Rapid urbanization, population explosion and climate change have threatened water security globally, regionally and locally. While there are many ways of addressing these problems, one of the innovative techniques is the recent employment of Sustainable Urban Drainage Systems (SUDS) which include rainwater harvesting systems (RWHS). Therefore, this paper reviews the design and component of two types of RWHS, the namely roof harvesting system (RHS) and the pond harvesting system (PHS). The performance in terms of quantity and quality of collected rainwater and energy consumption for RWHS with different capacities were evaluated, as well as the benefits and challenges particularly in environmental, economic and social aspects. Presently, the RHS is more commonly applied but its effectiveness is limited by its small scale. The PHS is of larger scale and has greater potentials and effectiveness as an alternative water supply system. Results also indicate the many advantages of the PHS especially in terms of economics, environmental aspects and volume of water harvested. While the RHS may be suited to individual or existing buildings, the PHS has greater potentials and should be applied in newly developed urban areas with wet equatorial climate.

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This study presents Gene-Expression Programming (GEP), an extension of Genetic Programming (GP), as an alternative approach to modeling the stagedischarge relationship for the Pahang River. The results are compared to those obtained by more conventional methods, i.e., the stage rating curve (SRC) and regression techniques. Additionally, the explicit formulations of the developed GEP models are presented. The performance of the GEP model was found to be substantially superior to both GP and the conventional models.

Poor water quality is a serious problem in the world which threatens human health, ecosystems, and plant/animal life. Prediction of surface water quality is a main concern in water resource and environmental systems. In this research, the support vector machine and two methods of artificial neural networks (ANNs), namely feed forward back propagation (FFBP) and radial basis function (RBF), were used to predict the water quality index (WQI) in a free constructed wetland. Seventeen points of the wetland were monitored twice a month over a period of 14 months, and an extensive dataset was collected for 11 water quality variables. A detailed comparison of the overall performance showed that prediction of the support vector machine (SVM) model with coefficient of correlation (R(2)) = 0.9984 and mean absolute error (MAE) = 0.0052 was either better or comparable with neural networks. This research highlights that the SVM and FFBP can be successfully employed for the prediction of water quality in a free surface constructed wetland environment. These methods simplify the calculation of the WQI and reduce substantial efforts and time by optimizing the computations.

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The 2003 flood of the Muda River reached 1 , 340 m 3 / s at Ladang Victoria and adversely impacted 45,000 people in Malaysia. A flood control remediation plan proposed a levee height based on a 50-year discharge of 1 , 815 m 3 / s obtained from hydrologic models. This design discharge falls outside the 95% confidence intervals of the flood frequency analysis based on field measurements. Instream sand and gravel mining operations also caused excessive riverbed degradation, which largely off sets apparent benefits for flood control. Pumping stations have been systematically required at irrigation canal intakes. Several bridge piers have also been severely undermined and emergency abutment protection works were needed in several places. Instream sand and gravel mining activities should be replaced with offstream mining in the future.