Utilizing aquifers as underground water storage reservoirs can effectively meet water management goals. Aquifer Storage and Recovery (ASR) technology involves injecting water into aquifers through specialized wells and withdrawing it when needed. The success of ASR depends on evaluating and modeling the specific hydrogeological characteristics of a site, such as groundwater salinity, transmissivity, storativity, slope, soil properties, proximity to recharge water networks, and road accessibility. This study introduces an ASR site-scoring system to identify the most suitable locations across Qatar based on these criteria. The results provide valuable insights into the feasibility of using ASR technology for water management in Qatar. The study reveals that approximately 7414.11 km2, or 64

The increasing demand for freshwater, combined with limited availability and the exacerbating effects of climate change, poses significant global challenges to sustainable water supply. Addressing the widening gap between water supply and demand necessitates the adoption of innovative and sustainable water management strategies. As a renewable and locally available resource, rainwater holds considerable potential to alleviate water scarcity in arid and semi-arid regions. This study explores rainwater harvesting (RWH) as a practical strategy to address the annual groundwater deficit in the State of Qatar. A Geographic Information System (GIS)-based approach was employed to assess the potential of RWH implementation. The analysis integrated ground-based observations with satellite-derived datasets to identify suitable locations for RWH. To enhance the accuracy of the suitability mapping, legal constraints such as urban areas, parks, and farms as well as protective buffer zones were applied, resulting in a refined delineation of land realistically available for RWH development. The findings indicate that approximately 59 % of Qatar's land area is potentially suitable for RWH initiatives. Of this, 1.27 % was classified as 'very highly suitable', 27.27 % as 'highly suitable', and 49.50 % as 'moderately suitable'. Assuming the installation of engineered RWH wells with a recharge efficiency of 40 % and annual rainfall of 67 mm, the estimated net groundwater recharge from these three suitability classes could reach approximately 107 million m3 annually. These results highlight the substantial contribution that RWH can potentially make toward reducing Qatar's national freshwater deficit. The outcomes of this research support the strategic expansion of RWH infrastructure and provide a basis for informed decision-making in future water resource planning across the country.

Utilizing aquifers as groundwater storage reservoirs is an effective strategy for water management in water scare regions. The success of managed aquifer recharge (MAR) relies on the assessment and modeling of site-specific hydrogeological characteristics, including groundwater salinity, transmissivity, storativity, slope, soil properties, proximity to water recharge networks and road accessibility, etc. This study employs a GIS-based multi-criteria evaluation technique, integrating both ground and remote sensing datasets. The results indicate that a significant portion of the total land area, approximately 7,414.11 km 2 (64%), can potentially be utilized for MAR practices, while the remaining 36% is restricted due to various constraints, such as built-up areas, roads, agricultural lands and nationally protected areas for conservation. The available 64% of land is further categorized into subclasses ranging from highly suitable to least suitable areas. Most of the highly and moderately suitable regions are located in the northern central parts of the country where seasonal surplus treated wastewater and desalinated water may be used to recharge groundwater. Furthermore, MAR technology can also be used to tackle saltwater intrusion in the coastal areas by injecting seasonal surplus desalinated and treated wastewater. These findings suggest that MAR technology has a high potential to facilitate aquifer water storage and recovery in the country, which can contribute to sustainable water resources.

In this paper, we described a computationally efficient simulation–optimization (S/O) framework for coastal groundwater management (CGM), based on the combined application of numerical modeling, artificial neural networks, and genetic algorithm. The objective was to analyze the ‘trade-off’ between optimality and risk in deriving CGM strategies and to show that reformulating the problem using a mean–variance bi-criterion objective function can be a valuable tool in minimizing the risk of non-optimality. As a case study for our analysis, we studied the optimal design of an aquifer storage and recovery (ASR) system in the Muscat metropolitan area in Oman. S/O was applied to find the optimal constant daily abstraction and injection rates that maximize the net present value (NPV) of the ASR system. The results show that the choice of the decision variables significantly affects the risk of non-optimality, and reducing this risk comes at the cost of a decrease in the expected NPV.

Combined simulation–optimization (CSO) schemes are common in the literature to solve different groundwater management problems, and CSO is particularly well-established in the coastal aquifer management literature. However, with a few exceptions, nearly all previous studies have employed the CSO approach to derive static groundwater management plans that remain unchanged during the entire management period, consequently overlooking the possible positive impacts of dynamic strategies. Dynamic strategies involve division of the planning time interval into several subintervals or periods, and adoption of revised decisions during each period based on the most recent knowledge of the groundwater system and its associated uncertainties. Problem structuring and computational challenges seem to be the main factors preventing the widespread implementation of dynamic strategies in groundwater applications. The objective of this study is to address these challenges by introducing a novel probabilistic Multiperiod CSO approach for dynamic groundwater management. This includes reformulation of the groundwater management problem so that it can be adapted to the multiperiod CSO approach, and subsequent employment of polynomial chaos expansion-based stochastic dynamic programming to obtain optimal dynamic strategies. The proposed approach is employed to provide sustainable solutions for a coastal aquifer storage and recovery facility in Oman, considering the effect of natural recharge uncertainty. It is revealed that the proposed dynamic approach results in an improved performance by taking advantage of system variations, allowing for increased groundwater abstraction, injection and hence monetary benefit compared to the commonly used static optimization approach.

Decision making plays an important role in economic, management, business, marketing, psychology, philosophy, mathematics, statistics, and many other fields. In each field, decision making consists of identifying the values, uncertainties, and other issues that define the decision. Randomness and fuzziness or vagueness are two major sources of uncertainty in the real world. Practical applications in areas of industrial engineering, management, and economics, are such that decision-makers are being confronted with information that is simultaneously probabilistically uncertain and fuzzily imprecise, and a decision making has to be performed under such a twofold uncertain environment of co-occurrence of randomness and fuzziness. This paper presents an application to the transportation problems in fuzzy stochastic hybrid uncertainty environments. In this paper, we focus on our attention on unbalanced transportation problems in the fuzzy stochastic environment.

The hydrological and economic feasibility of aquifer storage and recovery (ASR) of excess desalinated water and managed aquifer recharge (MAR) using tertiary treated wastewater (TTWW) to manage stressed coastal aquifers in Oman has been studied numerically using the code, MODFLOW 2005 and the different transport packages MT3DMS, and MODPATH. The current ASR study aims to assess the feasibility of saving and recovering water for the purpose of supply to the city of MUSCAT during high demand periods by banking excess-desalted water during winter and recover it during the rest of the year. The second objective of the study is to explore the feasibility of MAR using TTWW to mitigate salinity in two costal aquifers in North of Oman exploited for different purposes: domestic water supply (Al-Khod aquifer), and for irrigation purposes (Jamma aquifer). ASR in the Al-Khod Aquifer was explored using Simulation Optimization multi-objective modeling using evolutionary algorithm NSGA-II (namely, the Non-dominated Sorting Genetic Algorithm-II), to generate the set of Pareto optimal solutions according to recharging scenarios. The results show that the potential net benefit of storage and recovery might reach as high as $17.80 million/year. The maximum profitable volume that can be recharged into the aquifer, given the limited number of wells and their locations, is estimated at 8.4 Mm(3)/year, which is lower than the current excess estimated of 10 Mm(3)/year. For MAR using TTWW, different managerial scenarios were simulated and analysis of the results reveals that the Jamma aquifer will further deteriorate in the next 20 years if it remains poorly managed. The groundwater level will decline further to exceed 3 m on average, and the iso-concentric salinity line of 1,500 mg/L will advance 2.7 km inland that will severely affect farming activities in the area. However, MAR using TTWW when integrated with the management of groundwater abstraction (e.g., smart water meter, higher irrigation efficiency to reduce the abstraction rate) becomes hydrologically feasible to augment the aquifer storage and controlling seawater intrusion, and hence sustains farming activities. The economic analyses of such situation recommend: (1) injecting TTWW in the vicinity of irrigation wells; (2) investing in smart water meters and online control of pumping from the wells to reduce the abstraction rate by 25%; and (3) a combination of both are feasible scenarios with positive net present values. Recharge in upstream areas is found not economically feasible because of high investment cost of the installation of pipes to transport the TTWW over a distance of 12.5 km. Because the financial resources for investments are limited, scenario (2) shows a Net Benefit Investment Ratio of 4.41 (i.e., investment of a $1 yields $4.41). Although option (3) shows the lowest Net Benefit Investment, it is very attractive from a social perspective because it entails an integrated demand and supply management of groundwater. Farmers are requested to reduce pumping, and the government will invest in injecting TTWW to improve groundwater quality in the vicinity of irrigation wells and to form a hydrological barrier to control seawater intrusion in the long run. The primary objective of MAR for the Al-Khod aquifer is to increase the urban water supply and to sustain the aquifer service with the lowest possible damages from seawater intrusion. A number of managerial scenarios were simulated and progressively developed to reduce seawater intrusion and outflow of the groundwater to the sea. An economic analysis was conducted to characterize the trade-off between the benefits of MAR and seawater inflow to the aquifer under increased abstraction for domestic supply. The results show that the abstracted volume for domestic supply can be doubled under MAR practices if irrigation wells are properly managed and public wells are better located. Even though injection of TTWW is more expensive ( due to the injection cost), will result in higher benefits. The results indicate that managing the aquifer would produce a net benefit ranging from $8.22 million to $15.21 million compared with $1.57 million with the current practice. MAR using TTWW is feasible to develop water resources in arid regions, and the best scenario depends on the decision maker's preference when weighing the benefits of MAR and the level of damage to the aquifer. MAR, as a smart water governance technology, mitigates stresses on aquifer systems in arid zones, maximizes the benefit of using groundwater for both agricultural and domestic purposes while minimizing the adverse socio-hydrological and agricultural consequences of mismanagement of commingled groundwater-TTWW resources at all scales (national, catchment, metropolitan area, village, farm).

Gulf Cooperation Countries depend mainly on desalinated water for urban purposes. In Muscat, the capital city of Oman, desalinatedwater supplies 94% of the urban water. However, given the nature of take-orpay contract between the desalinating company and the public water authority a seasonal surplus of desalinated water is produced during thelow demand winter period. The take-or-pay contract is the most common type of contract in the desalination business worldwide. A numerical groundwater flow simulation model is coupled to a dynamic multi-objective optimization model to optimize storage and recovery of the excess desalinated water in a protected coastal aquifer in Oman. Maximizing the net benefit of storage and recovery of the excess desalinated water is undertaken while minimizing the seawater intrusion.. The results show that the potential net benefit of storage and recovery might reach as high as $17.80 million/year. The maximum profitable volume that can be recharged into the aquifer, given the limited number of wells and their locations, is estimated at 8.4 Mm /year, which is lower than the current excess estimated of 10 Mm /year. 3 3

Aquifer recharge rates and patterns are often uncertain, especially in arid areas due to sporadic and erratic rainfall. Therefore, determining the optimal groundwater abstraction using classical approaches such as Monte Carlo Simulation (MCS) requires a large number of groundwater simulations and exorbitant computational efforts. The problem becomes even more complex and time consuming for regional coastal aquifers whose domains must be discretized using high-resolution meshes. In fact, even fast evolutionary multi-objective optimization techniques generally require a large number of simulations to determine the Pareto-front among the objectives. This study explores the performance of a Decision Tree (DT) approach for the generation of the Pareto optimal solutions of groundwater extraction. This paper applies the DTs for the optimal management of the Al-Khoud coastal aquifer in Oman. The learning process of the developed DT-based model uses the output of a numerical simulation model to assess the aquifer response based on different abstraction policies. The trained DT network then utilizes the NSGA-II to determine the Pareto-optimal solutions. The simulation show that the general flux pattern in the study area is toward the sea and the hydraulic head following a similar pattern in both best and worst recharging scenarios downstream of the studied recharging dam. Statistical tests showed a good correlation between the DT-based and simulation-based results and demonstrate the capability of the DT approach to obtain high-quality solutions by incorporating a large number of recharge scenarios. Moreover, the required runtime of the DT-based approach is extremely low (5 min) compared to that of the simulation-based method (several days). This means that including additional Monte-Carlo simulations can be readily done in few minutes using the obtained DTs, instead of the long computational time needed by the simulation-based approach.

The problem of planning the petrol station replenishment problem (PSRP) consists in making simultaneously several decisions, such as determining the minimum number of trucks required, assigning the stations to the available trucks, defining a feasible route for each tank-truck, etc. The objective to be achieved is usually defined as the minimisation of the travelled distance by the tank-trucks to serve all of the distribution stations. Traditional studies in the literature model and solve this problem over a time period of one single day. Only few works have recognised the fact that extending the time horizon to several days may yield important savings for the delivering company. The goal of this paper is to survey the optimisation techniques that support the petrol companies in improving their delivery performance over a multi-period planning horizon. We present the mathematical optimisation models that have been developed for both the t-day and periodic variants of the problem and discuss the heuristic methods so far developed for their solution.

This paper focuses on the periodic aspects within the Petrol Station Replenishment Problem when defined on an extended planning horizon of t working days. It has the aim of surveying the scientific literature on this topic and giving an overview of the modeling issues, mathematical formulations, and solution approaches related to the Periodic Petrol Station Replenishment Problem (PPSRP).

We deal with the design of parallel algorithms by using variable partitioning techniques to solve nonlinear optimization problems. We propose an iterative solution method that is very efficient for separable functions, our scope being to discuss its performance for general functions. Experimental results on an illustrative example have suggested some useful modifications that, even though they improve the efficiency of our parallel method, leave some questions open for further investigation.

Droughts and climate variability cause uncertainties on water supply especially in arid regions and coastal aquifers’ over-exploitation causes seawater intrusion. Since the rate and extent of aquifer recharge is often very uncertain, determining the optimal groundwater abstraction is a challenging task. In this paper a framework is proposed for estimating the optimal abstraction of groundwater for urban supply under uncertainty and under complex conditions of water table fluctuations and seawater intrusion. It is based on a combination of several models: (i) a Monte-Carlo Simulation (MCS) to incorporate the uncertainties in groundwater recharge, (ii) a numerical groundwater flow model, MODFLOW to simulate the effects of abstractions on the water table fluctuations and seawater intrusion and (iii) a multi-objective optimization model to generate the set of Pareto optimal solutions for each recharging scenario. Maximizing the benefit to the water utility, minimizing the average groundwater table level fluctuations and minimizing the seawater intrusion are the objectives of the model. A fast multi-objective evolutionary algorithm is used to obtain the Pareto efficient solutions for each recharging scenario. Compromise programming (CP) is then used to select the closest solutions to the ideal. Finally, the amount of optimal reliable groundwater abstraction is estimated using a cumulative distribution function. The proposed methodology is applied to a coastal aquifer in the western part of Muscat metropolitan area, Oman. The results have shown that annual groundwater abstraction volume may range from 12.7 to 18.8 Mm 3 compared to 6.8 Mm 3 currently pumped. This would result in an economic benefit of $10.5 million to $15.4 million/year. On the other hand the aquifer’s maximum annual mean drawdown would range from 0.7 to 0.9 m.

In this paper we introduce the Periodic Petrol Station Replenishment Problem (PPSRP) over a T-day planning horizon and describe four heuristic methods for its solution. Even though all the proposed heuristics belong to the common partitioning-then-routing paradigm, they differ in assigning the stations to each day of the horizon. The resulting daily routing problems are then solved exactly until achieving optimalization. Moreover, an improvement procedure is also developed with the aim of ensuring a better quality solution. Our heuristics are tested and compared in two real-life cases, and our computational results show encouraging improvements with respect to a human planning solution

In this paper, we investigate the Steiner tree problem with delays, which is a generalized version of the Steiner tree problem applied to multicast routing. For this challenging combinatorial optimization problem, we present an enhanced directed cut-based MIP formulation and an exact solution method based on a branch-and-cut approach. Our computational study reveals that the proposed approach can optimally solve hard dense instances.

The main feature of neural network using for accuracy improvement of physical quantities (for example, temperature, humidity, pressure etc) measurement by data acquisition systems is insufficient volume of input data for predicting neural network training at an initial exploitation period of sensors. The authors have proposed the technique of data volume increasing for predicting neural network training using integration of historical data method. In this paper we have proposed enhanced integration historical data method with its simulation results on mathematical models of sensor drift using single-layer and multi-layer perceptrons. We also considered a parallelization technique of enhanced integration historical data method in order to decrease its working time. A modified coarse-grain parallel algorithm with dynamic mapping on processors of parallel computing system using neural network training time as mapping criterion is considered. Fulfilled experiments have showed that modified parallel algorithm is more efficient than basic parallel algorithm with dynamic mapping, which does not use any mapping criterion.

In this article we describe a heuristic algorithm to solve the asymmetrical traveling salesman problem with periodic constraints over a given m ‐day planning horizon. Each city i must be visited r i times within this time horizon, and these visit days are assigned to i by selecting one of the feasible combinations of r i visit days with the objective of minimizing the total distance traveled by the salesman. The proposed algorithm is a heuristic that starts by designing feasible tours, one for each day of the m ‐day planning horizon, and then employs an improvement procedure that modifies the assigned combination to each of the cities, to improve the objective function. Our heuristic has been tested on a set of test problems purposely generated by slightly modifying known test problems taken from the literature. Computational comparisons on special instances indicate encouraging results. © 2004 Wiley Periodicals, Inc. NETWORKS, Vol. 44(1), 31–37 2004

In this paper we discuss the use computational grids to solve stochastic optimization problems. These problems are generally difficult to solve and are often characterized by a high number of variables and constraints. Furthermore, for some applications it is required to achieve a real-time solution. Obtaining reasonable results is a difficult objective without the use of high performance computing. Here we present a grid-enabled path-following algorithm and we discuss some experimental results.