Water resource systems, with an abundance of project purposes and resource values, are subject to conflicting policy, planning, and management decisions. Multi-criteria decision making methods (MCDM) provide a framework to help water managers identify critical issues, attach relative priorities to those issues, select best compromise alternatives, and facilitate communication to gain general acceptance. This paper addresses a method that incorporates several system factors/components within a general framework for providing a holistic analysis of the problems and comprehensive evaluation of the related mitigation/adaptation measures and policy responses. The method accounts for uncertainties in both the quantification and importance of objectives in the ranking process. The proposed fuzzy multi-criteria decision making process uses the well known Technique for Order Preference by Similarity of Ideal Solution (TOPSIS) method in both deterministic and uncertain environments. The performance of the proposed approach to a real water resource management problem in Iran is illustrated. Results show that the model may be used in a large-scale multi-level assessment process. Ranks of the alternatives are presented using deterministic and fuzzy based models.

Presence of various types of uncertainties in water quality management problems has been recognized as one of the major challenges in water quality modeling. Vagueness, lack of adequate data and nonlinearity of cost and/or benefit functions in most of water quality and waste load allocation management problems have reduced the capability of direct inclusion of uncertainty analysis in the management models. This study presents a fuzzy waste load allocation model in which cost function and the water quality standards or the goals of dischargers and pollution control agencies are expressed with appropriate linear and/or nonlinear and nondecreasing and/or nonincreasing membership functions. QUAL2E and Classified Population Genetic Algorithm (CPGA) were coupled to develop the optimum strategy resulting in maximum value of the minimum nonzero membership values, which represent the optimum satisfaction level of the conflicting goals. Number of constraint violations was used to penalize the fitness function in order to eliminate the infeasible solutions at the final results. The model was applied to a hypothetical case example. Results show a very suitable convergence of the proposed algorithm to good of possibility to the near global optima. Effects of linear and nonlinear membership functions are examined and the results are analyzed.

An automatic calibration of water quality model is developed in this research. Automatic calibration as the process to determine the parameters appearing in the equations of a 2-dimensional, hydrodynamic, and water quality models (CE-QUAL-W2) is carried out with Particle Swarm technique as an optimization tool. In the calibration of the CE-QUAl-W2 model, evaporation as a significant parameter influences the thermal profile and water surface elevation in reservoir, simultaneously. Therefore to consider the simultaneous effects of the water temperature variations on water surface elevation in the reservoir, a multi objective technique is used to minimize the weighted sum of total deviations of temperature from field data at check points on monitoring days and those of water surface elevation on daily monitoring period. The proposed approach overcomes the high computational efforts required if a conventional calibration search technique was used, while retaining the quality of the final calibration results. The automatic calibration approach is applied in temperature and water budget calibration of Karkheh reservoir in Iran. Applying the proposed automatic calibration approach, shows the produced results by the CE-QUAL-W2 model with the calibrated coefficients agree closely with a set of field data.

A simulation-optimization approach is a suitable tool in waste load allocation problems when considering competing objectives and complex pollutant fate and transport processes in water bodies. Here, an archived multi-objective simulated annealing (AMOSA) algorithm is developed to determine various decision variables related to multi-pollutant waste load allocation (MPWLA) problems. The developed AMOSA algorithm has been coupled to QUAL2Kw in order to derive optimal MPWLA programs in Gheshlagh River, Kordestan, Iran. Minimizing wastewater treatment plant (WWTP) costs, improving the EquityMeasure, and enhancing water quality index (WQI) of the river have been considered as objective functions of MPWLA problems. The applied WQI integrates various water quality parameters (biochemical oxygen demand (BOD), dissolved oxygen (DO), NH4-N, NO3-N, PO4-P, total suspended solids (TSS), and Coliform) in monitoring stations along the river. Results show in the scenario with the best EquityMeasure, higher pollutant removal rates have been allocated to Sanandaj WWTP effluent and pollutant point source No. 7 (creek of landfill leachate) due to their greater contributions to Gheshlagh River contamination. Owing to high pollutant load effluents and unsuitable background conditions in Gheshlagh River, more specific studies show that the water quality index may not be improved over 0.22, no matter how much cost is incurred or equity is sacrificed.

No abstract