Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Implementation of Water Demand Forecasting Model to Aid Sustainable Water Supply Chain Management in UAE

Version 1 : Received: 3 November 2020 / Approved: 5 November 2020 / Online: 5 November 2020 (10:17:30 CET)

How to cite: Ahmed, V.; Saad, A.; Saleh, H.; Saboor, S.; Kasianov, N.; Alnaqbi, T. Implementation of Water Demand Forecasting Model to Aid Sustainable Water Supply Chain Management in UAE. Preprints 2020, 2020110205 (doi: 10.20944/preprints202011.0205.v1). Ahmed, V.; Saad, A.; Saleh, H.; Saboor, S.; Kasianov, N.; Alnaqbi, T. Implementation of Water Demand Forecasting Model to Aid Sustainable Water Supply Chain Management in UAE. Preprints 2020, 2020110205 (doi: 10.20944/preprints202011.0205.v1).

Abstract

Climate change has become the greatest threat to the survival of world and its ecosystem. With the irreversible impact on the ecosystem, problems like rise in sea level, food-insecurity, natural resources scarcity, seasonal disorders have increased over the past few years. Among these problems, the issue of water scarcity due to the lack of water resources and global warming has plagued several nations. Owing to the rising concerns over water scarcity United Nations (UN) has acknowledged water as a primary resource to the development of societies under the ‘Water Goal’ of the sustainable development goals. As the changing climate and intermittent availability of water resources pose major challenges to forecast demand, especially in countries like the United Arab Emirates (UAE) which has one of the highest per capita residential water consumption rates in the world. Therefore, the aim of this study is to propose an accurate water demand forecasting technique that incorporates all significant factors to predict the future water demands of the UAE. The forecasting model used is the Long Short Term Memory (LSTM), with the factors considered are mean temperature, mean rainfall, relative humidity, Gross Domestic Product (GDP), Consumer Price Index (CPI) and population growth. The LSTM model predicts the water demand forecasting in the UAE showing that the future demand will decrease from 1821 million m3 in 2018 to 1809.9 million m3 in 2027.

Subject Areas

Neural Networks; Long-Short Term Model; Water demand; Forecasting; Sustainable development goals; Water Goal.

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