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Water demand prediction is essential in any short or long-term management plans. For short-term prediction of water demand, climatic factors play an important role since they have direct influence on water consumption. In this paper, prediction of future daily water demand for Al-Khobar city in the Kingdom of Saudi Arabia is investigated. For this purpose, the combined technique of Artificial Neural Networks (ANNs) and time series models was constructed based on the available daily water consumption and climatic data. The paper covers the following: forecast daily water demand for Al-Khobar city, compare the performance of the ANNs [General Regression Neural Network (GRNN) model] technique to time series models in predicting water consumption, and study the ability of the combined technique (GRNN and time series) to forecast water consumption compared to the time series technique alone. Results indicate that combining time series models with ANNs model will give better prediction compared to the use of ANNs or time series models alone.

The prioritization of water mains for renewal requires the consideration of their impact on the deterioration of water quality, in addition to their structural integrity and hydraulic capacity. The deterioration of water mains may lead to structural failure that may have grave economic impacts. This paper develops a fuzzy-based decision support system (DSS) to identify the vulnerable locations in water distribution network (WDN) that may cause overall system failure not only to compromise structural integrity, but also include failures related to water quality and hydraulic capacity. The developed DSS was applied to Al-Khobar WDN located in the eastern part of the Kingdom of Saudi Arabia. To achieve the objectives of the study, an aggregate vulnerability index representing the likelihood of system failure was developed using multi-criteria decision models. In addition, the potential impacts in terms of sensitivity index were also evaluated using advanced soft computing methods. Finally, a risk index, based on both vulnerability and sensitivity indices, was developed to help water managers to prioritize the water mains based on the overall risk of failure.

Delivering water in sufficient quantity and acceptable quality is the main objective of water distribution networks (WDN) and at the same time is the main challenge. Many factors affect the delivery of water through distribution networks. Some of these factors are relevant to water quality, quantity and the condition of the infrastructure itself. The deterioration of water quality in the WDN leads to failure at the water quality level, which can be critical because it is closest to the point of delivery and there are virtually no safety barriers before consumption. Accordingly, developing a powerful monitoring system that takes into consideration water demand distribution, the vulnerability of the distribution system and the sensitivity of the population to the deterioration of water quality can be very beneficial and, more importantly, could save lives if there was any deterioration of water quality due to operational failure or cross-contamination events. In this paper, a framework for a water quality monitoring system that considers water demand distribution, the vulnerability of the system and the sensitivity of the population using fuzzy synthetic evaluation and optimization algorithms is developed. The proposed approach has been applied to develop a monitoring system for a real WDN in Saudi Arabia.

This study investigated the rainfall patterns, spatial variability, surface runoff generation and dam requirements in the southwestern region of Saudi Arabia. The region was divided into four areas Asir, Jazan, Al-Baha and the Red Sea Coast. Surface runoff was estimated for eight scenarios considering the runoff coefficients of 0.05–0.70, resulting in 203–2,835 million cubic meters (MCM) of runoff per year in this region. The runoff in the Asir, Jazan, Al-Baha and the Red Sea Coast were estimated to be in the ranges of 88–1,230, 53–738, 32–443 and 30–425 MCM per year, respectively. The capacities of the existing dams in Asir, Jazan and Al-Baha are approximately 373, 194 and 31 MCM, respectively, while the coast does not have any dam. A significant fraction of runoff is likely to be lost in each scenario of assessment. Water resources may be augmented through construction of new dams and/or wells in appropriate locations. However, better understanding is advisable on locations, water availability, surface evaporation in wadies and reservoirs, accumulation of solids in dam/reservoirs, hydraulic conductivity, economic burdens and national policy.

Delivering water in sufficient quantity and acceptable quality is the main objective of water distribution networks (WDN) and at the same time is the main challenge. WDN risk assessment is gaining importance worldwide due to the wide range of factors that could alter the operation of WDN and the scarcity of data of some of these parameters. Some of these factors are relevant to water quality, quantity and the condition of the infrastructure itself. The deterioration of water quality in the WDN leads to failure at the water quality level, which can be critical because it is closest to the point of delivery and there are virtually no safety barriers before consumption. This research developed a decision support system (DSS) to identify the risk, vulnerable and sensitive locations in WDN that may lead to overall system failure caused by deterioration, insufficient and/or critical conditions of water quality, quantity and infrastructure, respectively. In addition, using water demand and the identified risk, vulnerable and sensitive locations, water quality monitoring system was developed. To achieve the objectives of this research, an aggregate vulnerability index, representing likelihood of system failure, was developed using multi-criteria decision models. Similarly, the potential impacts (consequences) in terms of sensitivity index were evaluated. Advanced soft computing methods including fuzzy synthetic evaluation (FSE) and fuzzy rule-based (FRB) were used to develop these indices. In addition, a risk index on both vulnerability and sensitivity indices was developed, and Geographic Information System (GIS) was used for data display. Other tools, such as WaterGEMs (for hydraulic simulations of distribution network) and (fuzzy-based) techniques were implemented for the prioritization of regions based on risk, vulnerability and sensitivity in distribution network. Optimization techniques including mixed integer programming (MIP) and multi-criteria decision making (MCDM) were used to develop water quality monitoring system. The developed DSS was applied to a local water distribution network (Al-Khobar WDN) to study the vulnerability and sensitivity of the network and recommend a suitable risk management strategy, which was used to manage, control and/or reduce the overall risk of failure of the network.