مجله هیدروژیولوژی
سال ششم شماره 1 (بهار و تابستان 1400)

The phenomenon of the exploitation of groundwater recently has caused a sharp decline in groundwater levels, resulting in both subsidence and desertification caused by the groundwater withdrawal. Thus, reliable prediction of groundwater level has been an important component in sustainable water resources management. In this study, a data-driven prediction wavelet neural network model (WNN) was proposed for groundwater level in Azna-Aligodarz, Dourod-Brojerd, Delfan, Selseleh plain forecasting, and the results were compared with the artificial neural network. Parameters including precipitation, temperature, flow rate and water level balance during the period of the previous month were used as input of the model and level of the water table in each period as the output of the model through monthly scale (2002-2019) were selected. The criterion of the correlation coefficient, Root-Mean-Square-Error and average absolute error and coefficient of Nash Sutcliff for evaluating and the comparison of performance models were used.  The results of the hydrograph analysis indicated that the increase of rainfall has an effect on groundwater resources, and also the findings evaluation of criteria showed that WNN has better performance and less error than the artificial neural network.

Soil subsidence is one of the dangers facing many Iranian plains. In the meantime, the plains of Semnan province, including the Ivanaki plain, are at risk. Due to the importance of the subject, in this study, the rate of subsidence of Ivanaki plain and its effective factors are analyzed. In this research, the descriptive-analytical method was used. The survey data included radar and satellite imagery, statistical information, and a 30-m digital elevation model. The method is first to evaluate the geomorphology, land use and groundwater status of the Ivanaki Plain, and then, using Sentinel 1 radar image and SBAS time series method, the amount of subsidence is calculated. Surveys show that natural factors, including trends and trends in anomalies, have played a major role in shaping climate and water resource constraints, and restricting access to water causes the excessive use of groundwater and severe surface depletion. The results of calculating the groundwater level in the area show that the average of the study wells falls between 1372 and 1394 years between 0/85 to 2/01 cm per year. Also, the results of the assessment of the area's subsidence indicate that the range of studies over the 3-year period (from 2016/01/06 to 2018/12/21) decreased between -0.9 to -33.2 cm. Most of the subsidence has been in the center of the plain of Ivanaki, near the village of Cheshmeh Nadi.

The increasing water resources demand for various purposes, such as agriculture, drinking and industry, has faced many difficulties and limitations in achieving programmable water. In recent years, inter-basin water transfer is inevitable due to an increase in population cities and inappropriate spatial water resources distribution and is regarding as one of solving ways of water shortage issues. Regarding the complexity of the inter-basin water transfer projects caused by various factors of getting involved such as environmental issues, right of water and water balance, social cautions and immigration, the decision-making about water transfer and inter-basin water transfer effects is very important. Despite the existence of various multivariate decision-making approaches, still these methods have not applied in an appropriate way in national executive and infrastructure projects like inter-basin water transfer. One of inter-basin water transfer infrastructure projects is water transfer from Charmahal_O-_Bakhtiari province to Iran central plateau called the Beheshtabad project. In the present research, 3 recommended options of the Beheshtabad water transfer project considering 14 quantity and quality criteria using pairwise comparisons questionnaires, expert knowledge was ranked using analytical heretical process method that is a multivariate decision-making approach and the best option was identified for water transfer. Results showed that current and capital cost criteria were identified as the most important criteria and reservoir geological issues criteria was identified the less important criterion. Also, the sensitivity analysis of studied criteria was assessed using a change in the weight of each criterion. The results of this study could present a simple way in order to mythological usage of inter-basin water transfer projects especially in some cases that there is no clear vision of hazards and consequences.

A grout curtain is constructed beneath the dam foundation to control water seepage. Cement is used to execute the grout curtain and to extend until it reaches impermeable bedrock. Grout curtain will mostly reach groundwater, too. The reaction between cement and water caused the cement compound to enter groundwater and changes its quality. The extent of change and its continuity is important so that it needs to be studied and investigated over time. In this research, the change of water quality has been studied by comparison of water test analysis during 4 years. For this purpose, four phases of water samples including site springs samples before any disturbance (first phase, 1392), after cement grout and before impoundment (second phase,1394), and also two phases samples after impoundment (third phase,1395 and fourth phase,1396) with one-year interval were examined. Samples of the second, third and fourth phases were taken from observation wells which are located downstream of the grout curtain. Therefore, it is expected to see the effects of cement entry in the groundwater flow through the curtain to the observation wells. In this study after categorizing the achieved data, using SURFER and GIS software, different parameters were interpolated in different periods and compared by the production of zoned maps, and the degree of variation of different elements was investigated. Diagrams and maps show that pH, SiO2, Na, K, Fe, CaCO3, SO4 and EC have been significantly changed which indicates that cement compounds have been entered into the groundwater and have decreased the water quality.

In the aquifers of hard rock, spring water is a good indicator for evaluating the interaction of rock water and water level changes. Maku area is located in West Azarbaijan province in northwestern Iran. Supplying part of the water needed for agriculture in the region through travertine-maker springs, the role of these springs in the travertine deposition process and the effect of the chemical composition of the waters on the travertine discoloration has made the study of hydrogeochemical properties of springs in the study area important. Thus, in this research, the assessment of hydrogeochemical characteristics, chemical composition and the water origin of springs were evaluated by water sampling, sediment deposits and rock units around the springs. The results of hydrogeochemical diagrams, ionic ratios and saturation index showed that the reservoir of the aquifer is impure limestone and the water of springs is saturated to supersaturated with respect to carbonate minerals and undersaturated for sulfate minerals and chemical type mainly is Ca- HCO3. The high pressure of carbon dioxide and its removal from the water composition of the springs has caused the limestone springs of the region to have a high deposition ability and formation of Fissure-ridge and vein-type travertine with relatively high height and expansion. The results of geochemical analysis of the elements in the composition of sediments around the springs show that the correlation between CaO and Al2O3, SiO2, TiO2, Fe2O3 is negative, also the correlation between CaO and Ba and Sr is positive and also the correlation between SiO2 and Fe2O3 and Al2O3, is positive, therefore, the water source of the spring can be considered mostly thermogenic.

Groundwater plays a vital role in supplying water demands for different consumptions in dry and semi-dry regions of earth. Iran is considered as an arid and semi-arid region and its groundwater resources have recently shown some significant changes. Owing to the reduction of groundwater resources and recent droughts, simulation of groundwater level variations has significant importance. In some areas of the country of Iran, groundwater levels have been dropped significantly. Therefore, the prediction and simulation of the groundwater level variation are crucially important. In this study, the Gene Expression Programming (GEP) model was combined with Wavelet Transform (WT) to estimate long-term variations of groundwater level (GWL) in the Sarab-Ghanbar observation well over a 13-years period. Firstly, observation data were divided into two sub-samples, 9 years for training and 4 years for testing. Then, the most effective input lags were identified using the autocorrelation function. Next, four different models for each GEP and WGEP method were developed using the lags. The superior model was identified by analyzing all GEP and WGEP models. The superior GEP model simulated the GWL with acceptable accuracy. For instance, the correlation coefficient and Nash-Sutcliffe efficiency coefficient for the model were calculated at 0.938 and 0.851, respectively. A comparison between the GEP and WGEP models showed that the wavelet transforms enhanced the performance of simulation significantly. For example, Variance Accounted For (VAF) index for the best WGEP model was 14 times more than the best GEP model. In addition, the sensitivity analysis indicated that (t-1), (t-2), (t-3) and (t-4) lags were the most influenced input lags.

Mian- Ab is one of the most important plains in Khouzestan province and the most water demands in this plain is related to the irrigation for agriculture. In this research, the agricultural water requirement was first calculated using Penman- Monteith FAO method due to the necessity of water resources consumptions revision in agricultural field. Then, the numerical groundwater model of the study area was prepared to know about the water balance and consumed water. The model was calibrated from September 2016 to September 2017 in the unsteady state conditions with the 0.94 m of RMSE error. According to the calculations, the actual crop water requirement is 278.89 MCM per year. The model results show that 334.07 MCM of water is used though irrigation network and wells for agricultural purposes. Therefor, 55.27 MCM of water is used more than the actual water needs. Three scenarios were considered for excess consumed water including the transfer of all surplus water to southern lands through the development of irrigation network, decreasing the rate of well pumping and the integrated management of surface and groundwater resources. Applying the discussed scenarios to the numerical model showed that the fist scenario causes drawdown and water logging in the northern and southern parts of the aquifer, respectively. Applying the second scenario will cause a severe water logging in the northern parts of the study area. In the third scenario, the excess consumed water will be managed by decreasing the 8.5 percent of exploitation wells rate (equal to 25.27 MCM) and transferring the remaining water (30 MCM) to the southern lands of study area. Therefore, considering the hydrodynamic conditions of the aquifer and the model results, the third scenario is more desirable for the management of excess consumed water.

Modeling qualitative and quantitative parameters of groundwater as one of the main water supply resources is crucially important. Due to recent climate changes in Iran, precipitation patterns have been dramatically altered leading to excess withdrawal. In this paper, two meta-heuristic artificial intelligence models are presented to simulate monthly time-series data of quantitative (groundwater level) and qualitative (chlorine and bicarbonate) parameters of groundwater within an observational well situated in the city of Kermanshah, Iran from 2005 to 2018. To define the hybrid artificial intelligence model, the extreme learning machine (ELM), differential evolutionary (DE) algorithms are combined with the wavelet transform and the Self-adaptive extreme learning machine (SAELM) and wavelet self-adaptive extreme learning machine (WSAELM) models are developed. It is worth mentioning that the autocorrelation function is utilized for detecting effective lags of the time-series data. Moreover, 70% of the observed data are used for training the artificial intelligence models and the remaining 30% for testing them. Then, using these influencing lags, different models are defined for the SAELM and WSAELM models. Also, different mother wavelets are assessed to choose the most optimal one for decomposing signals of the time-series data. After that, superior models for simulating GWL, Cl and HCO3 are introduced by performing a sensitivity analysis. For instance, the values of the correlation coefficient (R), the variance accounted for (VAF) and Nash-Sutcliff efficiency Coefficient (NSC) for the superior WSAELM model are obtained to be 0.988, 97.450 and 0.973, respectively. It should be noted that for forecasting HCO3 the lags (t-1), (t-2), (t-3) and (t-4) are identified as the most influencing lags of the time-series data. The utilization of an uncertainty analysis exhibited that the superior model has an underestimated performance in simulating Cl and HCO3. Finally, three formulae are presented for computing GWL, Cl and HCO3.

The plain of Shiyan, with an area of 207 km2,is one of the most important plains of the Kermanshah province, Iran. However, the framework of hydrogeochemistry of this plain has not been studied yet. This study investigated the hydrogeochemical characteristics of this aquifer through groundwater sampling, geological survey while considering the direction of groundwater flow. Hence, the geological map of the study area was prepared through aerial photographs and field investigation, and then a sampling of groundwater was carried out. After the chemical analysis of the samples, the dominant hydrogeochemical processes in the groundwater system were studied and interpreted according to the geological environment. According to the results, the most abundant process in the aquifer is ion exchange and the groundwater evolves hydrogeochemical as it flows from north-west to east and south-east. The electrical conductivity of the samples taken from the input and output borders of the plain is higher than that of its central zones due to the presence of the Amiran and Kashkan marly formations and maybe the elimination of dolomite from the broke-down zones. The results of the Piper, Stiff, Durov diagrams are consistent and indicate that the dominant water type in this aquifer is the bicarbonate with high magnesium. The Shahbazan Formation has the most influence of terrigenous agents on groundwater quality. Meanwhile, the Amiran and Kashkan formations are responsible for the high concentration of the chemicals found in the groundwater of the east and north of the Shiyan plain. Also, since the plain is entirely under irrigated cultivation and application of chemical fertilizers for increasing agricultural productivity is common in this region, the high level of nitrate in the aquifer should have a human-made origin.

Assessment and controlling groundwater quality have an important role in planning and developing water resources. Therefore, the use of an efficient method can greatly increase accuracy and reduce costs in this field. In this study, 6 optimization algorithms Consist of Particle swarm optimization (PSO), Genetic algorithm (GA), Imperialist competitive algorithm (ICA), Fireflies algorithm(FA), Cultural algorithm(CA) and covariance matrix adaptation evolution strategy- Evolution strategies (CMA-ES) were used to train and optimize the parameters of the neural-fuzzy inference system model (ANFIS) to model the groundwater quality of Bafgh plain in Yazd province. At first, to select the best combination of input for an estimate of the electrical conductivity (EC), sodium adsorption (SAR) and total hardness (TH), Pearson and Spearman's methods were used to analyze the sensitivity and correlation of other parameters. then qualitative modeling is done with hybrid methods and the performance of the models was measured by correlation coefficients (R2), root mean square error (RMSE), and Nash-Sutcliffe Efficiency (NSE). The results showed that all six combined methods showed a very good performance in modeling groundwater parameters. Also, the ANFIS-FA model was one of the best models in all three modeling parts. So that the value of R2, RMSE and NSE for the test part in TH was, 0.99, 0.41, 0.99, for SAR, 0.98, 1.11,0.95 and for EC 0.99, 305.7 and 0.99. Other methods have also succeeded in modeling and predicting the desired parameters with proper precision. According to the accuracy of the calculations, these methods are suitable alternatives for the prediction of groundwater quality variables.

By definition, drought is a period of dryness especially when prolonged that causes extensive damage to crops or prevents their successful growth and has a negative impact on water resources (groundwater resources). The Standardized Precipitation Index (SPI) is a widely used index to characterize meteorological drought on a range of timescales. The SPI can be related to groundwater and reservoir storage. For this purpose, the SPI index was evaluated at 3, 6, 9, 12, 18 and 24 months scales in relation to groundwater level changes in Bandar Abbas plains Hormozgan Province.

The Bandar Abbas plains, including the Isin, Shamil-Takht, Gnu, Choch Dehno, and Sarakhoon plains play an important role in agricultural production and development in the region. In order to investigate the impact of recent droughts on groundwater levels of the above-mentioned plains, meteorological data were obtained from Bandar Abbas Synoptic Station during the period 2001-2005.

The results showed that in all the plains during the years studied, the SPI index is getting worse, that is, the drought is expanding and intensifying. Concomitant to this situation, groundwater levels are also falling. The average water level drop for the whole region was 3.35 m. Changes in the groundwater level were more consistent with the long-term effects of drought so that the highest correlation with changes in groundwater level occurred in the 24-month series.

Drought has a great effect on the groundwater level drop and changes in the groundwater level were more consistent with the long-term effects of drought.

As aquifer feeder and influential parameter in water balance equations and groundwater resources balance, accurate prediction of dams and rivers discharge plays an important role in planning managing and operating optimal and sustainable water resources. In this research, in order to organize the Jamishan catchment area. In order to predict the future natural hazards of the basin, the monthly discharge of this basin is predicted by time series analysis methods. In this regard Was used from monthly discharge data of entrance to jamishan dam in sonqor city of kermanshah province during the period (1360-1389). Initial analysis of data included a review of definitive series semantics (period, trend, jump) done on the time series and after assurance remove these semantics, data was normal and the data stagnation was made. By examining the correlation and partial correlation functions for fifty percent of the data, the self-correlated model (AR) and the moving average model (MA) were fitted for the calibration period to the time series and with the non-correlation test of Port-Manto and the normalization of the remainder, a number of models that did not have these conditions were eliminated, and the best models were identified among the remaining models with Akayek's test. In the verification stage, using the best model during the calibration period, for the fifty-second percent of the data, the prediction verification step was performed. And error validation values were evaluated using white nose, Barlett-Test (Cumulative Rotational), mean of remaining significance, and after the success of the model in the verification prototypes, it was used to predict the monthly discharge of the next 15 years. It can be concluded that the more the model is more static, the analysis of the series is easier and the model with less acacia gives a better answer.