respective mechanism: regression analysis, stochastic, probabilistic, artificial intelligence based, hybrids and dynamic modelling. Details of the selected papers, including modelling approaches, authors, year of publication, methods, input variables, evaluation criteria, time scale and type of drought are tabulated for ease of reference. The basic concepts of each approach with key parameters are explained, along with the historical applications, benefits and limitations of the models. Finally, future outlooks and potential modelling techniques are furnished for continuing drought research.

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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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Drought is a harmful and little understood natural hazard. Effective drought prediction is vital for sustainable agricultural activities and water resources management. The support vector regression (SVR) model and two of its enhanced variants, namely, fuzzy-support vector regression (F-SVR) and boosted-support vector regression (BS-SVR) models, for predicting the Standardized Precipitation Evapotranspiration Indices (SPEI) (in this case, SPEI-1, SPEI-3 and SPEI-6, at various timescales) with a lead time of one month, were developed to minimize potential drought impact on oil palm plantations at the downstream end of the Langat River Basin, which has a tropical climate pattern. Observed SPEIs from periods 1976 to 2011 and 2012 to 2015 were used for model training and validation, respectively. By applying the MAE, RMSE, MBE and R2 as model assessments, it was found that the F-SVR model was best with the trend of improving accuracy when the timescale of the SPEIs increased. It was also found that differences in model performance deteriorates with increased timescale of the SPEIs. The outlier reducing effect from the fuzzy concept has better improvement for the SVR-based models compared to the boosting technique in predicting SPEI-1, SPEI-3 and SPEI-6 for a one-month lead time at the downstream of Langat River Basin.