جستجوی مقالات مرتبط با کلیدواژه « سطح آب زیرزمینی » در نشریات گروه « مهندسی آب »

One of the most important issues that has been considered by many researchers in recent years is the study of the phenomenon of land subsidence. The purpose of studying subsidence is to examine the risks and consequences that can result from it over many years. In their research, most researchers considered the occurrence of earthquakes and excessive abstraction of groundwater aquifers due to the drilling of a large number of illegal wells as the most important causes of subsidence. The aim of this study was to obtain a statistical relationship between groundwater level changes and the rate of vertical movement of the earth's surface using linear regression and grade 3 models and using InSAR radar interferometry technique in Varamin plain between 2014 and 2019. Most of the surface of Varamin plain is covered by agricultural lands and therefore it can be said that the uncontrolled abstraction of groundwater is considered as the main cause of vertical movements of the earth. In order to analyze the subsidence that occurred in this plain, Snap software was used and by applying the desired filters to eliminate the noise in the initial images, surface displacement maps were obtained in Varamin plain. The images used for the surface of the earth from 2014 to 2019 were obtained by Sentinel-1 satellite SAR sensor as ascending, and finally, by comparing the obtained statistical models from the fluctuation of the aquifer and the ground, it was found that the linear regression model has a better predictive power than the 3rd degree regression model.

Today, with the expansion of communities and their increasing groundwater exploitation, the scarcity of groundwater resources as a serious problem in most arid and semi-arid regions, is the main concern of managers and planners of the country's water sector. In the present study, it is intended to provide practical solutions by modeling the aquifer and applying the scenario of flood distribution to maintain and improve the aquifer condition. Since Salmas plain is located in the semi-arid region and also due to the uncontrolled abstraction, the Salmas aquifer suffers a severe crisis. To study the changes in groundwater level in Salmas aquifer after applying the flood distribution scenario, the MODFLOW mathematical code in GMS was used. The mathematical model was prepared and implemented for both steady and transient states. Then the flood spreading scenario was applied and the results showed that 2.6 million cubic meters is stored in the aquifer, which is equivalent to 44.7% of the total water fed to the aquifer. Applying the scenario, the water level increased on average by 13 cm at the end of October 2012, and the change in groundwater level was in the southern and middle part of the aquifer.

In recent decades, the application of intelligent evolutionary methods and hybrid models for forecasting groundwater spatiotemporal fluctuations were more focused by researchers. Genetic algorithm and Neuro-Fuzzy are new methods which are applicable in single and hybrid forms for forecasting in complex and nonlinear problems. In this research, aforementioned methods were applied to study the Hadishahr plain aquifer. The Hadishahr plain is located in the north of East Azerbaijan province and it is a part of Julfa–Duzal study area. This aquifer suffers from groundwater level declination due to groundwater withdrawal increase. To achieve practical ways for spatio-temporal groundwater level forecasting, the artificial intelligence methods such as neuro–fuzzy (NF), genetic programming (GP) and combination their best model with geostatistical methods were used. Precipitation and evaporation in t0 time step and groundwater table in t0-1 time step were the inputs to the Neuro-Fuzzy and Genetic Programming. The results showed that the average RMSE of selective piezometers for genetic programming and neuro-fuzzy were 19 and 23 centimeter in the test step, respectively. Then, genetic programming was used to present a hybrid model in combination with the geostatistical model (kriging). Finally, the hybrid model “genetic - kriging” were applied to predict the spatiotemporal prediction of the groundwater level. The simulated results were extended to the whole plain and the area with no groundwater level monitoring network.

In some aquifers such as Kouhdasht alluvial aquifer, lack of sufficient data for groundwater modeling by numerical models constrains us to use the various interpolation methods to determine the required data. Understanding temporal and spatial variations in the depth of groundwater in this region is important for developing management strategies. Among numerous interpolation methods, no method is uniquely optimal, and so the best interpolation method for a specific situation can only beobtained by comparing their results. The records of depth of groundwater for 15 peizometers in Kouhdasht plain were available for three years from 2006 to 2009, allowing us to compare five different interpolation methods based on three selected years (2006-2007, 2007-2008, 2008-2009) as starting points. The five methods were IDW, GP, LP, RBF, Kriging (including OK, SP and UK) and Cokriging (including OK, SP and UK). Cross-validation was applied to evaluate the accuracy of various methods, and two indices- R2 and RMSE- were used to compare the interpolation methods. A Comparison of interpolated values with observed values indicated that Cokriging was the optimal method for interpolating depth of groundwater in this region: it had the lowest value of RMSE and the highest value of R2.