اقبال احسان زاده

In this paper, a hybrid model is proposed to solve a reservoir operation optimization problem considering uncertain inflow conditions. In this model, the artificial neural network (ANN) and improved particle swarm optimization (IPSO) algorithm are used for inflow forecasting and reservoir operation optimization problem, respectively. The proposed IPSO is developed after applying some useful modifications to the original form of particle swarm optimization (PSO) algorithm. The modifications are proposed in order to reposition the infeasible particles. Two different conditions are considered in order to show the effect of inflow forecasting on reservoir operation optimization problem using ANN. It is worth noting that the ANN model is a powerful data driven model that can be used for real time inflow forecasting. In this research, in the first case, the actual measured inflow values are considered as input data to solve reservoir operation optimization problem. In the second case, the ANN model is used to forecast inflows while considering the effects of previous months inflows on the target month inflow. After determining the inflow, the reservoir operation optimization problem is solved using the forecasted inflows. In addition, in the proposed hybrid model, two different formulations are suggested to solve the optimization problem considering water release and reservoir storage volume as decision variables of the problem. The simple and the hydropower operation problems of Dez dam reservoir are solved for forecasted (5 year) time period considering all formulations and cases and the results are presented and compared with other available results. The results indicate the ability of ANN model to forecast the inflow of the Dez dam with acceptable accuracy. In addition, the improved particle swarm optimization algorithm shows to be an effective algorithm to solve reservoir operation optimization problem in which the results of first formulation is better than the second one.

Intensity-duration-frequency rainfall curves are one of the hydrological tools for calculating hydraulic design of structures, watershed design, collection and disposal of surface water in cities. In this research, using the empirical formulas for the Ghareman (2004), Vaziri (1991) and Bell (1969), as well as Hyfran-plus hydrologic software, the rainfall intensity and intensity-duration-frequency curves was determined. At first, using rainfall intensities with time duration less than 180 minutes, a run test was performed in SPSS software and manually to ensure the homogeneity of the data. Then, different statistical distributions were fitted to the data to calculate rainfall intensity in different return periods. The chi square (K2) fitness tests was used to determine the optimal statistical distribution. The results showed that inverse gamma statistical distribution compared to other distributions had the highest accuracy in determining rainfall intensity. In the next stage, the intensity of rainfall during different return periods was calculated by empirical formulas using daily rainfall data and maximum daily precipitation. Comparison of the results of the empirical models and the hyfran plus software used in this study showed that the maximum difference of rainfall intensity in long duration is less than 1.6% and in short-term duration less than 20.2%. Also, the difference between predicted rainfall intensity in Vaziri (1991) and Bell (1969) empirical models is less than 19.8 percent.

Surface water collection networks are responsible for collecting, transporting and directing runoff to the nearest acceptable outlet. In this research, simulation and evaluation of Ilam City runoff system with the ASSA mathematical model developed by the Autodesk Inc. was carried out. In the present research, the entire study area, which includes in-city and outsides, was divided into 46 sub-basins. The physical properties of each sub-basin were determined using the ArcGIS software and field investigation. Within the Ilam City there are four main channels that lead the runoff of sub-basins out of the city. The runoff from sub-basins was calculated using three methods of rational, modified rational and SWMM. The results of the study showed that the collecting system only had a flood transfer ability during the two year return period and during the return periods greater than five years the major part of the main channels in the city were waterlogged and damaged.

Trends invesigation of climatic variables in a catchment can lead to more realistic approaches for water resources planning and management. In this paper, monthly, seasonal and annual trends of inflow, precipitation, evaporation and temperature are studied for Sefidroud dam. Therefore, a parametric test (Regression) and a nonparametric tset (Mann-Kendall) under three different scenarios of independence, short term persistence, and long term persistence are used. The assumption of dataset independency is one of the limitations of the Mann -Kendall test. Therefore, if hydro-climatic time series are characterized with short or long term persistence, this assumption is unacceptable and leads to unreliable results. Since, serial correlation and Hurst phenomenon are seen in most hydro-climatic time series, it is necessary to modify Mann-Kendall test for considering these effects. For comparison of trend analysis results, the homogeneity of hydro-climatic variables is studied by cumulative deviation test, Worsley Likelihood test, and the Bayesian procedure. Comparison of results indicates remarkable significant decreasing trends for inflow using different methods and assumptions. Furthermore, decreasing trends for precipitation and minimum temperature and increasing trends for maximum temperature indicate that hydro-climatology of the study area might be impacted by possible climate change. Obtained results also show that most time series with significant deacrising or increasing trends are also characterized with significant non-homogeneity. Therefore, simultaneous use of homogeneity and trend analysis tests for studying Hydro-climatic variables lead to more reliable results.