IRAN-WATER RESOURCES RESEARCH
Year:2008 | Volume:4 | Issue:2 (11)|Papers Count:9

Decision-making is an essential process in many financial, engineering, and medical fields. Multi Criteria Decision Making (MCDM) and Group Decision-Making (GDM) are among well practiced approaches in solving decision making problems. Group Decision-Making basically combines professional judgments into a coherent group decision. This paper surveys Fuzzy Group Decision Making (FGDM) and develops a new consensus-based fuzzy group decision making algorithm. Decision Makers (DMs) may express their opinions about alternatives and importance of each criterion in four different formats as follow: (1) preference ordering, (2) utility values, (3) fuzzy preference relations; and (4) multiplicative preference relations. In this proposed algorithm, unifying the evaluations of each expert, result in an aggregated score for each alternative. The third step is to rank the linguistic labels or fuzzy sets and select the preferred alternatives based on this sorting. Finally, the decision manager assesses the consensus level and the individual contribution to the group decision and selects the final solution. To illustrate the application of the model in the real decision making processes, this algorithm is used to a groundwater development project to select the most preference alternative for a regional water supply system. Results indicate that the proposed Fuzzy Group Decision Making approach is a relevant approach to aggregate the individual expert's opinion in order to reach a reasonable and determinate consensus level among DMs.

Real time forecasting of river flow is an essential tool in optimum operation of water recourses systems especially storage dams. Because of high complexity of hydro climatology events, real time inflow forecasting is a difficult task. Many researchers have accordingly been carried out and various models have been developed for inflow real time forecasting.Many storage dams, mainly hydropower dams, were constructed or are under construction in the Great Karun basin in south western Iran. The optimization of hydropower generation in this system is therefore of great importance. In this regard the inflow real time forecasting would also be an important item. In this study the Karun-III reservoir inflow real time forecasting was conducted using combination of autoregressive technique and the precipitation forecasts information available for a period of 22, November, 2004 to 20, March, 2005. In this research, various stochastic series data are generated based on the 5-day observed discharges. A series with the most agreement with the daily precipitation forecasts in the following days is then selected to forecast the daily inflow in the next 5 days. Results showed that a relative error of the proposed method for rainy and non-rainy days was about %31.8 and %12.7, respectively. The average error for all the 109 days forecasted data was also reported as %21 which is quite below the average relative errors obtained in simple autoregressive technique which was about %30.

The Fuzzy Sets Theory has recently been widely and successfully used in engineering problems with complexity, ambiguity, or lack of enough data. The Fuzzy Inference System (PIS) is among these techniques. The main advantage of this technique over traditional methods is that it works based on IF-THEN rules and appoints the relation between input and output variables accordingly. In this study the monthly discharge, temperature, and rainfall are used in the Fuzzy Inference System context as continuous series in order to forecast the river flow discharge for the next months. The effect of each variable in previous time step is determined on the flow discharge in the upcoming month and the best combination and suitable lag time is obtained.

There were many hydrological researches focused on dynamic and linear modeling of rainfall-runoff process. Conversion of rainfall data to runoff consists of nonlinear complex relationships which are resulted from interactions of different sets of hydrological process. The stochastic modeling is therefore seems to be more sensible in this estimate than deterministic ones.In this research observed runoff is modeled against total rainfall. This will mainly avoid misleading theories in breaking the rainfall and runoff time series into the excess rainfall and the direct runoff time series. A Transfer Function (TF) model with single input and single output variable (SISO) is used in this research. This function is transferred to the state space equations. The stochastic Data-Based Mechanistic modeling (DBM) method relying upon recursive Kalman filtering algorithm is then used to identify the non-linear relationship between rainfall and runoff.This approach is applied to the Khersan sub basin in the Great Karon catchment south western Iran. The relation between the calibrated parameters and the routine characteristics of the basin flow showed a probable parallel structure of flow routine in this sub basin. Finally the sensitivity analysis is performed using the Monte Carlo Simulation (MCS) in order to quantify the reliability of the model.

In this paper the frequency and the spell of rainy days is analyzed for the city of Tabriz in northwestern Iran. This is done based on probability rule, stochastic process, and Markov Chain technique. The 55 year rain data (1951-2005) for Tabriz synoptic station is used. The frequency matrix is formed and the probability matrix of rainy - dry days is created accordingly based on maximum likelihood method. Recurrence interval is estimated based on persistence probability calculated based on succeed power on probability matrix. Rainy and dry days have 5- and 1-year return period (e.g. probability of 0.2206 and 0.7794), respectively. Finally the 1-5 day rain spell has calculated. The most probable spell length occurred in spring (mainly in May). For example, a 2-day rain in May would occur with a return period of 2.5 days.

In recent years many researchers have been performed on the subject of optimal usage of water resources. One of the issues in this regard is preparing short term water demand forecasting in conjunction with the optimum water demand management. Considering the effects of climatologic conditions on the short term water demand and according to the similarity of consumption trends in the consecutive days, two conventional and advanced models have been developed in this paper using time series method. These models have been used to predict the short term water demand in Tehran, Iran. In the conventional model the time series of Tehran daily water consumption is divided into different components of trend, seasonal variations, and random variations which are obtained by regression method. In the advanced model the consumption pattern in the past is recognized using combined methods of Auto Regressive (AR) and Seasonal Moving Average. Assuming that this pattern will continue in the future, it is then applied to predict daily water demand. In the conventional models it is assumed that different components of time series are independent and dividable. Therefore, its components are determined by different methods such as regression, moving average, etc. In the advanced models all the components are supposed to be correlated to each other and are therefore analyzed together. Comparing the results with the real data showed the ability and accuracy of the both time series models to predict the Tehran daily water demand. The advanced models produced better results than the conventional methods.

In this paper a compatible computational fluid dynamics procedure is presented for calculation of immiscible viscous incompressible fluids separated by a well-defined interface. Two fluids are modeled as a, single continuum with a fluid property jump at the interface by solving a scalar transport equation for volume fraction. The conservation equations for mass and momentum are solved using fractional step method. A Finite Volume discretisation and collocated arrangement are used. Also, the pressure integral term in Navier-Stokes equation is discredited based on a newly developed interpolation which results in non-oscilatoty velocity field especialy at the interface of two high density ratio phases.Finally, computer code is developed based on the above mentioned algorithm and is verified using dam breaking problem with and without obstacle and Raleigh-Taylor instability. The results showed a good concordance with available experimental and numerical data.

So far, the similarities of the electrical current to the liquid flow characteristics were the basis of many studies regarding electrical phenomena. Accordingly hydraulics may also benefits from this similarity. In the present study, analogs in electrics and hydraulics are stated using this new viewpoint. The basic elements and phenomena are presented and defined, among which are the equivalent definition of electromagnetic fields in hydraulic applications and the electric equivalent for the pressurized fluid. Some hydraulic relations are derived from electrical laws. The fluid mechanics formulas are accordingly classified into new groups some schematic representations are also presented.