Akram Farshadirad; Alireza Hosseinpour; Shojae ghorbani; hamidreza motaghian
Abstract
Introduction: In recent years, because of the presence of industrial factories around the Isfahan province of Iran and high concentrations of heavy metals in the vicinity of them, and the gradual accumulation of heavy metals from various sources of pollution in urban areas over time, including gasoline ...
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Introduction: In recent years, because of the presence of industrial factories around the Isfahan province of Iran and high concentrations of heavy metals in the vicinity of them, and the gradual accumulation of heavy metals from various sources of pollution in urban areas over time, including gasoline combustion, and use of urban waste compost and sewage sludge as fertilizer, there has been widespread concerned regarding the human health problems with increasing heavy metals in soils around the Isfahan city. The variation of composition in the soil matrix may lead to variation of composition and behavior of soil heavy metals. Soil is a heterogeneous body of materials and soil components are obviously in interaction. Studies tacking this complexity often use aggregate measurements as surrogates of the complex soil matrix. So, it is important the understanding soil particle-size distribution of aggregates and its effects on heavy metal partitioning among the size fractions, the fate of metals and their toxicity potential in the soil environment. Therefore, the present study aimed to determine the Cu release potential from different size fractions of different polluted soils by different extractants and their availability for corn plant.
Materials and Methods: Five soil samples were collected from the surface soils (0–15 cm) of Isfahan province, in central of Iran. The soil samples were air-dried and ground to pass a 2-mm sieve for laboratory analysis. Air dried samples fractionated into four different aggregate size fractions 2.0–4.0 (large macro-aggregate), 0.25–2 (small macro-aggregate), 0.05–0.25 (micro-aggregate), and
Sh. Ghorbani; M. Homaee; M.H. Mahdian
Abstract
Abstract
Infiltration is a significant process which controls the fate of water in the hydrologic cycle. The direct measurement of infiltration is time consuming, expensive and often impractical because of the large spatial and temporal variability. Artificial Neural Networks (ANNs) are used as an ...
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Abstract
Infiltration is a significant process which controls the fate of water in the hydrologic cycle. The direct measurement of infiltration is time consuming, expensive and often impractical because of the large spatial and temporal variability. Artificial Neural Networks (ANNs) are used as an indirect method to predict the hydrological processes. The objective of this study was to develop and verify some ANNs to predict the infiltration process. For this purpose, 123 double ring infiltration data were collected from different sites of Iran. The parameters of some infiltration models were then obtained; using sum squares error optimization method. Basic soil properties of the two upper pedogenic layers such as initial water content, bulk density, particle-size distributions, organic carbon, gravel content, CaCO3 percent and soil water contents at field capacity and permanent wilting point were obtained for each sampling point. The feedforward multilayer perceptron was used for predicting the infiltration parameters. Two ANNs types were developed to estimate infiltration parameters. The developed ANNs were categorized into two groups; type 1 and type 2 ANNs. For developing type 1 ANNs, the basic soil properties of the first upper soil horizon were used as inputs, hierarchically. While for developing type 2 ANNs the basic soil properties of the two upper soil horizons were used as inputs, using principal component analysis technique. Evaluation results of these two types ANNs showed the better performance of type 1 ANNs in predicting the infiltration parameters. Therefore, this type of ANNs was used for predicting the cumulative infiltration. The reliability test indicated that the developed ANNs for Philip model have the best performance to predict cumulative infiltration with a mean RMSE of 6.644 cm. The developed ANNs for Horton, Kostiakov-Lewis and Kostiakov have the next best ranks, respectively.
Keywords: Multilayer Perceptron, Artificial Neural Networks, Infiltration Models, Soil Infiltration
