S. Yusefzadeh; A. A. Nadiri
Abstract
Today Ground water is the main source of drinking, agriculture and other uses for humans. The demand for this critical and strategic natural resource increased with population growth and development of society. This increasing has been declining water resources and damage aquifers environment. Therefore, ...
Read More
Today Ground water is the main source of drinking, agriculture and other uses for humans. The demand for this critical and strategic natural resource increased with population growth and development of society. This increasing has been declining water resources and damage aquifers environment. Therefore, we need to manage aquifers and understanding the hydrogeological parameters to deal with the water crisis and prevent the distraction of aquifers. The one of most important parameter is hydraulic conductivity. Although, the ground water system is a complex system and estimation of hydrogeological parameters is associated with inherent uncertainty and also is costly and time consuming that usually done with classical methods such as laboratory tests, slug test, tracing test and pumping tests. So recently use artificial intelligence methods for estimation of hydraulic conductivity, reduced the uncertainty of this parameter and it adds up some accuracy. So that it can overcome on the shortcoming of classical methods. In this study, four artificial intelligence methods; mamdani fuzzy logic(MFL) system, sugeno fuzzy logic(SFL) system, Wavelet-neural network method and Least square support vector machine(LS-SVM) method were used as individual models to estimate the hydraulic conductivity by using of surface geophysical data in Maragheh-Bonab aquifer. Given that each these models based on their inherent properties, they presented good results in some parts of area. Therefore, for concurrent use of performance of all these models the nonlinear combination method as a supervised committee machine artificial intelligence (SCMAI) model were used to estimate the hydraulic conductivity in maragheh-bonab aquifer. The result of this model showed that this new combinational model has high performance than other single models that presented by using different evaluation criteria. Therefore, this model could also be used for estimation hydrogeological parameters in areas with high complexity. The SCMAI model was tested against 15 data. The RMSE and for SCMAI prediction were computed as 0.045 and 0.97, respectively. Comparing the error measure values with dose of individual models above, it is seen that SCMAI outperforms individual AI models with low RMSE and high values. This result implies that SCMAI model shows high performance for estimation the hydraulic conductivity values in the heterogeneous unconfined aquifer in Maragheh-Boanb plain.
M. Gharekhani; A. A. Nadiri; A. Asghari Moghaddam
Abstract
Due to the infiltration of contaminants from surface to underground water systems, groundwater pollution is one of the serious problems, especially in arid and semi-arid areas that encounter with lack of quality and quantity of water resources. Therefore, groundwater vulnerability evaluation is necessary ...
Read More
Due to the infiltration of contaminants from surface to underground water systems, groundwater pollution is one of the serious problems, especially in arid and semi-arid areas that encounter with lack of quality and quantity of water resources. Therefore, groundwater vulnerability evaluation is necessary to manage the groundwater resources by identifying areas with high potential of contamination. In this study, groundwater vulnerability in Ardabil plain aquifer was evaluated by applying DRASTIC model. DRASTIC model was prepared by seven effective parameters on vulnerability, including groundwater depth, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity. These parameters were prepared as seven raster layers, and DRASTIC index was then calculated after ranking and weighting. The DRASTIC index value was obtained between 82 to 151 for the Ardabil plain. The main problem of this model is the subjectivity in determining rates and weights of the parameters. Therefore, the purpose of this study is to improve DRASTIC model using 5 methods of artificial intelligence (AI), such as Feedforward network (FFN), Recurrent neural network (RNN), Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Committee machine (CM) to obtain the most accurate results of vulnerability evaluation. Because of heterogeneity in the Ardabil Plain, it is divided into 3 sections including west, east and north, and each section needs an individual model. For this purpose, the DRASTIC parameters and the vulnerability index were defined as inputs data and output data respectively for models, and nitrate concentration data were divided into two categories for training and test steps. The output of model in training step was corrected by the related nitrate concentration, and after model training, the output of model in test step was verified by the nitrate concentration. The results show that all of the artificial intelligence methods are able to improve the DRASTIC model, but the supervised committee machine artificial intelligence (SCMAI) model had the best results. According to this model, the most of high pollution potential areas located in western and northern parts of the plain, and need more protection.
A.A Nadiri; F sadeghi Aghdam; A Asgharai Moghaddam
Abstract
This study presents an intelligence committee fuzzy logic (ICFL) model to estimate the concentration of total arsenic (III, V) in the area of the Sahand Dam basin, Iran. Because of a high concentration of arsenic (III, V) compared to WHO standard, Geolology Department of Tabriz University and East Azerbaijan ...
Read More
This study presents an intelligence committee fuzzy logic (ICFL) model to estimate the concentration of total arsenic (III, V) in the area of the Sahand Dam basin, Iran. Because of a high concentration of arsenic (III, V) compared to WHO standard, Geolology Department of Tabriz University and East Azerbaijan Regional Water Authority have attempted to sampling and analysis of surface water and groundwater resources in the mentioned area. Hydrochemical parameters Including pH, SO42-, NO3--N, F-, Fe(II, III) and As used as input parameters for Mamdani fuzzy logic (MFL), Larsen fuzzy logic (LSL) and Sugeno fuzzy logic (SFL) to estimate arsenic concentrations. The results in train and test steps showed that all of these models have a similar fitting to the arsenic concentration data in the study area. The ICFL model was adopted to combine the output of the three single fuzzy models instead of the selecting superior single model. To reap advantage of all three models, the weighted combination of the output of fuzzy models used to create a committee fuzzy model. The mentioned model uses particles population algorithm, to obtain weight based on the output of the three fuzzy models to estimate the total arsenic concentration. The results of ICFL model shows, significant fitting improvement compare to individual fuzzy logic models.
SH Safari; A Asghari Moghaddam; A Nadiri; K Siahcheshm
Abstract
Arsenic is one of the most toxic and dangerous soluble substances in natural water. It has long-term ill effects on human health. Arsenic-contaminated water resources have been reported from many parts of the world and Iran, particularly from the Kurdistan province in the west of the country. The aim ...
Read More
Arsenic is one of the most toxic and dangerous soluble substances in natural water. It has long-term ill effects on human health. Arsenic-contaminated water resources have been reported from many parts of the world and Iran, particularly from the Kurdistan province in the west of the country. The aim of this study is to identify the source of arsenic and mechanisms of its release into groundwater resources of the Chahardoli plain aquifers. Groundwater resources in this plain supply much of the water needs for drinking, agriculture and industry. Therefore, 31 water samples were collected from the plain aquifer and chemically analyzed for major and minor ions in the Hydrology Laboratory of Earth Sciences Department of the Tabriz University. Also, the trace elements were analyzed in the Kurdistan Waste Water Organization Laboratory. The results show high arsenic concentrations in the groundwater of the area. The highest arsenic concentration (270 µg/L) is related to a well located in the northwest part of the area which supplies water for agricultural purposes of Delbaran sector. According to the results obtained from multi-variable and graphical methods, there is a meaningful correlation between arsenic and major ions such as Na and K as well as silica, indicating that the source of arsenic is from volcanic rocks. It is therefore a geogenic rather than an anthropogenic phenomenon. The mechanism of arsenic releases into the water can be related to competitive adsorption of dissolved SiO2 in adsorption sites such as oxides of iron, aluminium and manganese.
R Barzegar; A Asghari Moghaddam; A Nadiri; E Fijani
Abstract
With respect to the concentration of population, agricultural activities and industrial manufactures in Tabriz Plain area, vulnerability assessment of the plain aquifer is very useful for development, management, decision making for land use and preventing groundwater contamination. In this research, ...
Read More
With respect to the concentration of population, agricultural activities and industrial manufactures in Tabriz Plain area, vulnerability assessment of the plain aquifer is very useful for development, management, decision making for land use and preventing groundwater contamination. In this research, vulnerability of the Tabriz plain complex aquifer to contamination was considered by DRASTIC model in GIS media and different fuzzy logic methods adapted to optimize the model. The DRASTIC model uses seven environmental parameters effective on assessment of aquifers vulnerability such as Depth to groundwater level, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity, which created as seven layers in ArcGIS media and weighted, rated and compiled, finally groundwater contamination vulnerability map was prepared and the DRASTIC index for the study area calculated between 40 to 126. The vulnerability map created by DRASTIC is compared to nitrate data and the results indicated a relative correlation between the nitrate level and vulnerability index. The Sugeno, Mamdani and Larsen fuzzy logic methods were adapted for optimizing the weights of the DRASTIC parameters. For this purpose, the DRASTIC parameters as input data and the vulnerability index as output data were defined for fuzzy models and nitrate concentration data were divided in two categories for training and test steps. The output of model in training step was corrected by related nitrate concentration, and after model training, the output of model in test step was verified by nitrate concentration. The results obtained from different fuzzy models show that the Sugeno fuzzy logic model is an applicable and useful method for optimizing DRASTIC model. According to the final model results, the eastern part of the area, inside the Tabriz city limits, has the maximum potential rate for contamination.
