Hydrogeomorphology - Articles List

Groundwater

New Z-Number-Based Method for Specialized Groundwater Vulnerability Assessment (Case studies: The Ardabil and Qorveh-Dehgolan plains)

Sana Maleki; Vahid Nourani; Hessam Najafi

Volume 11, Issue 38 , April 2024, , Pages 122-98

https://doi.org/10.22034/hyd.2024.59132.1713

Abstract

Systems for assessing groundwater vulnerability are designed to protect groundwater resources from pollution. The DRASTIC method is a well-known approach for determining groundwater susceptibility. One drawback of the DRASTIC method is that it relies on expert judgment to rank parameters, which introduces ... Read More

Groundwater

Evaluation land subsidence in Gorgan plain

Mohammad Hossain Motedayen; Mehrdad Esfandiari; Abolfazl Moeini; Ali Mohammadi Torkashvand

Volume 10, Issue 35 , August 2023, , Pages 119-102

https://doi.org/10.22034/hyd.2023.55996.1688

Abstract

In recent years, the irreversible phenomenon of land subsidence has led to environmental hazards in various plains of Iran including Gorgan. In general, the most important activities causing this phenomenon are inappropriate groundwater withdrawal and geological factors. The research method consists ... Read More

Hydrogeomorphology

Spatial modeling of groundwater capacity using a combination of maximum entropy method and random forest method in GIS environment Case study: Ardabil Ghorichay catchment

Jafar Jafarzadeh; Meysam Argany

Volume 9, Issue 31 , August 2022, , Pages 116-95

https://doi.org/10.22034/hyd.2022.50729.1631

Abstract

Groundwater is one of the most important natural resources in arid and semi-arid regions. The purpose of this study is to identify areas that have groundwater capacity and to prioritize the factors affecting it. In this study, 11 indicators affecting groundwater capacity including Slope, Elevation, Aspect, ... Read More

hydrogeology

Effective Factors in Ground Water Variations and Water Table Decrease in Ardabil Plain

Sina Ziaye Shendershami; Abazar Esmali Ouri; Raoof Mostafazadeh; Ardavan Ghorbani

Volume 8, Issue 28 , December 2021, , Pages 127-143

https://doi.org/10.22034/hyd.2021.46333.1590

Abstract

The aim of this study was to investigate the factors affecting the decrease and change of groundwater level in Ardabil plain in two periods 1995 to 2005 and 2005 to 2015. The monthly precipitation data of Ardebil, Nir, Namin, Abi baglo, Hir, Samiyan stations in the Ardabil plain during the statistical ... Read More

Potential mapping groundwater resources by using an integrated approach AHP and Fuzzy Topsis (Case Study: Silakhor Plain)

Mohammad Saeidi; Mehdi Komasi; Shahab Hasanpor

Volume 8, Issue 26 , May 2021, , Pages 59-41

https://doi.org/10.22034/hyd.2021.37835.1548

Abstract

Over the past few decades, as a result, population growth, industrialization, urbanization, etc., demand for water has increased, most of these requirements have provided by exploiting groundwater resources. Therefore, the uncertainty in the demand and supply of water should be minimized by proper groundwater ... Read More

Vulnerability of Hajiabad plain aquifer based on DRASTIC and SINTACS models

mehrdad hassanzadeh; mehdi momeni reghabadi; amir robati

Volume 8, Issue 26 , May 2021, , Pages 202-183

https://doi.org/10.22034/hyd.2021.45261.1584

Abstract

1-IntroductionGroundwater pollution is one of the most serious and important issues in urban and agricultural areas due to land use. For this purpose, in order to obtain methods and garbage water from the pollutants that removes them, the use of methods for garbage water vulnerability assessment ... Read More 1-IntroductionGroundwater pollution is one of the most serious and important issues in urban and agricultural areas due to land use. For this purpose, in order to obtain methods and garbage water from the pollutants that removes them, the use of methods for garbage water vulnerability assessment such as AVI, GODS, DRSTIC, SINTACS, etc. were developed. Intrinsic vulnerability is assessed according to the hydrological and hydrogeological characteristics of the region, such as the characteristics of the aquifer and the stresses imposed on it. Occurs with inherent vulnerability components. The most common methods of assessing vulnerability index include DRASTIC, GOD, SINTACS, SI and AVI rating methods. In this study, the vulnerability of the aquifer has been investigated using DRASTIC and SINTACS models, and in order to validate the results of the methods used, electrical conductivity concentration data were used. 2-MethodologyHajiabad plain is located 160 km north of Bandar Abbas and between 35, 55 to 00 and 56 longitudes and latitudes 17, 28 to 21 and 28 north, from the north to the heights of Bibi Dokhtaran mountain from the west to Sirjan-Bandar Abbas road from To the east to the heights of Anfuzeh mountain and from the south to the congomara hills and the average width is 4 km. The climate of the region is warm and the average temperature of the region is 19.8 degrees Celsius and the average annual evaporation of the plain is 2464.7 mm. In order to study the hydrochemical properties of groundwater in the region, 16 samples of water analyzed from groundwater study wells by the Regional Water Organization of West Azerbaijan Province for the water year 93 were used.3- Results and DiscussionVulnerability maps of Drastik and SINTACS models were prepared by applying weights related to each parameter and combining layers using the overlap function. According to the SINTACS map, the vulnerability of the plain is estimated from 115 to156, the plain is in the range of medium, medium to high and high vulnerability. According to the vulnerability classification with SINTACS model, it shows that parts of the center of the plain (near Aliabad and Hajiabad villages) are in the upper floor and the northern slope of the Hajiabad plain basin has the middle floor. Most of the plain area was in the range of moderate to high vulnerability. The results showed that the Syntax model has more flexibility than the Drastic model and the probability of vulnerability is slightly higher than the Drastic model. The final map of Drastik model estimated the vulnerability of the plain from 94 to 128. The highest vulnerability is in parts of the center of the plain (near Aliabad and Hajiabad villages) and the lowest in the northern slope of Hajiabad plain basin and according to the range of Drastic vulnerability index provided by Aller Et al, (1987), vulnerability of the region is divided into 3 categories between low to medium risk. In order to study more closely and also to compare the classical methods used in this study, the method of calculating the correlation index (CI) in the aquifer and electrical conductivity data were used. For this purpose, electrical conductivity values were divided into three categories of low, medium and high electrical conductivity. Adaptation of wells with three levels of EC pollution and vulnerability categories predicted by DRASTIC and SINTACS methods was brought for Hajiabad aquifer. Based on the value of the correlation coefficient between the map produced using the drastic model with the electrical conductivity map, 39 and the same value was obtained for the Syntax model 35, which are slightly different from each other.4-Conclusions In this study, both drastic and syntactic methods predicted the potential risk in Hajiabad aquifer with almost equal accuracy. Having the correlation index between the electrical conduction point data and the vulnerability map, it showed that the Drastic model provided better vulnerability than the SINTACS model. Contamination potential in both studied models is low in the northern and southern regions. This can be due to high groundwater depth and low hydraulic conductivity. Comparing the models with the coefficient of determination between the electrical conductivity concentration and the vulnerability parameters showed that the highest correlation was in the slope layer, depth to the water table and the material of the unsaturated medium.Keywords:Aquifer vulnerability, SINTACS Method, Groundwater, Hormozgan5-References Aller, L., T. Bennet, J.H. Lehr, R.J. Petty, and G. Hackett. (1987). DRASTIC: a standardized system for evaluating groundwater pollution potential using hydrogeological settings. EPA/600/2–87/035. US Environmental Protection Agency, Ada, OK, USA.

Investigating the Qualitative Satus of Groundwater in the Plain of Khorasan Razavi Province Using GWQI and AWQI Indexes and Its Zoning with Geographic Information System (GIS

Ali Shahidi; Fahime Khadempour

Volume 7, Issue 22 , June 2020, , Pages 1-20

https://doi.org/10.22034/hyd.2020.10802

Abstract

1-Introduction Increasing water consumption due to population growth has led to a reduction in the quality and quantity of extracted water. Given this situation, quantitative and qualitative knowledge of suitable sources for drinking and farming is necessary and inevitable. Meanwhile, ground water ... Read More 1-Introduction Increasing water consumption due to population growth has led to a reduction in the quality and quantity of extracted water. Given this situation, quantitative and qualitative knowledge of suitable sources for drinking and farming is necessary and inevitable. Meanwhile, ground water is considered a safe source for water supply. Today, due to the excessive use of groundwater resources in many plains, water levels have fluctuated and groundwater levels have fallen, and these plains have been hit by decreasing water quality. Therefore, water resources control and optimal use of them are of high priority. Since, groundwater resources are considered as important sources of water supply for various uses. In order to better understand the qualitative status of water resources, water quality indicators are used. To do this, by conducting experiments on water samples and using mathematical relationships defined for each index, a value is obtained that can be used to describe the qualitative state of water based on it and refer to the relevant tables. 2-Methodology Based on international standards for drinking and agriculture uses, some of the parameters examined are lower and others are more than global standards. Basically, these differences indicate the presence of pollution in various water sources. The water quality index is, in fact, simply a numerical value that reduces the large amounts of data, including physical, chemical, and biological parameters, and generally indicates the overall quality of water for various uses, especially for drinking. Typically, heavy metals are included in the water quality index to assess overall water contamination. In this study, the quality of groundwater in the Jangal plain in Khorasan Razavi province has been investigated. 10 wells in this plain were analyzed for concentrations of Ca2+, Mg2+, Na2+, HCO3-, SO42-, Cl-, pH and TDS with GWQI and AWQI indices in the period 2007-2016. Also, plain zoning was performed using the GWQI index and Arc GIS 10.2 software. In cases where the AWQI value is zero, it means that there is no pollutant in water, and if this value reaches 100, that is, all pollutants have reached their maximum permissible limit. The high level indicates the high level of contamination and the passage of this value from 100 indicates serious contamination. 3-Results The results showed that most of the Jangal plain wells pollution are less than the contamination level during the statistical period. Fayez Abad, Kheirabad Ali Akbar Rahmani and Bandazik Salehi Wells have passed through the limit of contamination over the years and have serious pollution and are not suitable for drinking and farming. Based on the zoning, the indicator status in all wells (except wells of Fayez Abad (well No. 2), Kheirabad Ali Akbar Rahmani (well No. 9), and Badazaki Salehi Forest (well No. 10) have inappropriate water (red)) and in the whole area of the Jangal zone is poor (orange color), so the pollution of these wells is not serious and is suitable for drinking and farming. The total amount of all parameters except total dissolved solids (TDS) in all wells is standard. The reason for this is the lack of industrial activities and human communities near these wells. The highest mean total dissolved solids is 5378.49 mg/l. In this study, the least amount of GWQI and AWQI indices for Janet Abad Khordemalkin well were 69.66 and 56.49 (highest quality in 2008) and the highest of these two indicators were 239.12 and 189.48, respectively. 4- Conclusion According to the results of this study, the GWQI index in the region ranges from 69.66 to 239.12 and the AWQI index is between 56.49 and 189.48, that is, the quality of groundwater in the Jangal area is weak and inappropriate. The cause it is also the high solids content of the total solution of water. In fact, in this region, all of the measured quality parameters, except the total dissolved solid, are at the standard level. According to the results of this study, although the amount of calcium and magnesium is in the standard range but at lower levels, and given the body's need for these micronutrients, it is necessary to plan the provision of these elements through other sources or add them to the water in the refinery.

The Application of the Entropy and Empirical Basin Kriging for the Optimization and Spatial Interpolation of Groundwater Monitoring Network (Case Study: Silakhor Plain)

Mehdi Komasi; Hesam Goudarzi

Volume 6, Issue 19 , September 2019, , Pages 145-162

Abstract

Introduction Groundwater monitoring has an important role in water resource management. Groundwater monitoring network can provide groundwater levels, but sometimes this information is too much and not useful. Optimum water management requires sufficient information on the quantitative and qualitative ... Read More Introduction Groundwater monitoring has an important role in water resource management. Groundwater monitoring network can provide groundwater levels, but sometimes this information is too much and not useful. Optimum water management requires sufficient information on the quantitative and qualitative features of the aquifer. Assessing and anticipating the level of groundwater through specific models helps to predict groundwater resources. In recent years, with the demonstration of the capabilities of smart models in modeling time series, these models have been enhanced in groundwater modeling. The optimization of the monitoring network is a process for having the best combination of existing stations, adding new stations, or redefining a monitoring network based on the predetermined goals. Methodology The present study aimed to develop an optimal network monitoring model with two goals of maximum entropy value at monitoring stations and minimizing the costs of monitoring the groundwater level network. In this study, data from 29 stations in the current monitoring network were used to optimize the groundwater monitoring network in Silakhor plain located in Lorestan Province. Firstly, using the entropy method, the amount of entropy in each of the 29 stations of the monitoring network was determined. Then, the amount of the entropy matching for each station was compared with the amount of entropy of the time series of rainfall, in order to determine the optimal station water supply through the available network. Finally, the prediction of the location of data using the Empirical Bayesian Kriging (EBK) method in the ArcGIS software was carried out and evaluated by the results of four models of the K-Bessel Detrended, K-Bessel, Exponential Detrended and Whittle Detrended. Results The time series of rainfall is directly related to the feeding of aquifers. In fact, the amount of precipitation entropy and its variations at different times affect the amount and variation of entropy of groundwater level at the same time. If the entropy value obtained from the station is not consistent with the season's entropy variations in rainfall, it is said that the station is able to scare water based on the uncertainties of the rainy season and aquifer feeding. The results of the research showed that the entropy variations for eleven stations of the existing stations were similar to the entropy variations of the rainy season, and also the entropy of this station was higher than the other stations. The results obtained from the first step indicated a network with 11 stations out of 29 available stations. The RMSE value of this network was 0.75 m. At this point, by reducing 62% of the network station, peak network costs and RMSE value were optimized. The comparison of the optimal network and the existing network showed that the optimal network could reduce cost of monitoring stations and had a similar zoning in Silahkor plain rather than existing monitoring network. Data interpolation was modeled in ArcGIS software and in the Geostatical Analysts section by the EBK method. The absoluteness of the estimation in interpolation and location is one of the main features of the EBK model. In this sense, the value of the estimate of the quantity at the sampling points was equal to the measured value and the estimate of variance was zero. The EBK model uses four semi- modifications to interpolate the groundwater level. The least square standardized error between the actual and estimated values in the EBK method was K-Bessel Detrended with a half-value of 0.99. In addition, the EBK method with the K-Bessel Detrended half-change was based on the average mean power of error (20.87) and the highest correlation coefficient (0.82) was the best interpolation method. The EBK methods were respectively ranked in the Whittle Detrended, K-Bessel and Exponential Detrended models. Discussion and conclusion Considering that the implementation of water resources monitoring programs is costly and requires time, a method for optimizing the existing network is necessary. The results of the research were suited to the adequacy of the network with 11 stations of 29 monitoring stations for the Silakhor plain aquifer. The optimized monitoring network, in comparison with the existing observation network, was able to reduce the number of stations in the monitoring network by 62% and improve the spatial distribution of stations. In addition, investigating the predictive results of the groundwater level and comparing it with the actual level in the aquifer area indicated the accuracy of the EBK method. In addition, the comparison of the aquifer zoning using a network with 29 monitoring stations with the network with 11 monitoring stations showed the level of groundwater level with an acceptable estimate, which indicated that the precision of the entropy criterion was in the selection and optimization of the monitoring stations.

Using Hybrid Wavelet-Support Vector Machine and Wavelet-Neural Network Models for Groundwater Level Prediction in Ardabil Plain

Farnaz Daneshvar Vousoughi; Vahid Manafianazar

Volume 5, Issue 17 , March 2019, , Pages 45-64

Abstract

Abstract Groundwater has played an important role in the urban and rural water supply and agriculture. In order to manage water resources, an accurate and reliable groundwater level forecasting is needed. In this research, 15 piezometers in Ardabil plain were used. SVM was applied for a prediction method ... Read More Abstract Groundwater has played an important role in the urban and rural water supply and agriculture. In order to manage water resources, an accurate and reliable groundwater level forecasting is needed. In this research, 15 piezometers in Ardabil plain were used. SVM was applied for a prediction method in one month-step-ahead. Clustering tool and Wavelet Transform (WT) as spatial and temporal pre-processing and an artificial neural system for modeling were also used. The results showed that the values of R2 coefficients in calibration and verification of prediction were respectively 0.94 and 0.89. On the other hand, the application of the WT to groundwater level data increased the performance of the model up to 3% and 5% for calibration and verification parts. The performance of the SVM model was compared to the proposed combined WT–ANN and ANN models. The results showed that the values of R2 coefficients in calibration and verification of prediction were respectively 0.94 and 0.88. The application of the WT to groundwater level data increased the performance of the model up to 3% and 7% for calibration and verification parts. The results obtained by the SVM model showed the improved performance of modeling and its combination with WT showed the best performance in the pre-processing of the modeling. Also the results of the ANN and hybrid WT-ANN models yielded good performance. Also, the results of the hybrid WT-ANN models showed slightly better results than the ANN model in some clusters. Introduction Recently, Artificial Intelligence (AI) approach, as a new generation of robust tools, has been developed for time series forecasting purpose. As such forecasting tools, Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been extensively employed at different engineering fields. Among such AI models, the capability of the commonly used ANN models to approximate nonlinear mappings between inputs and outputs makes it a useful tool for modeling hydrological phenomena. However, ANN-based modeling may include some shortcomings, such as over fitting, convergence to local minima and slow training, which make it difficult to achieve adequate efficiency when dealing with complex hydrological processes [12]. Support Vector Machine (SVM), proposed in [13], is one of the most persuasive forecasting tools as an alternative method to ANN. SVM is based on the structural risk minimization principle and Vapnik–Chervonenkis theory, and involves solving a quadratic programming problem; thus, it can theoretically get the global best consequence of the primal problem. In recent decades, SVMs have been implemented in several hydrological fields and in groundwater levels. In this paper, the conjunction of SVM and the wavelet-based data pre-processing was examined by proposed Wavelet-SVM (WSVM) in modeling groundwater level for one month ahead. The proposed models were also compared with single SVM, ANN and Wavelet-ANN (WANN) models. The plain of Ardabil (38 – 38 N and 47 – 48 E), located in the north-west of Iran, covers an area of about 990 km2. In this plain, 15 piezometers (wells) are operated to measure the GWLs. The data sampling has been reported in one-month intervals for all of the piezometers. The plain is equipped with one runoff gauge at the outlet and 6 rain gauges within the watershed. Fig. 2 shows the position of piezometers as well as rainfall and runoff gauging stations. The monthly rainfall, runoff, and GWL data were available from 1988 to 2012 and used in this study. About 18 years of data were used for the training, and the remaining 7 years for the validation. Support Vector Machine SVM as a powerful methodology was used for solving problems in non-linear classification, function estimation, and density estimation. Via SVM, a non-linear function can be shown as: (1) where f indicates the relationship between the input and output, w is the m-dimensional weight vector, φ is the mapping f unction that maps x into the m-dimensional feature vector and u is the bias term. Artificial Neural Network (ANN) ANN is widely applied in hydrology and water resource studies as a forecasting tool. In ANN, feed– forward back–propagation (BP) network models are common to engineers. The Feed forward neural network (FFNN) is widely applied in hydrology and water resource studies as a forecasting tool. Three-layered FFNNs, which have usually been used in forecasting hydrologic time series, provide a general framework for representing nonlinear functional mapping between a set of input and output variables. The explicit expression for an output value of a three layered FFNN is given by (Kim and Valdes, 2003): (2) where i, j and k respectively denote the input layer, hidden layer and output layer neurons. wji is a weight in the hidden layer connecting the i th neuron in the input layer and the j th neuron in the hidden layer, wjo is the bias for the j th hidden neuron, fh is the activation function of the hidden neuron, wkj is a weight in the output layer connecting the j th neuron in the hidden layer and the k th neuron in the output layer, wko is the bias for the k th output neuron, fo is the activation function for the output neuron, xi is i th input variable for input layer and k and y are computed and observed output variables, respectively. NN and MN are respectively the number of the neurons in the input and hidden layers. The weights are different in the hidden and output layers, and their values can be changed during the network training process. Wavelet transform (WT) The WT has enlarged in occupation and popularity in recent years since its inception in the early 1980s, but the widespread usage of the Fourier transform has yet to occur (Grossman and Morlet, 1984). In real hydrological problems, the time series are usually in the discrete format rather continues and, therefore, the discrete WT in the following form is usually used (Mallat, 1998): (3) where m and n are integers that respectively control the wavelet dilation and translation; a0 is a specified fined dilation step greater than 1; and b0 is the location parameter and must be greater than zero. The most common and simplest choice for parameters are a0 = 2 and b0 = 1. This power-of-two logarithmic scaling of the dilation and translation is known as the dyadic grid arrangement. Self Organizing Map (SOM) SOM is an effective software tool for the visualization of high-dimensional data. It implements an orderly mapping of a high-dimensional distribution onto a regular low-dimensional grid. Thereby, it is able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display while preserving the topology structure of the data (Kohonen, 1997). The way SOMs go about reducing dimensions is by producing a map of usually 1 or 2 dimensions which plot the similarities of the data by grouping similar data items together. The SOM is trained iteratively: initially the weights are randomly assigned. When the n-dimensional input vector x is sent through the network, the distance between the weight w neurons of SOM and the inputs is computed. The most common criterion to compute the distance is the Euclidean distance (Kohonen, 1997): (4) Results and Discussion The results of the proposed one-step-ahead GWL modeling using pre-processed data by SVM and WT-SVM were given. The SVM-based results were also compared with those of the ANN-based model. Results of clustering Due to the existence of various piezometers over the Ardabil plain and the importance of managing groundwater resources, it is a necessity to unite the adequate information about GWLs in various regions of the plain and identify the dominant piezometers to predict GWL conditions of the plain in the future. In order to accomplish the spatial clustering, an SOM was utilized to identify similar and predominant piezometers. The SOM classifies the similar piezometers (with similar temporal patterns and seasonalities) into the same classes. The clustering results of piezometers into 5 clusters are shown in Table 1. It is clear that clustering was achieved in the direction of main stream flow and probably groundwater flow regime was parallel with the surface water toward the outlet in the northwest of the plain. To evaluate the performance of the clustering results produced by SOM, the Silhouette coefficient was used as a measure of cluster validity. The Euclidean distance was then utilized to select the centroid piezometer of each cluster, which was the best representation of the GWL pattern of the cluster. Table (1) The results of clustering Cluster NO. Piezometers Silhouette Coefficient Central Piezometer Cluster 1 P4, P9 0.42, 0.34 P4 Cluster 2 P2, P12 0.46, 0.72 P12 Cluster 3 P1, P8, P11 0.45, 0.58, 0.11 P8 Cluster 4 P6, P7, P10, P14 0.41, 0.62, 0.40, 0.54 P7 Cluster 5 P3, P5, P13, P15 0.65, 0.71, 0.53, 0.51 P5 Results of SVM and ANN The results of one-step-ahead for all 5 central piezometers of clusters are shown in Table 2. As mentioned previously, for each ANN, the dominant input variables (column 2, Table 2) were determined by linear correlation, in which Pi(t) and Ij(t) respectively indicate the GWL and rainfall time series of central piezometer i and rainfall gauge of j. Q(t) is the outflow time series from the outlet of basin. The results of one-step-ahead indicated that all of the models produced acceptable outcomes, and confirm the appropriate identification of the representative GWL patterns over the watershed. Cluster 1 did not show reliable results because the Silhouette coefficient of P4 had a lower value than 0.5, which shows that cluster 1 had a weak structure. Piezometers in cluster 3 showed better results than cluster 1, despite the large utilization in the region which was due to being close to the outlet of the plain and accumulation of water of other regions near the outlet area. Other clusters showed superior results since they were near the supplying and recharging resources and in the highlands of plain. Therefore, the spatial clustering not only can enhance the modeling performance by grouping the similar time series within the same clusters but also it can identify the piezometers and regions with irrelevant data due to artificial and/or external impacts on the system. Table 2 Results of ANN and SVM models for one-step-ahead predictions Cluster NO. Input variable Output variable Model Type R2 RMSE (Normalized) Calibration Verification Calibration Verification Cluster 1 P4(t), P4(t-1), I4(t), Q(t) P4(t+1) SVM ANN 0.977 0.977 0.958 0.951 0.006 0.006 0.005 0.005 Cluster 2 P12(t), P12(t-1), Q(t) P12(t+1) SVM ANN 0.944 0.935 0.86 0.869 0.041 0.044 0.035 0.034 Cluster 3 P8(t), P8(t-1), I3(t-1), Q(t-2) P8(t+1) SVM ANN 0.99 0.996 0.99 0.992 0.023 0.015 0.015 0.014 Cluster 4 P7(t), P7(t-1), I4(t-1), Q(t-1) P7(t+1) SVM ANN 0.819 0.832 0.667 0.677 0.038 0.037 0.023 0.022 Cluster 5 P5(t), P5(t-1), Q(t-1) P5(t+1) SVM ANN 0.955 0.97 0.94 0.94 0.006 0.005 0.004 0.004 Results of WANN and WSVM models In addition to spatial patterns, some temporal features may also exist in the GWL process due to highly non-stationary fluctuations of the time series. To handle such features, wavelet-based temporal pre-processed data were entered into the ANNs or SVM in order to improve the modeling accuracy. The hybrid model, Wavelet-ANN (WANN) and Wavelet-SVM (WSVM), were simultaneously designed to catch the non-linear GWL modeling. Due to the structure of the Daubechies-4(db4) mother wavelet which is almost similar to the GWL signal, it could capture the signal’s features, especially peak values, and was selected as the mother wavelet for the decomposition of the GWL time series in this study. The decomposition of the main GWL time series at level L yields L+1 sub-signals (one approximation sub-signal, Pa(t) and L detailed sub-signals, Pdi(t) (i=1, 2, …, L)). The decomposition level 3 was considered as the optimum decomposition level. The decomposed sub-series of GWL (each resolution demonstrating a specific seasonal feature of the process) accompanied by the rainfall and runoff data of each cluster were used in the FFNN and SVM models in order to predict one-month-ahead GWL values. The results of WANN and WSVM models for one-step-ahead forecasting are presented in Table 3. The WANN and WSVM results of one-step-ahead showed that the performance of models for all clusters were accurate during both training and verification periods. According to Table 3, the results obtained by the WANN model show the improved performance of modeling in comparison to the ANN modeling. It is clear from the performance criteria that all WSVM yielded slightly better results than the WANN (except for clusters 1 and 5 in scenario 2). Table 3 Results of WANN and WSVM models for one-step-ahead predictions Cluster NO. Input variable Output variable Model Type R2 RMSE (Normalized) Calibration Verification Calibration Verification Cluster 1 Pi4(t), I4(t), Q(t) P4(t+1) WSVM WANN 0.993 0.988 0.973 0.975 0.003 0.005 0.004 0.004 Cluster 2 Pi12(t), Q(t) P12(t+1) WSVM WANN 0.962 0.968 0.901 0.916 0.033 0.031 0.029 0.027 Cluster 3 Pi8(t), I3(t-1), Q(t-2) P8(t+1) WSVM WANN 0.997 0.997 0.995 0.995 0.013 0.013 0.011 0.011 Cluster 4 Pi7(t), I4(t-1), Q(t-1) P7(t+1) WSVM WANN 0.898 0.922 0.822 0.861 0.028 0.025 0.017 0.015 Cluster 5 Pi5(t), Q(t-1) P5(t+1) WSVM WANN 0.979 0.971 0.967 0.963 0.004 0.005 0.003 0.003 Concluding Remarks In this paper, ANN based models were developed for GWL forecasting over the plain of Ardabil, in the north-west of Iran. The inputs of the AI models were monthly rainfall, runoff, and GWL at 15 piezometers over the study area. Data pre-processing via SOM and WT were shown to be useful tools in improving AI based GWL forecasting models. The proposed methodology was applied to Ardabil plain data to find one-month-ahead forecasts of GWL. As a result, the entire study area was divided into five clusters with SOM clustering scheme and then AI modeling was performed separately for each cluster. In order to improve model efficiency and consider seasonality effects, the WT which can capture the multi-scale features of a signal, was used to decompose GWL time series into different sub-signals at different levels. The sub-signals were then used as inputs of the AI models to predict GWLs. Overall, the results of this study provide promising evidence for combining spatial and temporal data pre-processing methods, and more specifically SOM and WT methods, to forecast GWL values using the AI method. One of the advantages of the proposed method is that by using a clustering method it is possible to identify piezometers and regions with good and bad data quality. In order to complete the current study, it is recommended to use the presented methodology to forecast the GWL by adding other hydrological time series and variables (e.g., temperature and/or evapotranspiration) to the input layer of the model. Moreover, due to the uncertainty of the rainfall process and the ability of the Fuzzy concept to handle uncertainties, the combination of the ANN and fuzzy inference system (FIS) models as an adaptive neural-fuzzy inference system (ANFIS) model, could provide useful results. It would also be useful to apply the proposed methodology in other heterogeneous groundwater systems in order to investigate the overall effect of the climatic conditions on the performance of the proposed model.

The Effect of Geomorphologic Factors on Feeding Underground Water Resources in Kermanshah Meyandareh Plain

Mahmood Alaei Taleghani; Najmeh Shafiei; Marzyeh Rajabi

Volume 4, Issue 13 , March 2018, , Pages 21-41

Abstract

Introduction Groundwater resources, due to being sweet and having chemical compounds, fixed temperatures, lower pollution rates, and higher levels of reliability in supplying water resources, are considered as reliable resources, especially in arid and semi-arid regions. In addition, due to the ecological ... Read More Introduction Groundwater resources, due to being sweet and having chemical compounds, fixed temperatures, lower pollution rates, and higher levels of reliability in supplying water resources, are considered as reliable resources, especially in arid and semi-arid regions. In addition, due to the ecological potential of the region, it is an important and effective phenomenon in the economic development, ecological diversity, and community health. Relying on groundwater resources, especially in dry and semi-arid lands, has led many scholars to study how to form or access them. The main objectives of this research are to investigate the effective role of geomorphologic factors in the potential of underground water resources in the region and the possibility of proper management of water resources in the studied basin and to be more knowledgeable about groundwater issues,. Therefore, studying and identifying the hydro geomorphology of the area and the factors affecting the aquifers is essential. The study area is located in the geographical boundary of the west of the country in the northeast hillside of the Zagros range. The area of Meiandareh, with an area of 329 km2, is located in the northern part of Kermanshah Province. Methodology The method used in this research was based on the analytical and weight-empirical analysis carried out in separate steps. First, an inferential method was used to determine the direction and amount of groundwater flow, the role of nutrition of various geomorphological phenomena in the plain, the position of the piezometer wells, and the groundwater level map for the plain. Second, weighing index based on expert opinion and expert of Delphi-completed questionnaire of various weighted layers were used. Finally, the potential of the plain and its favorable regions were studied using the pairwise G.I.S. software. Discussion In the maps of the groundwater level of the plain, it was indicated that while the maximum level in the eastern margin of the eastern part of the region at the beginning of the apple flank was about 25 m, in the boundary of the Ghareh Souz River and flood plains, it was about 3 m. Thus, the groundwater flows from the northern and eastern parts to the central parts and outlet of the basin. Indeed, the farther from the heights, the lower the thickness and the higher the level of the stairway. Therefore, the river is located in the Al-Qaer plain line and plays the role of the drainage of the plain and the outlet of the water of the upper land. In 1382, the water table was the lowest with a depth of 3-16 m. In 2009, however, it was the highest water table with a depth of 3.17-25 m. it was also shown that there was a decline in the amount of the groundwater since 1388 in comparison to 1382 due to harvesting. Conclusion The map obtained from the composition of the layers indicated the importance or weight of each zone in the groundwater potential. The final configuration was divided into three classes with a very suitable, appropriate, and inappropriate potential. Regarding the results and the status of discharge, the eastern and central boundaries of the middle reaches have high potential for the artificial feeding of groundwater. There is also a lower risk for drilling wells. In general, the aquifer of the plain is considered as the limit of humidity and rainfall absorption and water supply required by the middle reaches plain. Physical weathering of the rocks and proper rangeland cover caused plenty of gaps and increased groundwater nutrition in this area. It seems that one of the important reasons for water guidance in the axis of the plain of the navy building and the direction of the slopes of the China's flanks is the drainage of the surface water and the underground water. However, the volume of groundwater in the plain is the only function. The result of the study of water behavior in exploratory and piezoelectric wells has shown that the low drainage density plays the main role in feeding plain in flood plains, coniferous fringes of eastern plains, slopes of 0-2%, and low altitudes. These lands are usually highly influential and because of the fertility and access to surface and underground water resources, the establishment of the demographic and agricultural lands can be seen within them. A significant level of plain lands is flood plain, which plays a very important role in the nutrition of groundwater resources of the plain. According to the maps, the depth of the groundwater level, the main flow of underground water in the plain are from north to south, which indicates that the main river plain in this region plain and evacuates underground water from the area. Sedimentary plain with infiltration infrastructure and young alluvial coverage is the most potential area for water resources in Meiandareh.

The Study of the Periods of Drought and its Impact on Groundwater Resources of the Gorgan Roud River Basin

Narjes Bay; Shima Niko; Vahid Feizi; Haydeh Ara

Volume 4, Issue 13 , March 2018, , Pages 79-97

Abstract

Abstract Introduction Drought, with its gradual, tranquil, and crawling occurrence, is one of the most important natural disasters that affects various aspects of human life. This phenomenon, as a disastrous climatic phenomenon, directly affects communities through changes in their access to water ... Read More Abstract Introduction Drought, with its gradual, tranquil, and crawling occurrence, is one of the most important natural disasters that affects various aspects of human life. This phenomenon, as a disastrous climatic phenomenon, directly affects communities through changes in their access to water resources. One of the most important effects of drought on water resources is the reduction and drop in groundwater aquifers and the decrease in river flow rates. The hydrological drought, with the effects of periods of atmospheric drops, affects the sources of groundwater or surface water supplies such as river flows, reservoirs, lakes, and groundwater. Therefore, the effect of the rainfall shortage on the components of the hydrological system such as soil moisture, river flow, surface of reservoirs, and groundwater is seen after a long time. Climatological drought with a time lag in one place leads to a hydrological drought which, consequently, leads to water stress. Determining the starting and ending dates of droughts, their severity, continuity, spatial distribution, assessment, and quantification is one of the most important issues in the study area. Accordingly, the main objective of this research was to determine the extent of the continuity of meteorological and hydrological droughts and the relationship between them. Methodology The Gorganroud River watershed forms 48% of Golestan Province with an area of 11393.1 km2. It is located in the geographical range of '36 ° 36 'to 37 ° 37' the northern latitude and '00 ° 54 'to '29 ° 56 the eastern altitude. It is located in the national scale of the Gorgan River basin in the north of the country. From the south east to the eastern Alborz, from the east to mount Aladagh and mount Glydiyah, from the north to the Atrak basin, and from the west to the Caspian Sea and the Gharasso basin. The Gorganroud River has 17 main branches that are connected in different parts and, ultimately, flood the Caspian Sea. The basin is used as a forest in the south and east, but in the north and west, alluvial plains are exploited in agriculture and pasture. In order to study droughts, the standardized precipitation index (SPI) and standardized water-level index (SWI) were used. The data used in this study was extracted from 16 meteorological stations and 31 piezometric wells, with a common statistical period of 30 years (1362-1392). To analyze the droughts' trend, seven scales of 1, 3, 6, 12, 24, and 48 were used in a monthly and annual scales. In this study, to reconstruct the statistical errors and homogenize the data, acorrelation and normal ratio methods were used. Then, the SPI and SWI indices and the quantitative analysis of droughts of basin were used to evaluate the trend of rainfall and underground water in different spatial and temporal scales. Result According to the calculations and checking the map of the annual extent of meteorological drought, the western and eastern regions of the basin were affected by drought more than other regions. In addition, according to the map of the annual extent of the groundwater droughts, the southwestern, western, and northern parts were affected by drought more than other regions. Considering the duration of the meteorological drought, the northeastern, western, and southwestern parts had longer durations than other regions. Considering the duration of the groundwater drought, the northern, southwestern, and central parts of the basin had the longest duration.

A Survey on the Quality of Drinking Water in the Populated Areas of Ardabil Plain Using a Combination of Multi Criteria Decision Making Models and Geostatistics in the GIS Environment

Hashem Rostamzadeh; Mohammad Reza Nikjoo; Ismaeil Asadi; Jafar Jafarzadeh

Volume 2, Issue 3 , January 2017, , Pages 43-60

Abstract

Ardabil Plain is an intermountain area of approximately 820 square kilometers in northwestern Iran, located in the eastern plateau of Azerbaijan within the province of Ardabil. Plain water needed for agriculture, industry and drinking are provided from rivers, deep and semi-deep wells and springs in ... Read More

Groundwater Potentiality Assessment Through Analytical Hierarchy Process (AHP), Remote Sensing and GIS in Mahdishahr Area

Hasan Fathizan; Hamid Alipoor; Seideh Negar Hasheminasab; Haji Karimi

Volume 3, Issue 8 , December 2016, , Pages 1-20

Abstract

Hasan Fathizad[1]* Hamid Alipoor[2] Seideh Negar Hasheminasab[3] Haji Karimi[4] Abstract Groundwater is considered as an important part of renewable waters of the world. With the increasing population, urbanization trend, etc., the demand for these resources, day by day is increasing. Nowadays, ... Read More Hasan Fathizad[1]* Hamid Alipoor[2] Seideh Negar Hasheminasab[3] Haji Karimi[4] Abstract Groundwater is considered as an important part of renewable waters of the world. With the increasing population, urbanization trend, etc., the demand for these resources, day by day is increasing. Nowadays, remote sensing and geographic information system (GIS) has become one of the most powerful and affordable tools for assessing and exploration of accessible groundwater resources. The purpose of this study is to identify potential areas of groundwater in the Mahdishahr area located in Semnan using Analytical Hierarchy Process (AHP), remote sensing, and GIS. The parameters which are considered to identify the areas of potential groundwater are: lithological units, lineaments, slope, topography, drainage density, vegetation, and isoheytal maps which prepared by using the 1:50000 scale topographic maps, digital elevation model, ETM+ satellite images, 1:250000 scale geological map, and precipitation data of meteorology stations by remote sensing and GIS techniques. To determine potential areas of groundwater, all layers in different classes were weighted through hierarchical analysis and after modeling in the GIS medium, Mahdishahr basin was subdivided in the groundwater potential point of view. The results showed that among the 7 examined criteria determined by the expertise and analytic hierarchy process method, the geology and lineaments have relative importance of 0.33 and 0.22 respectively as the highest priority in groundwater potential determination in this area. Quaternary alluviums including old and new terraces and alluvial deposits have the highest relative importance and desirability in the study area. Terrace storages and old elevated and recent low elevation alluvial fans are as fair potential groundwater area. [1]- Ph.D. Student in Department of management the arid and desert regions, College of Natural Resources and Desert, Yazd University, Iran; hasan.fathizad@gmail.com. [2]- Ph.D. Student in Department of management the arid and desert regions, College of Natural Resources and Desert, Yazd University, Iran. [3]- M.A.of Management the arid and desert regions. [4]- Associate professor of Pasture and Watershed, Ilam University, Iran.

Investigation of Groundwater Nitrate Pollution with Different Interpolation Methods (Case Study: East Azarbayjan, Bilverdy Plain)

Volume 1, Issue 1 , January 2015, , Pages 75-92

Abstract

Abstract Nowadays application of geostatistical interpolation methods is very important in estimating the spatial distributions in all aspects of water science. Likewise, acquiring sound knowledge on the quality and conditions of the groundwater for different usages is vital. However, the measurement ... Read More

Evaluating the Quality of Mehraban Plain Groundwater Resources Using GQI and FGQI Methods

Volume 2, Issue 2 , January 2015, , Pages 79-98

Abstract

Mehraban Plain is located in the eastern part of the East Azarbaijan province. In this plain groundwater resources are the main source of water supply for drinking. In this area the bedrock of the aquifer and its surrounding high lands are mainly formed by Neogene sediments including gypsy and ... Read More

Finding potential Ground Water Sources by Using Analytical Hierarchy Processes (AHP), Case Study: Abadeh - Eghlid basin of Fars

Volume 1, Issue 1 , January 2015, , Pages 131-144

Abstract

Abstract Ground water is one of important freshwater sources for human. Since surface water sources are limited in most regions, groundwater can be considered as one of the most available resources for supplying water. This research tries to identify regions which have the most groundwater resources ... Read More

Volume 11 (2024)

Volume 10 (2023)

Volume 9 (2022)

Volume 8 (2021)

Volume 7 (2020)

Volume 6 (2019)

Volume 5 (2018)

Volume 4 (2017)

Volume 3 (2016)

Volume 2 (2015)

Volume 1 (2015)

Keywords = Groundwater

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