Browsing by Author "Ahmad Khairi Abd Wahab"

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    Changes in reference evapotranspiration and its driving factors in peninsular Malaysia
    (Elsevier, 2020) Sahar Hadi Pour; Ahmad Khairi Abd Wahab; Shamsuddin Shahid; Zulhilmi Bin Ismail
    Trends in reference evapotranspiration (ETo) have been found highly diverse in different regions of the globe due to the contradictory changes in the meteorological variables that define ETo. Despite a significant impact of ETo in water resources and ecology, knowledge on the changes and the cause of the changes in ETo is very limited in tropical regions. The trends in ETo, the factors influencing the changes in ETo and the change point (year) that made the trend significant were evaluated in this study for tropical peninsular Malaysia.
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    Determination of significant wave height offshore of the Federal Territory of Labuan (Malaysia) using generalized Pareto distribution method
    (Coastal Education & Research Foundation, Inc., 2018) Soheil Saeed Far; Ahmad Khairi Abd Wahab; Sobri Bin Harun
    Probabilistic evaluation of surface waves was undertaken to estimate extreme wave heights for several return periods to use them in coastal and offshore constructions in the Labuan region. Generalized Pareto distribution (GPD), which is the standard practice in mainstream extreme statistics, was developed in MATLAB programming. Several diagnostic plots were drawn to ensure the validity of the GPD model. Extreme wave heights were estimated for several return periods and the confidence intervals band was determined for the estimated extreme wave heights. The wave height data set used in the modeling was observed during a 41-year period from 1949 to 1989, in the South China Sea, inside the offshore area of the Federal Territory of Labuan, off the coast of Sabah, Malaysia.
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    Physical- emipirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia
    (Elsevier, 2020) Sahar Hadi Pour; Ahmad Khairi Abd Wahab; Shamsuddin Shahid
    Reliable prediction of rainfall extremes is vital for disaster management, particularly in the context of increasing rainfall extremes due to global climate change. Physical-empirical models have been developed in this study using three widely used Machine Learning (ML) methods namely, Support Vector Machines (SVM), Random Forests (RF), Bayesian Artificial Neural Networks (BANN) for the prediction of rainfall and rainfall related extremes during Northeast Monsoon (NEM) in Peninsular Malaysia from synoptic predictors.