فهرست مطالب فرید فروغی

Since the increase in the depth of groundwater and its intensification can indicate serious limitations in the exploitation of these resources, predicting the changes of this parameter plays an important role in managing these resources and preventing possible damage to them. For this purpose, the use of smart methods has been strongly recommended by researchers. In this research, the methods of multilayer perceptron neural network (MLP), fuzzy inference system (fis), adaptive neural fuzzy inference system (ANFIS), and the combined method of fuzzy neural inference system and particle swarm optimization (ANFIS-PSO) were used for simulation of groundwater Fluctuations depth in Haji Abad area between March 1995 to October 2022 on a monthly scale. The training and testing phases were done with 75 and 25 percent of data, respectively. To measure the accuracy of the models, root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute value of error (MAE) indices were used. The best results in the training phase are related to ANFIS-PSO, ANFIS, and MLP models, respectively. Simultaneously with the training of the mentioned models, the testing stage of said models was also implemented. Finally, the best results in this stage belonged to the neural network model with time delay [1 3 5], the ANFIS-PSO model with time delay [1 2 3], and the neural network model with time delay [1 2], respectively. The accuracy indices in the test stage for the best models are (0.1871, 0.1865, 0.1857) for RMSE, (0.7402, 0.6715, 0.6684) for MAPE, and (0.1326, 0.1238, 0.1198) for MAE, respectively. These values show that all three models have an error of less than 20 cm, an error percentage of less than 0.75%, and an absolute error of less than 14 cm, which indicates the acceptable accuracy of these models. Also, the coefficient of determination obtained from the regression relationship of the calculated and measured values of the groundwater depth in the test phase for all three models is around 0.82, which indicates a relatively high linear relationship between these two parameters.

Correct estimation of the river sediment volume is important for many water resources projects.In this study, the empirical equations of sediment transport, support vector regression (SVR), and developed k-nearest neighbor regression (KNN) were used in order to estimate suspended sediment load in the Sistan River. In this regard, in addition to the maximum temperature, minimum temperature, and dischargeand Suspended Loud In the period 1374 to 1390, it was paired on 1682, discharge, the classified discharge was used as effecting variable to suspended sediment load modeling. For each of the input combinations in support vector regression and development k-nearest neighbor regression the best structure of the regression model determined based on the performance criteria. The Result showed that the Toffaleti method is the best method between empirical equations with R2 equal to 0.705 and RMSE equal to 66558 Ton/day. Also, the support vector regression model with discharge, minimum temperature, maximum temperature and classified discharge as the set of input data is the best model in estimation of suspended sediment load with R2 equal to 0.96 and RMSE equal to 2809.3 Ton/day. The results indicate that the regression method estimate suspended sediment load much better than empirical equations in the Sistan River.

One of the most important issues that has always threatened the health of human societies is the contamination of food with heavy metals and reaching the danger limit. To conduct this research in a case study and laboratory work, the amounts of heavy metals (Cd, Zn, Cu, Pb, Mn, Ni and Fe) and their Target hazard quotient of human health through salt consumption of Maharloo Lake and Korsia salt mine were investigated for both child and adults groups. For this study, Maharloo Lake and Korsia salt mine were sampled and their heavy metals were determined. The daily intake of metals was determined and compared with the reference value. The target hazard quotient, THQ for all metals of Maharloo Lake salt for adults and child was less than one. This means that the hazards of the proposed metals are within the acceptable limits. For korsica salt, the THQ for lead metal (for child group) was more than one and for other metals (child and adult groups) was less than one, indicating that non-cancerous adverse effects were likely to occur in childs. Among the proposed metals, the highest THQ was obtained for lead metal and the lowest one for zinc metal for Maharloo salt and korcia salt mine. The Hazard index for adults and child was less than one for Maharloo salt and more than one for Korsia salt, indicating that there are adverse effects of non-cancerous diseases due to the consumption of heavy elements caused by the consumption of this salt. Salt consumption threshold for hazard level 1, which is negligible in terms of chronic hazard, for Maharloo Lake salt, for child and adults were 1003.8 and 4133.4 mg/day and for Korsica salt for child and adults were 13.2 and 543.5 mg/day, respectively.

In the past decade the different methods have been used to analyze and predict the physical variables, one of which is singular spectrum analysis (SSA) statistical methods. SSA is one of the methods, used in modeling various statistical processes and more recently, its use in various engineering disciplines including water resources, in order to eliminate random components in time series has been expanded. The main objective of this study was to forecast streamflow in the Karkheh basin using singular spectrum analysis. The gage stations in the Karkheh basin (five station) were selected for this study. The high flow period for these gage stations were determined. In order to modeling methods, 70% and 30% of data were used for calibration and validation respectively. The singular spectrum analysis method was used for pre-processing of data and elimination of noise in the time series of streamflow. Then, the recursive algorithm of the singular spectrum analysis model was used to develop forecasts models of streamflow in the Karkheh basin gage stations. To evaluate the performance of the model Normalized root mean square error, mean absolute error and correlation coefficient were used. In the validation the highest and lowest value of the NRMSE and MARE statistics were 0.47 and 0.50 for Pol Chehr station. The lowest value of the NRMSE statistic for Pol Dokhtar and Cham Anjir stations was 0.3 and 0.31 respectively and close to each other and the lowest value of the MARE statistic for Cham Anjir and Pol Dokhtar stations was 0.29 and 0.30 respectively and close to each other. Finally, the best and the weakest results in two stages of calibration and validation were for Cham Anjir and Pol Chehr Stations respectively. The results of this research showed that singular spectrum analysis can be used to forecast streamflow with reasonable accuracy

Through a measurement of the annual growth of a tree ring and finding out of its chronology, the possibility of study and reconstruction of Palmer Drought Severity Index (PDSI) in the habitat areas is provided. The aim followed in this research is to reconstruct PDSI using dendrochronology, and drought monitoring within Karkheh basin. Throughout the research the chronology index of two tree species, namely Quercus brantii, and Quercus infectoria, in the central Zagross region during the period of 1840-2010 were used to reconstruct Palmer Drought Severity Index in KarkhehBasin. Correlations between Palmer Drought Severity Index and regional chronology index were positive and significant within 1% of confidence level. With regard to this fact, the Palmer Drought Severity Indexes from year 1840 to 2010 were reconstructed. The values of observed vs. reconstructed Palmer Drought Severity Index within the common statistical period are consistent with each other. Within further steps, the hydrological conditions during chronology period were studied and accordingly, hydrological drought was analyzed within the Basin for years, 1840 to 2010. Severity and duration of the droughts as well as decades of the highest number of drought and wet events were determined. In addition, the results were compared with those obtained by other researchers as well.

By measuring the annual growth of tree ring and make their chronology, the possibility of study and reconstructions of streamflow in the habitats areas is provided. The aim of this research is to reconstruct streamflow using dendrochronology, and modeling and classification of hydrological drought in Karkheh basin. In this research the chronology index of two tree species, Quercus brantii, and Quercus infectoria, in the central Zagros region during the period 1840-2010 were used to reconstruct streamflow in Karkheh basin. Three gage stations in Karkheh basin were selected, and their high flow periods were determined. Correlations between streamflow in high flow period and regional chronology index for gage stations were positive and significant at 1% confidence level, and with regard to this fact, the average streamflow in high flow period of these three gage stations from the year 1840 to 2010 were reconstructed. The values of observed and reconstructed streamflow in common statistical period are properly consistent with each other. In the following, hydrological conditions during chronology period were studied and accordingly, hydrological drought was analyzed in the Karkheh Basin for years 1840 to 2010. Severity and duration of the droughts as well as low flow and high flow decades were determined. In addition, the results were compared with other researchers as well. After reconstructing streamflow a probabilistic neural network model were utilized in order to classify the periods of very low flow, low flow, high flow, and very high flow. The results showed that provided model is able to recognize the mentioned periods with a high accuracy in the region.

One of the most important problems in water resources management is the optimal operation of dam reservoirs. Various methods have been applied to deal with optimization problems. Noting the inability of the conventional optimization methods to solve complex optimization problems, the Meta-Heuristic algorithms have been noticed more than ever. In this study, particle swarm, genetic and continuous ant colony system algorithms were used to optimize the operation of the Doroodzan reservoir. Furthermore, the effect of chain constraints inclusion on the framework of these algorithms were analyzed. The results indicated that regarding the chain constraints, all programs resulted in feasible solutions, where without chain constraints, in some cases the algorithm was unable to find a feasible solution. Therefore, applying these constraints in the framework of the algorithms improved their performances further. In finding the optimized solution, the Genetic algorithm had a better performance than the two other algorithms. Reliability criterion was used to evaluate the algorithms performances. Based on this criterion, as one of the most important criteria in determining system performance, the Genetic algorithm with the Reliability value of 0.964 had the most suitable performance.

As a crucial issue in aqua sciences, optimizing dam reservoirs exploitation has been studied with a variety of optimization techniques. One of these methods is using Meta-Heuristic algorithms such as Firefly and Ants Algorithms. Using the firefly algorithm, this study studies the exploitation optimization of Doroudzan reservoir in a 99-month period. The most sensitive parameter in sensitivity analysis of Firefly Algorithm, α, is known as the mutation rate. Selecting its appropriate value by Firefly worms leads to an appropriate solution and increases the efficiency of the Firefly Algorithm dramatically. To determine the efficiency of this algorithm in optimizing the utilization of the dam reservoir, the obtained results were compared with the results of Continuous Ant System and Ranking Ant System. The findings indicated that FACC algorithm with objective function rate of 4.196 had a satisfactory performance. ACOrCC and ACOrankCC algorithms with the values of 17.004 and 26.156 followed it respectively. In addition, FACC algorithm with a value of 0.959 had the highest reliability coefficient. The results indicated that regarding Chain constraints, all program performances led to feasible solutions; however, ignoring chain constraints, the Continuous Ant System algorithm was unable to find a feasible solution. Hence, applying these constraints in the main structure of this algorithm would enhance its efficiency significantly.

In irrigation scheduling, one question is “how much is the optimal depth?”. In this study the optimal irrigation depth (by assuming the power distribution function and by taking into consideration the distribution function, which governs the data (normal distribution)) by considering the environmental effects was determined for three water management strategies (i. e. unlimited, limited nitrogen leaching and environmental protection) for wheat under center pivot irrigation system. The System layout in 32 hectare field was installed according to the ASAE standard. Four radial lines of catch cans were used at intervals of 6m (two radial lines of catch cans A and B, were installed on the maximum slope with 3˚ between every two rows and two radial lines of catch cans C and D, were installed on the minimum slope with 3˚ between every two rows). The field data were collected by the five different speeds of the center pivot system. The economical ratio (C) for three water management strategies were 31.19, 4.58 and 1.45, respectively. The optimum irrigation water was calculated for the three different management strategies. The results indicated that the lowest value of optimal irrigation depth was observed under the unlimited strategy, and the highest value of optimal irrigation depth was found for the environmental protection strategies (both with assuming power distribution and normal distiribution function).