An Evolutionary Model for Operation of Hydropower Reservoirs

In this study, an optimization model is developed for monthly operation of a multi-purpose hydropower reservoirs using genetic algorithm. The real value encoding approach is used considering alternative representation, selection, crossover, and mutation schemes. The constraints are handled using the Multiplicative Penalty Method (MPM) function, in order to evaluate the objective function in deferent conditions. The reliability of water allocation to different demands and hydropower generation are evaluated using an economic objective function which has been calculated based on the actual value of water and energy of Karoon-I Reservoir in southwestern part of Iran. The results of this study have shown the importance of selecting a suitable mutation operator for reducing the computational run time of the optimization model. The robustness and efficiency of genetic algorithm in developing the operation policies for a multi-purpose hydropower reservoir is discussed in the paper

A Case Study of Water Quality Modeling of the Gargar River, Iran

Human activities in the recent years have considerably increased the rate of water pollution in many regions of the world. In this case study, the main sources of wastewater discharging into the Gargar River were identified. Using river and point source flow rates and water quality parameters measured along the river, the river water quality was simulated using a commonly used, one-dimensional water quality model, the QUAL2K model. Simulated values of DO, CBOD, NH4-N and NO3-N demonstrated the accuracy of the model and despite a significant data shortage in the study area, QUAL2K model was found to be an acceptable tool for the assessment of water quality. Still, for this case study, it was found that the model was most sensitive to river and point source flows and moderate to fast CBOD oxidation, and nitrification rates

Mid-term Prediction of Meteorological Drought Using Fuzzy Inference Systems

Forecasting and monitoring droughts are important elements of optimum water resources management specifically in the metropolitan areas. Tehranas the biggest city of Iranand its five dams (Amirkabir, Lar, Latyan, Mamloo and Taleghan) are also exposed to drought hazards. In the current article, monthly meteorological data in the geographic area covering [0˚, 60˚] Northern latitudes and [0˚, 90˚] Eastern longitudes with 10×10 degree resolution including air temperature and geopotential height at 1000, 850, 700, 500 and 300 mbar levels are used as the model predictors. These data recorded in the period of 1948 to 2008 have been used to develop a model for forecasting SPI (Standardized Precipitation Index) values in Winter and Winter-Spring seasons with 2.5 and 4.5 months leadtime. This model has been calibrated using 31 years of data. Mutual Information (MI) index has been used to select the inputs (predictors) for each basin in each season. Fuzzy Inference System (FIS) has been used to formulate the model. The fuzzy membership functions have been selected based on sensitivity analysis and engineering judgment. The results of the study have shown that geopotential height in 850 and 300 mbar levels are the best predictors for forecasting SPI values in the selected seasons. The model results have had enough accuracy to be used for forecasting SPI values in Winter and Spring seasons inKaraj and Taleghan basins and SPI values in the Winter season in Mamloo, Latyan, and Lar basins

Seasonal Meteorological Drought Prediction Using Support Vector Machine

In various researches, implementation of meteorological parameters in drought prediction is studied. In the current work, meteorological drought classes based on Standardized Precipitation Index (SPI) for six seasonal scenarios (autumn, winter, spring, autumn + winter, winter +spring, and autumn + winter + spring) and meteorological predictors contained ground and sea surface temperature, weather temperature (at 300, 500, 700 and 850 mi bar) and geopotential height (at 300, 500, 700 and 850 mi bar) wide of North (0, 60) and East (0, 90) was applied in prediction models based on data from 1975 to 2005. In these models, temporal range of meteorological predictors is between October to April month on the same predicted SPI. SPI was calculated based on mean precipitation at seasonal time scale in the main watershed of Tehran (Taleghan, Mamloo) by Inverse Weighted Distance method. The well known statistical supervised machine learning method, support vector machine (SVM), is applied to predict SPI. Regarding to selected data points, the effective regions on Tehran precipitation are southern, southwestern and northwestern of Iran in spring, northern and northwestern in autumn and northwestern and western in winter. SVM depicted accurate results in prediction of SPI, spatially prediction of SPI in all scenarios, and it can be proposed as a very suitable statistical learning method in investigating of nonlinear behavior of meteorological phenomena with a short samples. The predicted SPI in spring and autumn are more accurate than the other scenarios

Role of social network measurements in improving adaptive capacity : the case of agricultural water users in rural areas of western Iran

This study aims at exploring ways of increasing adaptive capacity and reducing vulnerability of farmers in the face of water scarcity through establishing a link between social network indicators and the dimensions of adaptive capacity. For this purpose, a Social Network Analysis (SNA) model along with a Structural Equation Model was developed to investigate the effects of SNA indices on the dimensions influencing the adaptive capacity of the water users in rural areas in the west of Iran. The developed model links participation in production and knowledge exchange with ten critical dimensions identified to affect the adaptive capacities of water resources users. The results indicated that the most important subdimensions affecting adaptive capacity were governance, innovation, and awareness. Also, the positive effects of individual and whole network level of indices on the adaptive capacity indicated high coherence in the study area and the ability of people to develop social learning to improve the adaptive capacity

Role of Social Network Measurements in Improving Adaptive Capacity: The Case of Agricultural Water Users in Rural Areas of Western Iran

peer reviewedThis study aims at exploring ways of increasing adaptive capacity and reducing vulnerability of farmers in the face of water scarcity through establishing a link between social network indicators and the dimensions of adaptive capacity. For this purpose, a Social Network Analysis (SNA) model along with a Structural Equation Model was developed to investigate the effects of SNA indices on the dimensions influencing the adaptive capacity of the water users in rural areas in the west of Iran. The developed model links participation in production and knowledge exchange with ten critical dimensions identified to affect the adaptive capacities of water resources users. The results indicated that the most important subdimensions affecting adaptive capacity were governance, innovation, and awareness. Also, the positive effects of individual and whole network level of indices on the adaptive capacity indicated high coherence in the study area and the ability of people to develop social learning to improve the adaptive capacity