جستجوی مقالات مرتبط با کلیدواژه « فازی » در نشریات گروه « مهندسی آب »

Comprehensive evaluation of organization performance is one of the most powerful tools for decision making. It helps managers to gain necessary information about the status of their organization. One of the most widely welcomed models in this regard is the Balanced Scorecard Model which provides comprehensive evaluation for various aspects in the Water and Wastewater Corporation. The present study was a descriptive-applied research in terms of objectives. The objectives included selecting a comprehensive evaluation pattern, recognizing and ranking the indexes and main sub-standards for evaluating the comprehensive performance with BSC model denoting various parts in water and wastewater corporations. The model was used in West Azarbayjan province water and wastewater company as a pilot. Data collection was based on the library and field study via 3 types of questionnaires and interview with staff, financial staff, and by the customers of the given company. Based on the correlation and regression coefficients, the mean variables test and the fuzzy method showed that the BSC fields are in the order of priority in the performance of the mentioned company. These included internal processes, learning and growth, and customer and financial perspective. In the regression dependent variable model, the performance of the company and independent variables included various variables in fields of the balanced scorecards, and the result showed that the most effective factors on the company performance were decreasing the unnecessary costs, increasing and developing new facilities, finding new custumers, providing infrastructures, and organizational resources through learning, increasing customer satisfaction, enhancing the efficiency and quality of the internal processes.

Supply of safe water as well as sanitation services are, as economic activities, the main purpose of the water and wastewater Company. To meet these goals there is a need to great financial supply as well as justified planning and optimized use of all facilities and talents available. One of the main challenges in this regard is the cost management which leads to increase in efficiency. This is why monitoring of efficiency in Water and Wastewater companies is vital. In this study group decision-making method is implemented using paired comparisons questionnaire to rank the costs of activities in West Azerbaijan Rural Water and Wastewater Company by fuzzy AHP approach. Then by applying the obtained weights typical model of measuring efficiency is provided. Result show that water costs with weight of 0.1458 and equipment costs with weight of 0.1446 are in priority. Total efficiency factor has a large fluctuation, which in this company with an average of 0.37 units. This is mainly due to reduced efficiencies in energy section and in human resources section. Applying the results of ranking and prioritizing the performance of the various types of company costs leads to more concerns on the necessary and unnecessary costs, without mistreating its core or basic functions.

Groundwater is a major source of water supply for domestic, agricultural, and industrial uses; hence, its quality modeling is an important task in hydro-environmental studies. While many data-based models have been developed for this purpose, the performance of such data-based models can be drastically enhanced if they are based on temporal and spatial pre-processing. In this study, geostatistics tools (e.g., Co-Kriging), as spatial estimators, and self-organizing map (SOM), as a clustering technique, were employed in conjunction with Adaptive Neuro-Fuzzy Inference System (ANFIS) for the temporal forecasting of such quality parameters as electrical conductivity (EC) and total dissolved solids (TDS) of the groundwater in Ardabil Plain. Using the results thus obtained, the impact of spatial data clustering was also investigated on the same parameters. The results showed that, if propoer input data are selected, the proposed spatial clustering technique is capable of imporving groundwater quality forecasts made by ANFIS.

River flow Simulation is the first and most important step for controlling flood in water resources management. In this study, daily flow data of Marboreh River was evaluated by using Nero-Fuzzy networks. To this end, different combinations of precipitation, temperature and runoff (as input data) were used for daily inflow estimation in the period 1998-2012. Correlation coefficient, root mean square error and Nash Sutcliff were used to evaluate the performance of this method. The results showed that the combination of structures using the intelligent method for estimating Marboreh River flows was quite acceptable. The correlation coefficient, root mean square error and Nash Sutcliff coefficient were 0.970, 0.001 m3/s, 0.928, respectively. To sum up, the results indicated that Neural-Fuzzy Inference has a better capability to estimate the minimum and intermediate values and has a good correlation with the observed values.

In this study, an adaptive neuro-fuzzy inference system (ANFIS) has been applied to model activated sludge wastewater treatment process of Mobin petrochemical company. The correlation coefficients between the input variables and the output variable were calculated to determine the input with the highest influence on the output (the quality of the outlet flow) in order to compare three neuro-fuzzy structures with different number of parameters. The predictions of the neuro-fuzzy models were compared with those of multilayer artificial neural network models with similar structure. The comparison indicated that both methods resulted in flexible, robust and effective models for the activated sludge system. Moreover, the root mean square of the error for neuro-fuzzy and neural network models were 5.14 and 6.59, respectively, which means the former is the superior method.

Due to different reasons like non-normative usage and sequential droughts, water scarcity has highlighted the necessity of water resources management. Studying the processes affecting water resources is necessary for a normative management with sustainable development. The most important hydrologic process is the rainfall-runoff process that influences both ground and surface water resources. The study of this process is closely related to water resources management. In the past two decades, the intelligent models have found more importance because of their high ability in simulation of nonlinear and complex processes like rainfallrunoff process. In this study, the efficiency of common intelligent models in hydrology including Multi-Layer Perceptron artificial neural network (MLP), Radial Basis Function artificial neural network (RBF) and Co-Active Neural Fuzzy Inference System (CANFIS) in rainfall-runoff simulation and estimation of monthly discharge in Balikhluchay watershed was investigated. According to the coefficients of MSE, NMSE, MAE and RMSE (0.0145, 0.276, 0.103 and 0.120 respectively), the MLP artificial neural network which has three hidden layers, three neurons in each layer, a momentum algorithm and hyperbolic tangent transfer which would stimulate the monthly discharge for a 24 month test period with high accuracy was chosen as the most accurate network.

Water resources are among basics for sustainable development. The non uniform temporal and spatial distribution of water resources is led to shortage water in arid and semi arid regions. The inter basin water transfer projects are one remedy to this problem. According to the diversity of the feasible water transfer projects, ranking of the projects with different criteria is currently one of the important issues in water resources management. Fuzzy Multiple Criteria Decision Making (FMADM) is a collection of methodologies which can be used to compare, select, or rank multiple alternatives that typically involve incommensurate attributes. The aim of this research was to use some of the fuzzy multiple attribute group decision making methods for ranking some inter-basin water transfer projects of Karoon River, western Iran. Maximizing and minimizing sets method, the Bonissone’s method for group decision making, Fuzzy TOPSIS for group decision making, and Fuzzy Decision Making software were used in this study. A set of 10 interbasin water transfer projects with 8 criteria were considered. First, the weight of each criterion was determined within a group of five water resources experts, Then the rating preference for each alternative were obtained by the samegroup of experts. Finally, the results of different methods were compared. The results showed that the Tunnel Koohrang 1 posses the higher rank. Also, the Spearman rank correlation coefficient was obtained between different methods. The results showed that the Fuzzy TOPSIS and Bonissone method provide the best correlation. For evaluating the criteria weightings, the sensitivity analysis was conducted and the results were compared.

Dealing with Water Scarcity is one of the important probleme in water resources management. There is no one of financial capability to execute all the water resources projects in onelot, so it is important to rank execution projects. In this study, one of the fuzzy multiple attribute decision making methods was employed for ranking some of the inter-basin water transfer projects, by using different critaria and modifying the Raj and Kumar method for both benefit and cost critaria conditions. The approach has been applied to the Karun river basin. For ranking alternatives, first critaria and their weight were determined and then 10 inter-basin water resources projects of Karun river with 8 critaria were considered. The weight of each criterion determined by 5 water resources experts. The fuzzy weights of alternatives are computed by using standard fuzzy arithmatic. The concept of maximizing snd minimizing sets are used to computed the total utility of each of alternatives. Finally, the results of maximizing and minimizing method were compared to FDM software.