simulation

Paying attention to the conservation of water resources in order to prevent water crises is one of the most important duties of people in the community, including officials. In this regard, the most effective action is water demand management, for which there are different methods. One of these methods is pressure management in order to demand management, which can be used in normal operating conditions as well as in the event of water scarcity. On the other hand, in critical situations where the available water does not meet the total demand, policies such as intermittent water supply are adopted, which are associated with many problems. Therefore, an alternative method is needed to minimize the disadvantages of intermittent water supply,to meet the objectives of demand management and, at the same time is feasible, efficient, and economical. In this research, a combined simulation and optimization model is created by using EPANET2.2 and MATLAB software. With this model, the effects of adopting a water demand management approach using pressure management on the hydraulics of water distribution networks will be investigated. In this research, optimization is done in two approaches. In each case, a different objective function is defined and a genetic algorithm is used for optimization. The developed model has been analyzed on the WDN of Baharestan city located in Isfahan province. The results show that the model is able to reduce the average network pressure by 8 meters by finding the optimal location and adjusting pressure for pressure-reducing valves under normal conditions. Also, during water scarcity, it is able to distribute the available water among the demand nodes considering equity and justice principles. After imposing an 8% deficit on the network, without applying for a pressure management program, 8 demand nodes experienced a shortage between 15 and 30% and 19 experienced a deficit below 5%. However, after optimizing the pressure, only 3 demand nodes experienced a shortage between 15 and 25% and 6 nodes experienced a shortage of less than 5%.

The paper demonstrates a simulation-optimization framework for enhancing the real-time flood control with gated spillways at places where no flood forecasting data is available. A multi-objective modeling scheme is presented for flood management in a gated spillway in which the operator may specify the priorities on floods based on their different return periods. Two different operation strategies are devised. Both of the operating strategies employ ten-stage policies, which mainly rely on the reservoir water level as the input data. The second strategy benefits from both observed reservoir water level and flood peak. The optimal values of the models parameters are obtained using genetic algorithm. This is a novel approach because none of its policies needs flood forecasting data, thus, making them adaptable to any flood with any return period. To evaluate the performances of the proposed models, the flood control through gated spillway of Karkheh reservoir is considered where flood hydrographs with different return periods are routed through the reservoir.

Estimation of rainfall runoff is an important step in water resources management, especially in watersheds without hydrometric stations. Therefore, research on models that can simulate river flow in these basins with the least error is necessary and inevitable.Nowadays, due to the issues and problems in the field of water resources, the estimation of the volume of runoff from rainfall is becoming more important day by day in terms of water supply and water resources management. In this study, HBV conceptual model and random forest artificial intelligence (RF) model have been used to simulate the runoff process of Chamanjir watershed in Lorestan province for the statistical period 2006-2015. For this purpose, first the statistics and information needed by the models, including temperature, precipitation, discharge, and evaporation and transpiration were collected. Then the simulation was carried out in the desired period of time and To evaluate the performance of the models, Nash-Sutcliffe criteria and coefficient of determination were used. The results of evaluation criteria for HBV model were Nash coefficient of 0.67 and determination coefficient of 0.68, respectively, and for RF Nash coefficient of 0.82 and determination coefficient of 0.86, respectively, which indicates better performance of RF model in simulating daily flow in the study area. It will be used in the future as a new option to simulate the daily flow of the Chamanjir basin.

The primary source of water supply in Dasht-e Hengam is groundwater; therefore, it is necessary to study the qualitative and quantitative status of groundwater resources in this area due to its high agricultural use. Groundwater researchers have used mathematical models, MODFLOW and MT3DMS, to simulate groundwater and evaluate their efficiency (Sadeghi Goghari, 2013; Paliz, 2014; Yousefi et al., 2014; Abbasi, 2014).Simulation of groundwater resources shows that droughts and wetlands have little effect on groundwater in Sarvestan plain (Torshizi et al., 2015), and the main reason for declining aquifers Is the uncontrolled extraction of wells from the groundwater aquifer (Torshizi et al., 2015; Jabbari et al., 2015; Runama and Jafari, 2017). In addition, the level of pollution has increased in some plains (Deymehkar, 2016; Fasihi and Zare Abyaneh, 2018; Shahnavaz, 2019). For improving the quality of aquifers, aquifer feeding (Ghafari et al., 2020), a suitable hydraulic system (Ghafari et al., 2020), and (or) reactive-nano-iron barriers have been used (Divya et al., 2020).In this research, with the help of two powerful specialized tools of water resources, MODFLOW and MT3DMS particle transfer model, the plain aquifer during the simulation and the quantitative and qualitative status of groundwater is investigated.

Dasht-e Hengam, with approximately ​​525 square kilometers, is located in 76 kilometers of Qirokarzin city and 220 kilometers south of Shiraz city. Geological conditions, alluvial sediment status, location of available groundwater resources, and observation wells are plain alluvial aquifers (Regional Water Company of Fars, 2012). ‎Fig. 1 shows the geographical location of Hengam Plain (Regional Water Company of Fars, 2012), and ‎Table 1 shows the monthly distribution of rainfall in the plain. Data from ten observation wells in the groundwater aquifer, ‎Fig. 2, were used to calibrate quantitative and qualitative models of the plain. The amount of feed from the surface includes returned water from agricultural and drinking wells, from the river (runoff), drainage, annual rainfall at the aquifer level, and sewage, the information of which is listed in (Regional Water Company of Fars, 2012). Piezometers per month were first interpolated in GIS software and then transferred to the model. The model network cell size for all possible observation wells in the area with a cell size of 150 m was considered. A series of pilot points (Figure 3) were designed at the range level for more accurate and faster calibration.

By calibrating the model over 36 months, the optimal hydraulic conductivity value with an average of 17.3 and a standard deviation of 24.9 m/day was obtained (‎Fig. 5). The highest hydraulic conductivity coefficient is related to the northern and northwestern parts of the plain, and the lowest coefficient occurs at the highest accumulation of exploitation wells. Also, the amount of specific storativity varies between 0.00167 and 0.636, with an average of 0.103 and a standard deviation of 0.117.The slight difference between the computational and observational data, ‎Fig. 6 to ‎Fig. 9, indicates desired modeling in the scope of the study. The correlation between computational and observational values ​​(‎Fig. 10, typically shown for the simulation month 60) also shows this.The quantitative model, MODFLOW, shows that the aquifer volume decreases daily during the 36-month modeling period (‎Fig. 11 and ‎Fig. 12); as a result, the amount of groundwater discharge from the boundary of the plain vanishes due to falling groundwater levels. Also, the volume of groundwater inflow is about 24.4 Mm3/year, the recharge into the aquifer from the surface is approximately 10.97 Mm3/year, discharge from alluvial groundwater and complex formation (often wells) is about 39.43 Mm3/year, and the volume of output due to natural drainage is almost 0.546 Mm3/year. The aquifer storage volume is 12.1, and the reservoir withdrawal volume is about 13.5 Mm3/year (‎Fig. 13); thus, the reservoir deficit is 1.36 Mm3/year.‎Fig. 14 shows the groundwater level of the Hengam aquifer in the last step of the 36 months, which, compared to the beginning of the period, indicates a drop in water after three years of groundwater abstraction. ‎Fig. 15 shows that the water level decreased by about 4.5 m during the simulation period, equivalent to an average annual drop of about 1.5 m.MT3DMS package and chemical quality data related to 10 agricultural wells were used to prepare a qualitative model. Information about the location of sampling wells and initial concentration of qualitative parameters in the aquifer in ‎Table 4 and the calibrated value of qualitative parameters are presented in ‎Table 5 for the last calibration step. The distribution of TDS in Figures 19 to 21 shows that the contamination is constantly expanding during these three years, and ‎Fig. 22 shows that as the groundwater level decreases, the TDS contamination increases during the simulation period.

In this study, using MODFLOW and MT3DMS, the quantitative and qualitative status of Hengam plain was investigated. The groundwater flow model created the slightest statistical deviation on the optimization parameters according to the automated calibration and validation approach. The results showed the nonlinear trend of groundwater level sagging with the final year conditions in an optimistic manner.

This paper studies the performance of model predictive control in improving the management of reservoirs in water transfer line. Model Predictive Control is an optimal modern control which due to its proper function against sudden disturbances has been developed for engineering control problems. One of the main capabilities of the predictive control algorithm is the prediction modeling of the system in optimization function. In this research with using a predictive control algorithm while applying the constraints regarding reservoir and pump stations capacity, an optimal controller is designed for the Zarinehrood transfer line to Tabriz. This network contains a total pipe length of 180 kms with 24 branches which is known as the largest transfer line in the Middle East. In this research the designed controller have been solved for the two objective functions, maintaining the safety water in the reservoirs and reducing the fluctuations in the pump stations. To evaluate the predictive control algorithm performance, the results of the MPC were compared with operator data and another control method called linear quadratic integral control . The obtained results show that the predictive control has been able to perform better than operator data and linear quadratic integral control. While yields high capability to satisfy all constraints while fluctuations in pumping stations have been decreased about 22% and the water in the reservoir tanks maintain the lowest error in the vicinity of the desired volume.The total amount of pumped water over the length of the transmission line has been decreased by 8%.

Calculation of stronger indices requires extensive meteorological data that may not be recorded in some areas, but in most areas rain gauges record rainfall data. In this research, the RDI and SPEI indices simulated with acceptable accuracy using the SPI index (only rainfall data is required for its calculation). Simulation was performed using generalized estimating equations (GEE) and then validation of these simulation models was performed using different goodness of fit measures (NSE, RMSE, MAE, R2 and comparison with 1:1 line (using t-test)). The calculated values of different goodness of fit measures indicated the acceptable validity of fitted GEE models. Comparison between goodness of fit parameters and validation coefficients indicated that, in general, the SPEI model has better than RDI model. Models validation showed that the difference between simulated and observed data was not significant at 5% significance level. Results indicated the better performance of simulation models in arid region of the Fars province.

Today, due to recent drought, one of the main sources of drinking water in the country is underground resources, and also nitrate is one of the most important pollutants of groundwater resources, which has adverse effects on people's health. The present study seeks to compare and provide an efficient and innovative technique for simulating and predicting nitrate in these resources. Therefore, three artificial neural networks (ANN) models of neuro-fuzzy inference system (ANFIS) and vector-supported vector (SVM) are compared in simulation as a data-driven tool. Simulation based on observation samples from wells in the aquifer under study for 13 years and the modeling period has been selected monthly. Estimates of model simulations include magnesium (Mg), bicarbonate (Hco3), calcium (Ca), sodium (Na). First, the heterogeneous simulation of heterogeneity is carried out on different makeup. Based on the results of the evaluation of the neo-Frazi system The correlation coefficient of R2 = 9978/0 and MS2 = 0002 have better capability and capability.