: Meeting water demands is a critical pillar for sustaining normal human living standards, industry evolution and agricultural growth. The main obstacles for developing countries in arid regions include unplanned urbanisation and limited water resources. Locating and constructing dams is a strategic priority of countries to preserve and store water. Recent advances in remote sensing, geographic information system (GIS), and machine learning (ML) techniques provide valuable tools for producing a dam site suitability map (DSSM). In this research, a hybrid GIS decision-making technique supported by an ML algorithm was developed to identify the most appropriate location to construct a new dam for Sharjah, one of the major cities in the United Arab Emirates. Nine thematic layers have been considered to prepare the DSSM, including precipitation, drainage stream density, geomorphology, geology, curve number, total dissolved solid elevation, slope and major fracture. The weights of the thematic layers were determined through the analytical hierarchy process supported by several ML techniques, where the best attempted ML technique was the random forest method, with an accuracy of 76%. Precipitation and drainage stream density were the most influential factors affecting the DSSM. The developed DSSM was validated using existing dams across the study area, where the DSSM provides an accuracy of 83% for dams located in the high and moderate zones. Three major sites were identified as suitable locations for constructing new dams in Sharjah. The approach adopted in this study can be applied for any other location globally to identify potential dam construction sites.

Rainwater tanks for larger roof areas need optimisation of tank size, which is often not carried out before installation of these tanks. This paper presents a case study of rainwater tank evaluation and design for large roof areas, located in Melbourne, Australia, based on observed daily rainfall data representing three different climatic regimes (i.e. dry average, and wet years). With the aim of developing a comprehensive Decision Support Tool for the performance analysis and design of rainwater tanks, a simple spreadsheet based daily water balance model is developed using daily rainfall data, contributing roof area, rainfall loss factor, available storage volume, tank overflow and irrigation water demand. In this case study, two (185 m3 and 110 m3) underground rainwater tanks are considered. Using the developed model, effectiveness of each tank under different climatic scenarios are assessed. The analysis shows that both the tanks are quite effective in wet and average years, however less effective in dry years. A payback period analysis of the tanks is preformed which reveals that the total construction cost of the tanks can be recovered within 15-21 years time depending on tank size, climatic conditions and future water price increase rates. For the tanks, a relationship between water price increase rates and payback periods is developed. The study highlights the need for detailed optimisation and financial analysis for large rainwater tanks to maximise the benefits.

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