Mohd Nazish Khan

TThe present paper addresses a comprehensive historical assessment of water consumption, demand, and supply in Saudi Arabia, along with future projections regarding water balance, in terms of demand and supply by source in various sectors. Being an arid region, Saudi Arabia experiences scorching heat, low precipitation, a high rate of potential evaporation, and the absence of permanent water bodies over the territory. Groundwater contributes almost 61% of total available water, while the recharge rate is negligible. However, few widyan (ephemeral streams) systems exists to satisfy water demand, which could contribute to approximately one year of domestic water consumption if managed efficiently. The study also predicts water consumption scenarios for the next three consecutive development plans, i.e., the 10th plan (2015–2019), 11th plan (2020–2024), and 12th plan (2025–2029). The analysis shows that water consumption may decline significantly in the future, if the present rate of decline continues. Scenario I, if the current rate is assumed, provides a decrease in consumption of 14.36, 12.66, and 11.15 BCM for 10th, 11th, and 12th plans, respectively. Moreover, the domestic and industrial sectors will consume more water in the future. In the same way, scenarios II and III represent a decline in total water consumption, along with that of agriculture, while domestic and industrial water usage would increase, thus improving environmental sustainability.

This paper explores the ethical implications of deploying machine learning systems in real-world applications, focusing on bias, fairness, and transparency.

We present a novel framework for ensuring fairness in algorithmic decision-making processes across various domains.