جستجوی مقالات مرتبط با کلیدواژه "groundwater level" در نشریات گروه "آبخیزداری، بیابان، محیط زیست، مرتع"

Salmas Plain represents one of the most critical areas in the country experiencing subsidence. In general, various factors cause land subsidence, but in many areas, the excessive extraction of ground water from aquifers causes land subsidence. The increasing use of ground water, especially in the sites that are accumulated with alluvial deposits, shallow sea or unconsolidated lake, leads to subsidence or collapse of the land. With the excessive extraction of ground water, the water level of the aquifer decreases and the hydrostatic pressure decreases, which makes it possible for the land to subside gradually. Subsidence in plains mostly occurs due to this factor, namely excessive groundwater extraction and compaction of clay and silt layers between aquifers. In this case, even if the water table level rises again, the land cannot return to its original level.

In this study, the susceptibility of land subsidence in Salmas Plain was investigated using layers of influential factors in subsidence with ArcGIS software and fuzzy logic. In the first stage, statistical information on some factors causing subsidence, including groundwater level decline, well extraction rate, aquifer storage coefficient, transmissivity coefficient, precipitation, DEM map, soil texture, and bedrock depth, was collected and raster maps of each of these factors at the aquifer level were prepared. In the next stage, fuzzy layering was performed using fuzzy membership functions based on the impact of decreasing or increasing each of these factors on land subsidence. Subsequently, the maps were combined using fuzzy operators (Gamma OR, AND, SUM, PRODUCT) to obtain a unified map of aquifer subsidence susceptibility. Finally, to select the best combination of operators, the results were compared and evaluated with field observation data and the ROC curve performance index.

The results showed that the OR operator had the lowest conformity with observed subsidence in the area with an AUC of 0.693. Gamma operators with an AUC above 70% had the highest overlap or conformity with observed subsidence in the plain. In this study, the Gamma 0.9 operator was selected as the best fuzzy operator with an AUC of 0.805. The results indicate that the eastern part of the aquifer is critical in terms of subsidence. Approximately 25% of the total area of Salmas Plain, equivalent to 93 square kilometers, has subsidence with very high susceptibility.

Based on the results obtained, it can be said that although the AUC value of the fuzzy operator sum is higher, the Gamma operator with a value of 0.9 has the highest conformity with the ground reality on the fuzzy map, even though it has a lower AUC value. It is essential to mention that the minimum operator AND and Product create a region with low susceptibility, while the maximum operator OR and SUM maximize the susceptible area. They cannot achieve satisfactory performance in preparing a subsidence susceptibility map. Here, they have only been used to demonstrate the inefficiency of fuzzy operators in maximizing or minimizing subsidence susceptibility.

Excessive exploitation of groundwater in Damghan plain has caused many problems such as land subsidence in the study area, along with the reduction of the groundwater level and the loss of a part of the aquifer and the reduction of the specific yield of aquifer. Therefore, in order to control the withdrawal from the aquifer and as a result to reduce the drop in the groundwater level in that plain, it was investigated. For this purpose, in this research, the mathematical modeling of Damghan Plain was done for the optimal management of exploitation and providing the optimal cultivation pattern in order to save water consumption with the focus on controlling the groundwater level and limiting land subsidence.

In this research, Damghan plain aquifer was first simulated using MODFLOW mathematical model. Then, taking into account different scenarios of aquifer exploitation, using the NSGAII genetic algorithm, optimization of multi-objective exploitation of water resources and optimal management of water supply and demand in the agricultural sector was done. The results of the model were evaluated based on statistical error parameters to predict the ground water level.

The results of this research showed that the MODFLOW numerical model was able to simulate the groundwater level of the studied plain well in steady and unsteady conditions in the calibration and validation stages. Also, the results of the simulation-optimization algorithm showed that by changing the cultivation pattern and also by changing the type of irrigation system from traditional to modern irrigation and as a result of increasing the irrigation efficiency (90 percent), the average drop of the groundwater level of the plain can be reduced. It reduced the groundwater level of the study area from 0.49 m in existing conditions to 0.07 m in optimal conditions. This will compensate the deficit of the aquifer from 31.90 to 5.1 million cubic meters per year.

Using management strategies such as changing the cultivation pattern, increasing irrigation efficiency, and also by controlling surface water and injecting it into groundwater, the process of reducing the groundwater level in the studied plain can be completely controlled and provided 100% of the water needs of the plain.

Droughts affects on agriculture and water consumption management through LU/LC and water resources changes in western mountainous areas of Iran. The aim of this study was to investigate terrestrial and remote sensing drought indices and their effects on LU/LC and ground water level in Razan-Ghahavand watershed. Precipitation trend in two stations and water level trend in six piezometric well were studied using Man Kendaland modified Man Kendal and also SPI and SWI drought indices were computed in month, season and year scale during 71-94. LU/LC changes and relationship among SPI, SWI, VCI and TCI drought indices were computed during 2008-2013(two main drought year). The results showed that water level drop(more than 10m) was relate to SPI index with two-year delay significantly. There is a significant correlation with SPI index for VCI index than TCI index and season scale than month and year scales. The finding indicted that agriculture area, gardens and rangelands were decreased 10.96 percent, 13.57 percent and 0.23 percent respectively and also, salin lands was increased 9.51percent during the 2008-2013.

Today, due to the importance of sustainable groundwater management, groundwater level modeling and forecasting are used to assess and evaluate water resources. The purpose of this study is to evaluate the performance of two models of Extreme Learning Machines (ELM) and Artificial Neural Network (ANN) and the combination of two models with wavelet transmission algorithms (W-ELM and W-ANN), which ultimately to increases the predictive power and optimization of input weights (the weights between the input and hidden layers) of models, Quantum Particle Swarm Optimization algorithm (QPSO) has been used. Also, in this study, the data of Ground Water Level of observation wells (GWL), precipitation (P) and average temperature (T) of Urmia Plain aquifer with a time series of 36 years (1981 – 2017) which were collected on monthly scale, are used. Also, in order to evaluate the performance of models, correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used. In this regard, 80% of the data (September 1981 to August 2010) are used for training section and 20% of data (September 2010 to August 2017) used for the test section of models. Based on the results of this study, the hybrid model of W-ELM-QPSO with correlation coefficient (R) 0.991, 0.983 and 0.975, respectively for periods of one, two and three months in the test section, have a better performance than other models and also in addition to predicting power, this model has a high speed in terms of training and testing speed than other models.

Groundwater system studies to understanding its behavior, requires the exploratory drilling wells, pumping test and geophysical experiments, which can carried out with most cost. For this reason, simulation of groundwater flows by mathematical and computer models, which is an indirect method to groundwater studies, is being spent a few costs. In this research, the efficiency of artificial neural network, fuzzy logic and random forest models has been investigated in groundwater level estimation of Boukan plain. Parameters of precipitation, temperature, flow rate and water level within time period of the previous month were used as input and the water table in each period were selected as output through monthly scale (2006-2017). To evaluating the performance of models, Correlation coefficient, root mean square error and coefficient of mean absolute error were used. The results showed that the Fuzzy Logic and Random Forest models are able to estimate water levels with acceptable accuracy. In terms of accuracy, fuzzy logic model with the highest correlation coefficient (0.96), lowest root mean square error (0.068 m0) and mean absolute error (0.056 m) was recognized as a best the model in the groundwater level prediction.

In groundwater resources management, continuous monitoring of wells discharge and charge and groundwater quantitative and qualitative variations are very important. Groundwater monitoring network could provide information on quantitative and qualitative variations in groundwater situations. Hence it would be used to predict the behavior of the aquifer in the future. This study describes an optimizing method to design the optimum groundwater-level monitoring network in aquifer of Eshtehard. Two scenarios were considered to optimize the locations of the observation wells: (1) exploring optimal locations of existing observation wells and (2) adding optimal observation wells to existing well network. Kriging method was utilized to estimate groundwater-level for preparing the database. Optimization of groundwater monitoring network with two objectives (1) minimizing root mean square error and (2) minimizing number of wells, was implemented by non-dominated sorting genetic algorithm (NSGA-II). The results show that by using this method it is possible to remove redundant wells in a monitoring network or to add new wells in case of shortage of observation wells in a network having acceptable accuracy.

One of the most important factors, in a good management in any field, is having a proper perspective of the upcoming events. There is no exception in water resources management and the environment and awareness of the condition of water resources, in an area, plays a decisive role for planning water and agriculture. In this study, the Adaptive Neural Fuzzy Inference System (ANFIS) was used for the monthly forecast of Dalaki Basin groundwater levels in the province Bushehr in a 12-year period (2002- 2013). In order to improve the results of the model, the wavelet transform was used and the original signal was decomposed to sub-signals. Then, sub-signals were entered, as input, into ANFIS model to obtain the hybrid model, Wavelet-Adaptive Neural Fuzzy Inference System (WANFIS). To forecast the groundwater level of five observed wells has been used, using groundwater levels, precipitation, evaporation, andtemperature. Results showed that hybrid model, WANFIS, has better performance than ANFIS model. Also, it was showed that hybrid model has better performance in estimate extreme points. So, this method, using wavelet theory, increased the performance by 14%. At the end, groundwater levels were estimated by the best model in a year. The results of thepredicted groundwater levels showed that theincrease of having access to groundwater in the Dalaki area. This problem is noted to authorities of the area regarding the effects on water resources and the environment of the area.

Groundwater system studies to understanding its behavior, requires the exploratory drilling wells, pumping test and geophysical experiments, which can carried out with most cost. For this reason, simulation of groundwater flows by mathematical and computer models, which is an indirect method to groundwater studies, is being spent a few costs. In this research, the efficiency of artificial neural network, fuzzy logic and random forest models has been investigated in groundwater level estimation of Boukan plain. Parameters of precipitation, temperature, flow rate and water level within time period of the previous month were used as input and the water table in each period were selected as output through monthly scale (2006-2017). To evaluating the performance of models, Correlation coefficient, root mean square error and coefficient of mean absolute error were used. The results showed that the Fuzzy Logic and Random Forest models are able to estimate water levels with acceptable accuracy. In terms of accuracy, fuzzy logic model with the highest correlation coefficient (0.96), lowest root mean square error (0.068 m0) and mean absolute error (0.056 m) was recognized as a best the model in the groundwater level prediction.

In recent years, severe droughts have occurred in most regions of Iran that can be attributed to reduced atmospheric precipitation. The consequence has been a steep fall in the phreatic zone of ground water aquifer. In this study, Standardized Precipitation Index (SPI) at different time scales and Groundwater Resource Index (GRI) were used in order to investigate the state of the recent droughts and their effects on groundwater resources. First, the monthly rainfall data of 6 meteorological stations and the water level of 24 wells in Sarvestan plain of Fars province were collected during the 1991-2012 period. After performing homogeneity tests on the data and eliminating statistical deficiencies by differentiation and ratios, the indicators mentioned were calculated in annual and 3, 6, 9, 12, 18, 24 and 48 months periods. The results of this study showed that during the studied period, the meteorological drought and the groundwater of the plain had an increasing trend and the groundwater table level decreased by as much as 10.9 meters. Correlations between SPI index at different time scales with and without time delay and the average level of groundwater table and GRI index were investigated. Statistical analysis showed that the correlations between SPI index calculated on a 48-month time scale without applying time delay and the mean groundwater level and GRI index were significant at 0.01 level indicating the effect of drought on the underground water of Sarvestan plain. Regression analysis between average height of groundwater level and the SPI showed that 56% of the variance in the mean saturation level was affected by 48-month SPI and 44% were affected by other factors, especially the extra exploitation of groundwater.

Groundwater and water resource management play key roles in water resource sustainability in arid and semi-arid areas. Forecasting groundwater level is very important for water resource management and planning. In this study, an artificial neural network and a particle swarm algorithm based on artificial neural network models have been used to estimate groundwater level in the Ardebil plain. Water table level data for the 1972 -2011 period was used as our data in this study. Model inputs were water table level of various months. Results of both models were evaluated by root-mean-square error, the correlation coefficient and Nash-Sutcliffe coefficient. Results showed the performance of the particle swarm algorithm based on artificial neural network models to be superior. Root-mean-square error results for the particle swarm algorithm model in spring, summer, autumn and winter were 0.476, 0.507, 0.309, and 0.386 respectively. These results show that the hybrid structure of the network in training leads to increased accuracy. Thus, the particle swarm algorithm based on artificial neural network models can be used to estimate groundwater level in the Ardebil plain with acceptable accuracy.

Groundwater has been raised as one of the major sources of water supply for drinking and agriculture, especially in arid and semi-arid. Simulation of groundwater system because of the complexity of these systems is a difficult task. In this paper, using data Ardabil plain water level in the period (1972-2011), the evaluation and selection of appropriate inputs for processing gamma test performance and efficiency of the least squares support vector machines and Bayesian network models were discussed. Monthly water level as input parameters with different delays Gamma test was considered. Gamma test results showed that the water level by 6 latency, offers better results to predict. Water level simulation using least squares support vector machines and Bayesian network models also showed that the input structure to predict the water level the next month will be delayed until six. The two models with the same input structure, least squares support vector machine model, better performance, according to the coefficient of determination 0.977, mean absolute error 0.204 and root mean square error 0.307, compared to Bayesian networks have. The results showed that gamma test compound in the appropriate input soft computing can have a better performance.

Groundwater level prediction is an important issue in scheduling and managing water resources. A number of approaches such as stochastic, fuzzy networks and artificial neural network have been used for such prediction. A neural network model has been employed in this research for Shahrood plain groundwater level prediction. For this reason, statistical parameters of groundwater level fluctuations for 16 successive years 1994 to 2010, have been used and also, weather forecasting parameters for 16 successive years from 1994 to 2010 have used. This study indicates that some of the data are correlated an possesed a seasonal pattern. This issue makes difficult the forecasting process. Hence, the proposed method employed policies for non season analyzing, normalizing, and ignoring the correlated date. 85% of the data for train and the rest for testing the proposed neural networks model have used. Results indicate that the proposed method can predict groundwater level of Shahrood Plain for three successive years with the mean square errors of 0.0257m, 0.0270m and 0.0452m. Also, the prediction shows that if the precipitation decreases 30 percent in a year, the groundwater level will decrease 0.7 m.

The Hadishahr plain, with 56 km2 area, is located in the northwest of the East Azarbaijan province. Due to the intensive withdrawal of groundwater from this area in the recent years, the water level has been declined significantly. In order to find the best method for managing the groundwater resources of the study area efficiently, artificial neural networks and fuzzy methods were utilized to model and predict the temporal and spatial variations of the groundwater level. Firstly, the central piezometer was used for modeling artificial neural network and determining the best algorithm structure. The results show that the forward neural network with the LevenbergÜMarkvrat (LM) algorithm with 1, 2 and 3 order structure is the best method in this plain, respectively. Afterward, the selected piezometers of the plain were classified with the hierarchical clustering (HCA) methods and each piezometers batch was modeled with the Sugeno fuzzy technique. The results were compared using the statistical parameters such as RMSE and R2. In this study, monthly data of rainfall, evaporation, and groundwater level were used as inputs to the model. The results show that the fuzzy and neural network techniques using feed forward neural network with the Levenberg-Markvrat (LM) algorithm achieves the best answer. Thus the neural kriging and neural cokriging method were used, for predicting the temporal and spatial variations of groundwater level.

Water resources management especially for groundwater resources is a very important task in arid and semi-arid regions. Over last decades, because of various natural and anthropogenic factors, critical condition and declined groundwater level have been reported in most regions of the country. With regard to importance of this issue variation pattern of groundwater level in Garmsar plain was assessed using the best geostatistics estimator method over 2002-2011 period.‌‌ Among the various interpolation methods including Kriging and inverse distance weighting (IDW) with power 1-3, Kriging method was determined as the best interpolation method based on RMSE criterion. Results showed that spatial zoning maps of groundwater level in this period indicates intense depletion of groundwater level in the northwest of the region. Results also show that the average level of groundwater in the whole plain has decreased 3.5 m because of decreased rainfall and over-pumping of groundwater for agricultural uses in the period.