A novel approach on water resource management with Multi-Criteria Optimization and Intelligent Water Demand Forecasting in Saudi Arabia

Abstract

Ever-increasing demands for freshwater resources have elevated the likelihood of severe water stress in several places of Saudi Arabia during the last several decades. With effective decision-making processes, development objectives on water resource management emerge. In the following series of research articles, recent innovations in various objective demand forecasting systems are examined and contrasted in terms of their utility in resolving tough challenges in water resource management. Hence, this study proposes a novel approach to water resource management integrating Multi-Criteria Optimization and Intelligent Water Demand Forecasting (MCO-IWDF). This framework addresses the challenges in allocating various water resources to multiple water sectors in a future changing environment. In order to plan for future water needs, water managers use a variety of tools. When forecasting future water demand, the most common method is to estimate current per-capita consumption (gpcd) and multiply this by the expected population growth. Conserving water in the Kingdom of Saudi Arabia to improve irrigation issues. This research analyzes the current situation of available water resources and the water demand in Saudi Arabia. The machine intelligence and big data analytic approach improve the proposed water resource management scheme. The simulation analysis identifies the highest performance in demand prediction accuracy of 98.96% and optimization ratio of 97.87% compared to the existing models. Over time, a mathematical model is used to conduct simulation experiments. Studying the problem, creating a model and collecting data are just some of the steps involved in simulation research. Response analysis and a simulation report are also part of this process. The case study analysis results in a significant satisfactory level of 99.23%.

Keywords: Big data analytics; Decision making; Machine intelligence; Muti-criteria optimization; Saudi Arabia; Water demand forecasting.