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Estimation of evapotranspiration (ET) is a challenging, yet important task as the ET value can be used to predict many other natural phenomena. In this work, the reference evapotranspiration (ET0) was estimated using the extreme learning machine (ELM) at two meteorological stations located in the northern region of the Straits of Malacca. Optimum designs of the ELM were first determined and it was found that the different number of hidden neurons and activation functions were favourable to various input combinations. In order to enhance the performance of the ELM, the bootstrap aggregating algorithm was integrated to resample the original dataset. However, the performance of bagged-ELM was found to be poorer than the base ELM. This could be attributed to the high stability of the base ELM model whereby the training size already overwhelmed the dimensionality of the problem itself. The bootstrap aggregation data fusion technique produced a “backfire” effect that degraded the accuracy and generalisability of the base ELM model.

Climatic parameters fluctuate dynamically and their turbulences become more significant as the influence of the climate change increases. A robust model that is able to factor in the recent climate change for long-term climatic parameters forecasting is desired to strategically plan for future anthropogenic activities. In this study, two stochastic time series model, namely the seasonal auto-regressive integrated moving average (SARIMA) model and the artificial neural network (ANN) model are used to predict monthly mean temperature (Tmean), relative humidity (RH), wind speed (u) and pan evaporation (Epan) up to 12 months ahead. This study is conducted using data collected from three meteorological stations in the northern region of the Peninsular Malaysia. The stochastic models forecasted the Tmean with the highest accuracy, followed by RH, u and Epan. Besides, despite the increasing time step (from 1 to 12 months), the accuracy of the models remain consistent. However, both of the models are susceptible to the occurrence of extreme climates. In general, the SARIMA model performs better than the ANN model, probably attributed to its ability to consider the seasonality of the climatic data rather than depending solely on black-box computation.