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In this study, a dynamic water budget model is developed for the Emirate of Abu Dhabi (EAD) in the United Arab Emirates (UAE). The model, called Abu Dhabi Water Budget Model (ADWBM), accounts for a number of drivers such as population growth, economic growth, consumption pattern and climatic factors. Model formulation, calibration, validation as well as simulation results for two future situations are presented in this paper. The two water simulations discuss demand-side options in response to different future water conditions until 2050. The first simulation, namely, baseline (BL) simulation examined water balance in the emirate assuming no change in both water production and consumption. BL simulation results highlight the expected shortages in water resources assuming no modification in the supply side. The second simulation, a more conservative and practical simulation considering water conservation options and sustainable improvements to the supply side was developed to achieve a balanced water budget by reducing the baseline consumption rates. The results show that a significant demand reduction is needed in all demand sectors, reaching 60% in the potable sectors and above 70% in non-potable sectors. Overall, results show that the ADWBM can be used as a numerical tool to produce accurate figures of water supply and demand for the sake of planning and decision making in the water sector of the EAD until 2050.

This paper presents a neural network tool for predicting the capital cost of desalination plants based on reverse osmosis technology. A multi-layer feedforward neural network with back propagation learning method is used to model the investment cost of RO plants. The model is developed using the data sets of 1806 RO plants of capacity at least 1000 m3/day, which involved training, testing and validation. The model used six inputs that included both categorical and numerical data elements, namely: plant location, plant capacity, project award year, raw water salinity, plant types, and project financing type. The output is the capital cost of the RO plants planned. This prediction model can be used by governments, investors or other stakeholders in desalination industry to make a reasonable estimate of investment costs of upcoming RO plant projects.

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