Developing a Robust Multi-Attribute Decision-Making Framework to Evaluate Performance of Water System Design and Planning under Climate Change

In theory, emergence of robustness concept has pushed decision-makers toward designing alternatives, such as resistant against the potential fluctuations fueled by uncertain surrounding environment. This study promotes an objective-based multi-attributes decision-making framework that takes into account the uncertainties associated with the impacts of the climate change on water resources systems. To capture the uncertainties of climate change, Monte Carlo approach has been used to generate a series of ensembles. These generated ensembles represent the stochastic behavior of the hydro-climatic variables under climate change. This framework represents the inherent uncertainties associated with hydro-climatic simulations. Next, a coupled TOPSIS/Entropy multi-attribute decision-making framework has been formed to prioritize the feasible alternatives using system performance measures. The main objective of this framework is to minimize the risk of deceptive and subjective assessments during decision-making process. Karkheh River basin has been selected as a case study to demonstrate the implication of this framework. Using a set of system performance attributes, the performance of two hydropower systems has been estimated during the baseline period and under the future climate change conditions. According to the conducted frequency analysis, the alternative in which both hydropower projects would go under construction emerged as the robust solution (i.e., there was a 99.9% chance that it outperforms other solutions). The results indicate that the construction of these hydropower systems leads to the increase of Karkheh River basin robustness in the future.

     

Developing Strategies for Agricultural Water Management of Large Irrigation and Drainage Networks with Fuzzy MCDM

Sustainable water resources management aims at increasing the efficient use of water and achieving food security. This work proposes a generalized novel spatial fuzzy strategic planning (SFSP) in combination with multi-criteria decision making (MCDM) and a conceptual agricultural water use model for determining sustainable agricultural water management strategies. The proposed framework is applied to an irrigation and drainage network in Iran, which constitutes a large-scale water resource system. A spatial strength, weakness, opportunity, and threat (SWOT) analysis of internal and external factors related to agricultural water management is applied in this work. Possible water management strategies were ranked with the MCDM approach that combines the Analytic Hierarchy Process (AHP) and the Fuzzy technique for order-preference by similarity to ideal solution (TOPSIS). The AHP estimates the criteria weights and the TOPSIS model prioritizes the agricultural water management strategies. The results of SWOT analysis show that the final scores of the internal and external factors are equal to 2.9 and 2.73, respectively. Accordingly, the most attractive strategic type is a SO (aggressive) strategy, and a combination of structural and non-structural strategies (SO, ST, and WO strategies) are the top-ranked ones. Proposed strategies for water supply and demand management are the development and rehabilitation of the physical structure of water resources system of irrigation network, improvement of operation management and maintenance of water resources system, wastewater management, and inter-basin water transfer within the irrigation network. The results indicate that the total annual volume of agricultural water under normal conditions is about 1.8 billion cubic meters, of which about 1707 million cubic meters (95%) issue from surface water sources and 90 million cubic meters (5%) from groundwater sources. The proposed model and the calculated results provide viable and effective solutions for the implementation of sustainable management of water resources and consumption in large-scale water resources systems.

     

Extracting Optimal Policies of Hydropower Multi-Reservoir Systems Utilizing Enhanced Differential Evolution Algorithm

Deriving the optimal policies of hydropower multi-reservoir systems is a nonlinear and high-dimensional problem which makes it difficult to achieve the global or near global optimal solution. In order to optimally solve the problem effectively, development of optimization methods with the purpose of optimizing reservoir operation is indispensable as well as inevitable. This paper introduces an enhanced differential evolution (EDE) algorithm to enhance the exploration and exploitation abilities of the original differential evolution (DE) algorithm. The EDE algorithm is first applied to minimize two benchmark functions (Ackley and Shifted Schwefel). In addition, a real world two-reservoir hydropower optimization problem and a large scale benchmark problem, namely ten-reservoir problem, were considered to indicate the effectiveness of the EDE. The performance of the EDE was compared with the original DE to solve the three optimization problems. The results demonstrate that the EDE would have a powerful global ability and faster convergence than the original DE to solve the two benchmark functions. In the 10-reservoir optimization problem, the EDE proved to be much more functional to reach optimal or near optimal solution and to be effective in terms of convergence rate, standard deviation, the best, average and worst values of objective function than the original DE. Also, In the case of two-reservoir system, the best values of the objective function obtained 93.86 and 101.09 for EDE and DE respectively. Based on the results, it can be stated that the most important reason to improve the performance of the EDE algorithm is the promotion of local and global search abilities of the DE algorithm using the number of novel operators. Also, the results of these three problems corroborated the superior performance, the high efficiency and robustness of the EDE to optimize complex and large scale multi-reservoir operation problems.     

Generalized Storage Equations for Flood Routing with Nonlinear Muskingum Models

Water Resources Management - The nonlinear Muskingum model is a leading method for hydrologic routing. The efficiency of the nonlinear Muskingum model for routing of hydrograph outflow has been...     

Optimization of Run-of-River Hydropower Plant Design under Climate Change Conditions

The assessment of climate change and its impacts on hydropower generation is a complex issue. This paper evaluates the application of representative concentration pathways (RCPs, 2.6, 4.5, and 8.5) with the change factor (CF) method and the statistical downscaling method (SDSM) to generate six climatic scenarios of monthly temperature and rainfall over the period 2020–2049 in the Karkheh basin, Iran. The identification of unit hydrographs and component flows from rainfall, evaporation and streamflow data (IHACRES) model was employed to simulate runoff for the purpose of designing a run-of-river hydropower plant in the Karkheh basin. The non-dominated sorting genetic algorithm (NSGA)-II was employed to maximize yearly energy generation and the plant factor, simultaneously. Results indicate the runoff scenarios associated with the SDSM lead to higher run-of-river hydropower generation in 2020–2049 compared to the CF results.     

Comparison of Accelerated Compressive Creep Behavior of Virgin HDPE Using Thermal and Energy Approaches

This article compares two available approaches for accelerating the creep response of viscoelastic materials, such as High Density Polyethylene (HDPE), which is increasingly gaining attention for use in construction. Thermal acceleration methods to predict the tensile creep of polymers are already available. The Time-Temperature Superposition (TTS) phenomenon is the basis of several available methods, and an ASTM standard for tensile creep of geosynthetics is based on one of its derivatives, the Stepped Isothermal Method (SIM). In this article, both TTS and SIM have been adapted to study the compressive creep of virgin HDPE. An alternate approach, based on the equivalence of strain energy density (SED) between conventional constant-stress creep tests and strain-controlled stress-strain tests, is also adapted for accelerated compressive creep of HDPE. There is remarkably a good agreement among the creep behaviors obtained from conventional tests, TTS, SIM, and SED predictions for virgin HDPE.     

The Enhanced Honey-Bee Mating Optimization Algorithm for Water Resources Optimization

Evolutionary and meta-heuristic algorithms are widely used to solve water resources optimization problems. In this context, the honey bee mating optimization (HBMO) algorithm, inspired by the mating ritual of honey bees, is a reliable and efficient algorithm. The HBMO algorithm is modified in this work leading to the Enhanced HBMO (EHBMO) algorithm. The EHBMO is then applied to solve several unconstrained/constrained mathematical benchmark functions and a multi-reservoir problem. The performance of the EHBMO is compared with those of the elitist genetic algorithm (EGA) and the HBMO algorithm. The results show that the EHBMO achieves a better solution in a smaller number of functional evaluations and with less variance of results about global optima in comparison with the EGA and the HBMO algorithm.     

Effect of Breakage Level One in Design of Water Distribution Networks

Design of water distribution networks (WDNs) that do not consider performance criteria would possibly lead to less cost but it could also decrease water pressure reliability in abnormal conditions such as a breakage of pipes of the network. Thus, awareness of the situation of consumption nodes, by considering water pressures and the amount of water that is being supplied, could be an effective source of information for designing high performance WDNs. In this paper, Two-loop and Hanoi networks are selected for least-cost design, considering water pressures and the amount of water supplied on each consumption node under breakage level one, using the honey-bee mating optimization (HBMO) algorithm. In each state of design, a specific pressure is defined as the minimum expected pressure under breakage level one which holds the pressure reliability in the considered range. Also, variations of some criteria such as reliabilities of pressure and demand, vulnerability of the network, and flexibility of the design are analyzed as a tool for choosing the appropriate state of design. Results show that a minor increase in the cost of design could lead to a considerable improvement in reliabilities of pressure and demand under breakage level one.     

Application of Swarm Intelligence and Evolutionary Computation Algorithms for Optimal Reservoir Operation

Water Resources Management - Real-world problems often contain complex structures and various variables, and classical optimization techniques may face difficulties finding optimal solutions....     

Optimizing Multiple Linear Rules for Multi-Reservoir Hydropower Systems Using an Optimization Method with an Adaptation Strategy

Water Resources Management - Water resources crisis has a significant impact on hydropower energy production, which highlights the importance of water resources management. Reservoirs are effective...