J. Dowlati; Gh. Lashkaripour; N. Hafezi Moghadas
Abstract
Zahedan aquifer is located in the northernof Zahedanwatedshed. It is essential to evaluate the quality of groundwater resources due to proving some part of drinking water, agricultural and industrial waters of this city. In order to carry out ground water quality monitoring, and assess the controlling ...
Read More
Zahedan aquifer is located in the northernof Zahedanwatedshed. It is essential to evaluate the quality of groundwater resources due to proving some part of drinking water, agricultural and industrial waters of this city. In order to carry out ground water quality monitoring, and assess the controlling possesses and determine cations and anions sources of the groundwater, 26 wells were sampled and water quality parameters were measured.The results of the analysis showed that almost all of the samples proved very saline and electrical conductivity varied from 1,359 to 12,620μS cm−1. In the Zahedan aquifer, sodium, chloride and sulfate were predominant Cation and Anions respectively, and sodium-chloride Na-Cl( and sodium - sulfate)Na-So4) were dominant types of the groundwater. The factor analysis of samples results indicates that the two natural and human factors controlled about the 83/30% and 74/37% of the quality variations of the groundwater respectively in October and February. The first and major factor related to the natural processes of ion exchange and dissolution had a correlation with positive loadings of EC, Ca2+, Mg2+, Na+, Cl-, K+ and So42- and controls the 65.25% of the quality variations of the ground water in October and the 58.82% in February. The second factor related toCa2+, No3- constituted the18.05% of the quality variations in October and 15.56% in February, and given the urban development and less agricultural development in the aquifer, is dependent on human activities. For the samples collected in October, the saturation indices of calcite, gypsum and dolomite minerals showed saturated condition and calcite and dolomite in February showed saturated condition for more than 60% and 90% of samples and gypsum index revealed under-saturated condition for almost all samples.The unsaturated condition of Zahedan groundwater aquifer is resulted from the insufficient time for retaining water in the aquifer to dissolve the minerals. So42- and No3- Ions in more than 70 percent samples showed unnatural sources (the sewer infiltration).
H. Ghorbani; A. Roohani; N. Hafezi Moghaddas
Abstract
In this research, a learning vector quantization neural network (LVQ) model was developed to predict and classify the spatial distribution of cadmium in soil in Golestan province. The cadmium data were obtained from soils measuring total Cd contents in soil samples. Some statistical tests, such as means ...
Read More
In this research, a learning vector quantization neural network (LVQ) model was developed to predict and classify the spatial distribution of cadmium in soil in Golestan province. The cadmium data were obtained from soils measuring total Cd contents in soil samples. Some statistical tests, such as means comparision, variance and statistical distribution were performed between the observed points samples data and the estimated cadmium values to evaluate the performance of the pattern recognition method. The Results showed that in training and test phase, there were no significant differences, with the confidence level of 95%, between the statsitcal parameters such as average, variance, statistical distribution and also coefficient of determination in the observed and the estimated cadmium concentrations. The results suggest that learning vector quantization (LVQ) neural network can learn cadmium cocentration model precisely. In addition the results also indicated that trained LVQ neural network had a high capability in predicting cadmium concentrations for non-sampled points. The technique showed that the LVQNN could predict and map the spatial cadmium concentrations variability. Our results indicated that it is possible to discriminate different cadmium levels in soil, using LVQNN.