Atallah Nadiri; Fariba Sadeghi Aghdam; Asghar Aghari Moghaddam; Keivan Naderi
Volume 2, Issue 4 , January 2017, , Pages 79-99
Abstract
Ata Allah Nadiri[1]* Asghar Asgharai Moghaddam[2] Fariba Sadeghi Aghdam[3] Keivan Naderi[4] Abstract Water demand management and water supply for different usages associated with identification, control and reduction of water pollution for improving water quality and environmental indices are the main ...
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Ata Allah Nadiri[1]* Asghar Asgharai Moghaddam[2] Fariba Sadeghi Aghdam[3] Keivan Naderi[4] Abstract Water demand management and water supply for different usages associated with identification, control and reduction of water pollution for improving water quality and environmental indices are the main purposes of water resources management in the country. Sahand Dam reservoir is the main source of water to supply drinking, industrial and agricultural demands. Therefore, the illness reported from residents due to high concentration of arsenic (200 times more than the permissible limit of drinking water) in drinking water, show the importance of the study of water quality in the area. To determine the quality of water resources for different consumptions, 50 water samples were collected from the water resources of the area and analysed in the hydrological lab of the Geolology Department of Tabriz University. In addition, data collected by the researchers sector of East Azerbaijan Regional Water Authority for studying the Sahand Dam also were used. In this research, Factor analyzing and time series variations methods for determing factor effecting on hydrogeochemistry of the area were used. Quality monitoring and hydrochemical factors affecting water resources of Sahand Dam identify two main factors affecting hydrochemistry of groundwater of the study area which are arsenic and salinity anomalies. In this regard the main anomalies, origins and areas affected by these anomalies were recognized and the distribution maps of these factors in the study area were prepared. [1]- Ph.D of Hydrogeology, Assistant Professor, Faculty of Natural Science, University of Tabriz (Corresponding author), Email:nadiri@tabrizu.ac.ir. [2]- Ph.D of Hydrogeology, Professor, Faculty of Natural Science, University of Tabriz. [3]- M.Sc of Hydrogeology, Department of natural science, University of Tabriz. [4]- M.Sc of Hydrogeology, Department of natural science, University of Tabriz.
Ata Allah Nadiri; Fatemeh Vahedi; Asghr Asghari Moghaddam
Volume 3, Issue 6 , January 2017, , Pages 115-134
Abstract
Ata Allah Nadiri[1]* Fatemeh Vahedi[2] Asghar Asghari Moghaddam[3] Abstract Groundwater is the main supply of drinking and agriculture demands in Meshginshahr plain located on Northwest of Iran in the Province of Ardebil. The investigation of groundwater level fluctuations is necessary for effective ...
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Ata Allah Nadiri[1]* Fatemeh Vahedi[2] Asghar Asghari Moghaddam[3] Abstract Groundwater is the main supply of drinking and agriculture demands in Meshginshahr plain located on Northwest of Iran in the Province of Ardebil. The investigation of groundwater level fluctuations is necessary for effective groundwater management in this plain. For this purpose, artificial intelligence methods are interested due to high ability, cost effectiveness, needing less data, and fast running. This study presents a supervised committee fuzzy logic (SCFL) model to predict groundwater level at three piezometers in the study area. For implementing SCFL model, first fuzzy logic models such as Mamdani fuzzy logic (MFL), Larsen fuzzy logic (LSL) and Sugeno fuzzy logic (SFL) were applied to predict groundwater level using precipitation, temperature, discharge of abstraction wells and groundwater level with one month lag data. Then a supervised committee fuzzy logic as a non-linear model was used to combine the outputs of individual fuzzy models to reap the advantages of all three models simultaneously. Three different criteria RMSE, MAE and R2 were used to assess the prediction efficiency and accuracy of models. Based on results, MAE values of SCFL model are 0.12, 0.04 and 0.03 for piezometer 1, 2, and 3 respectively for training step. It presents the superiority of SCFL model over the individual fuzzy models. Also SCFL model could reduce prediction RMSE to 6% for piezometer 1 and 8%, 14% for piezometers number 2 and 3 respectively. [1]- Assistant Professor of Hydrogeology, University of Tabriz, Tabriz, Iran (corresponding author), Email:nadiri@tabrizu.ac.ir [2]- Master student of Hydrogeology, University of Tabriz, Tabriz, Iran. [3]- Professor of Hydrogeology, University of Tabriz, Tabriz, Iran.
Asghar Asgari Moghaddam; Ataollah Nadiri; Vahid Pakniya
Volume 3, Issue 8 , December 2016, , Pages 21-52
Abstract
Received: 2015.06.08 Accepted: 2016.10.29 Asghar Asghari Moghaddam[1]* Ataollah Nadiri[2] Vahid Pakniya[3] Abstract Bostan Abad plain is located in East Azerbaijan province, North West of Iran. Groundwater resources of the plain supply significant portion of the drinking and agricultural water demands ...
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Received: 2015.06.08 Accepted: 2016.10.29 Asghar Asghari Moghaddam[1]* Ataollah Nadiri[2] Vahid Pakniya[3] Abstract Bostan Abad plain is located in East Azerbaijan province, North West of Iran. Groundwater resources of the plain supply significant portion of the drinking and agricultural water demands of the area, as a result, protection of these resources from contamination is an important task. Therefore, for assessing of the aquifer vulnerability, DRASTIC and SINTACS models were used in GIS software setting. The plain vulnerability maps for each model, according to data layers including depth of water table, net recharge rate, aquifer media, soil media, topography, VA-dose zone media and hydraulic conductivity were prepared. The final map of aquifer vulnerability with five zone of vulnerability from very low to high is produced. DRASTIC and SINTACS index were calculated from 61 to188 and 92 to 202 respectively. The sensitivity analysis was determined by a single parameter that the vadose zone media has the most significant impact on the vulnerability index. The distribution of nitrate ions concentrations were used for the models verification. The adaptation nitrate layer and zoning map of vulnerability for both models showed that the areas with high concentration of nitrates are coincided with high potential vulnerability areas. The correlation coefficient of 0.75 between DRASTIC model and nitrate layer were obtained. For preparing the contamination risk map of groundwater, the land use layer was overlapped to DRASTIC vulnerability map. The results of overlapping maps showed that 31.33 percent of the total area of land used for agriculture is high potential vulnerable area. According to the final maps of vulnerability for both models the central and northwestern parts of the plain contains the highest contamination potential in the area. [1]- Professor, Dept. of Earth Science, University of Tabriz (Corresponding Autor), Email:moghaddam@tabrizu.ac.ir. [2]- Assistant Prof, Dept. of Earth Science, University of Tabriz. [3]- M.Sc student, Dept. of Earth Science, University of Tabriz.
