Mahdi Zarghami

Effective watershed management; Case study of Urmia Lake, Iran

Abstract Limited water resources with uneven distribution and growing demands are the main challenges of water management in Iran. The government has planned several water resources development projects. The complex technical, socio-economical and environmental outcomes of these projects require a comprehensive evaluation. To select more adaptive and accountable projects, suitable group decision support systems are needed. In this research, 4 proposed routes of water transfer schemes to Urmia Lake in Iran, which is in danger of completely drying out, are evaluated with respect to different criteria. The criteria and weights were obtained from an organization responsible for major water infrastructures in the basin. By using an efficient multi-criteria decision making method of compromise programming, the 4 alternatives were ranked, and the most robust water transfer route was selected. This best alternative transfers about 300 million cubic meters per year from another basin in the north of the lake. Results show the importance of using decision support systems for participatory and effective lake management.

Revising the OWA operator for multi criteria decision making problems under uncertainty

Multi criteria decision making (MCDM) problems are usually under uncertainty. One of these uncertain parameters is the decision maker (DM)s degree of optimism, which has an important effect on the results. Fuzzy linguistic quantifiers are used to obtain the assessments of this parameter from DM and then, because of its uncertainty it is assumed to have stochastic nature. A new approach, entitled FSROWA, is introduced to combine the Fuzzy and Stochastic features into a Revised OWA operator. If the DM is not aware of the risk in decision, then the decision objective is to maximize the expected combined goodness measure. If the DM cares only about the risk, then minimizing the variance of the combined goodness measure is his/her objective. The results of the FSROWA provide the expected value and the variance of the combined goodness measure for each alternative. In order to combine these two attributes a composite goodness measure is introduced. This measure gives more robust decision outcomes. The theoretical results are illustrated in a watershed management problem.

Fuzzy quantifiers in sensitivity analysis of OWA operator

The efficient use of the Ordered Weighted Averaging (OWA) operator in decision making problems depends on the choice of the order weights. Using fuzzy quantifiers is one of the most popular methods to obtain them. In this study, a new method will be introduced for determining the order weights from the quantifiers, which is especially useful in the case of unimodal quantifiers. The new method is generic and has better computational efficiency in comparison to the previously applied methods. In addition, a new measure for sensitivity analysis on the outputs of OWA operator will be introduced. The theoretical results will be illustrated by a Ph.D. student selection problem discussed earlier in the literature.

On the relation between Compromise Programming and Ordered Weighted Averaging operator

This paper will demonstrate a strong relationship between the Compromise Programming (CP) method and the Ordered Weighted Averaging (OWA) operator. We will mathematically prove that the application of the CP method reflects the pessimistic view of the Decision Maker (DM), if the distance from the ideal point is minimized. It will however present an optimistic DM, if the distance from the nadir is maximized. The OWA operator may cover all of the optimistic, neutral and pessimistic views. A practical relationship will be developed between the power value of the CP method and the optimism degree of the DM based on fuzzy quantifiers. The theoretical results will be illustrated by a real case study of a water resources management problem.

Soft computing of the Borda count by fuzzy linguistic quantifiers

Borda count is a well-known social choice method frequently used for group decision making problems, however, it does not consider the optimistic/pessimistic view of the director, which has a great effect on group decisions. In the present study, the traditional Borda method is extended by using the ordered weighted averaging (OWA) operator to consider the risk-attitudinal characteristics. The new approach, entitled Borda-OWA, solves the group decision making problem in a more intelligent procedure. Borda-OWA is then applied on two real case studies of forest management and projects ranking. The outcomes show that the Borda-OWA operator produces more soft and sensitive results by using the directors risk attitudes, guided by the fuzzy linguistic quantifiers. These results are examined by sensitivity analysis, which shows the significance of selecting the suitable quantifier. It is also shown that the original Borda is a special case of the Borda-OWA approach, indicating only the neutral view for the director. Because of the uncertainty in selecting the quantifier, we have defined a new measure, which considers both the expected value and the variance of the combined goodness measure for each alternative. By using this measure, the Borda-OWA model will give more robust decision to the stakeholders whose optimism degrees are different than that of the group director.

Sensitivity Analysis of the OWA Operator

The successful design and application of the ordered weighted averaging (OWA) method as a decision-making tool depend on the efficient computation of its order weights. The most popular methods for determining the order weights are the fuzzy linguistic quantifiers approach and the minimal variability method, which give different behavior patterns for the OWA. These two methods will be first analyzed in detail by using sensitivity analysis on the outputs of the OWA with respect to the optimism degree of the decision maker, and then the two methods will be compared. The fuzzy linguistic quantifiers approach gives more information about the behavior of the OWA outputs in comparison to the minimal variability method. However, in using the minimal variability method, the OWA has a linear behavior with respect to the optimism degree, and, therefore, it has better computation efficiency. Since maximizing the combined goodness measure and minimizing its sensitivity to optimism degree are conflicting objectives, a new composite measure of goodness will be defined to have more reliability in obtaining optimal solutions. The theoretical results will be illustrated in a water resources management problem.

Simulating hedging rules for effective reservoir operation by using system dynamics: a case study of Dez Reservoir, Iran

Abstract Some of the most important challenges facing water managers are to increase water supply and reduce its demand. A single systematic method is needed to address both issues, such as the System Dynamics (SD) modeling approach. In this approach all the factors, parameters, and their influences on the problem are considered by causal loops and stock-flow diagrams. The multipurpose Dez Reservoir in southwestern Iran is a good case study for this approach, and we simulated 10 years under differing operation strategies to develop the most appropriate operation policy. A hydrologic time series analysis was conducted to generate simulated inflow to the reservoir, and differing policies, including hedging rules and a “goal-seeking hedge,” were applied. By using performance criteria and a new measure entitled “corrected reliability,”, the most appropriate scenarios were identified. We found that using the goal-seeking hedge in combination with water demand management offers the best chance for effectively meeting demands and minimizing supply shortages.

Combining Monte Carlo and finite difference methods for effective simulation of dam behavior

Monte Carlo simulation provides a probabilistic approach to evaluate the physical behavior of infrastructures. Therefore, the performance could be achieved in a more realistic manner. Within this framework, an innovative software code is developed by combining the Monte Carlo and finite difference methods to predict the behavior of embankment dams after impounding. In order to assess the efficiency of the method, the case study of Chahnimeh-4 dam, located at Southeast of Iran, has been investigated in detail. The behavior of this dam is predicted and compared with the field monitoring by using the Kolmogorov-Smirnov test. The results indicate the robustness of the proposed method and it can be then efficiently used in monitoring the dam responses with respect to the various factors like seepage, piping and settlement.

Water supply management using an extended group fuzzy decision-making method: a case study in north-eastern Iran

The purpose of this study was to develop a group fuzzy multi-criteria decision-making method to be applied in rating problems associated with water resources management. Thus, here Chen’s group fuzzy TOPSIS method extended by a difference technique to handle uncertainties of applying a group decision making. Then, the extended group fuzzy TOPSIS method combined with a consistency check. In the presented method, initially linguistic judgments are being surveyed via a consistency checking process, and afterward these judgments are being used in the extended Chen’s fuzzy TOPSIS method. Here, each expert’s opinion is turned to accurate mathematical numbers and, then, to apply uncertainties, the opinions of group are turned to fuzzy numbers using three mathematical operators. The proposed method is applied to select the optimal strategy for the rural water supply of Nohoor village in north-eastern Iran, as a case study and illustrated example. Sensitivity analyses test over results and comparing results with project reality showed that proposed method offered good results for water resources projects.

Water Distribution Networks Designing by the Multiobjective Genetic Algorithm and Game Theory

To design an urban water network, in addition to minimizing the cost, improving the water pressure is very important. In this chapter, we develop a biobjective optimization model and apply it to a water network in a district in the city of Tabriz, in northwestern Iran. Due to nonlinearity and the huge number of variables and constraints, the genetic algorithm was used. First, several Pareto solutions were obtained, and then, based on the game theory (the area monotonic and Kalai–Smorodinsky approaches), the most preferred point was provided. The solution was simulated by the WaterGEMS software, and then the elements of the network were designed. This optimum solution showed a decrease of approximately 10% in total cost, in addition to improved water pressure. Therefore, this new approach is suggested for other urban water management problems.