Seyed Ali Mohammadi Nezhad; Aslan Egder Nezhad
Abstract
The present study stimulated the groundwater quality parameters of Zeidoun plain including Sodium Adsorption Ratio (SAR), Electrical Conductivity (EC), Total Dissolved Solids (TDS), using ANN and ANN-GA models and in the end compare their results with measured data. The input data for TDS quality parameter ...
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The present study stimulated the groundwater quality parameters of Zeidoun plain including Sodium Adsorption Ratio (SAR), Electrical Conductivity (EC), Total Dissolved Solids (TDS), using ANN and ANN-GA models and in the end compare their results with measured data. The input data for TDS quality parameter consist of Na, EC, Ca, Mg, SO4 and SAR, for SAR including the Na, TDS, Hco3, Ca and Mg and quality parameter of EC contains Ca, Mg, SO4, Na and SAR, gathered from 2011 to 2018.The results showed that in ANN and ANN-GA models, the highest accuracy of SAR simulation in the model with sigmoid tangent function, in EC simulator model, the highest accuracy in ANN and ANN-GA models, respectively, related to logarithm stimulus functions. Sigmoid and tangent is sigmoid. Also in ANN and ANN-GA models, the highest accuracy of TDS simulation was obtained in the model with sigmoid tangent stimulus and sigmoid logarithm, respectively. so that the MAE and RMSE statistics have the minimum and R^2 has the maximum value for the model. In general, according to the obtained results, the accuracy of ANN-GA model is higher than ANN model, to simulate the groundwater quality parameters of Zeidoun plain. Therefore, the use of artificial neural network model along with genetic algorithm is a good tool to simulate high quality groundwater quality parameters, without the need for measurement and laboratory work, which requires high time and cost.
Seyed Reza Mousavian; Aliakbar Haghdoost; Razieh Tavakoli
Abstract
Abstract
Background and Aim: Air pollution is one of the most significant environmental problems that has a remarkable impact on the incidence of cardiovascular disease and associated mortality. It is essential to comprehend air pollution effects and the ways of emission and predict the number of patients ...
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Abstract
Background and Aim: Air pollution is one of the most significant environmental problems that has a remarkable impact on the incidence of cardiovascular disease and associated mortality. It is essential to comprehend air pollution effects and the ways of emission and predict the number of patients with acute respiratory problems to eliminate and reduce air pollutants and associated mortality. This study aimed to investigate the relationship between different air pollutants and the number of cardiovascular disease patients in Mashhad.
Materials and Methods: This study applied a neural network to model and analyze the relationship between CO, NO2, SO2, PM2.5, and PM10 and the number of patients with acute respiratory problems. The inputs were average temperature, humidity, wind direction, and wind speed and the output was the number of people referred per day by gender and age. The data set used included meteorological data from the Iran Meteorological Organization, air pollution data from the Mashhad Meteorological Organization, and the number of daily referrals of heart disease patients to the emergency department of Mashhad.
Results: According to this study, the most effective air pollutants in Mashhad were PM2.5 and PM10, followed by NO2, CO, and SO2, respectively.
Conclusion: Neural networks can be applied in the modeling of the relationship between environmental parameters and cardiovascular disease patients because they have a high ability to model nonlinear phenomena. These models show that the more airborne particles, the more rate of cardiovascular diseases in Mashhad
Seyed Saeed Keykhosravi; Farhad Nejadkoorki; Mahmood Amintoosi
Abstract
Background and Objective: Dust modeling can be considered as an appropriate tool for predicting future industrial dust and identifying pollutant emission control strategies. Perceptron (MLP) and radial base (RBF) neural networks were used as a means for predicting the outflow dust from the main cogeneration ...
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Background and Objective: Dust modeling can be considered as an appropriate tool for predicting future industrial dust and identifying pollutant emission control strategies. Perceptron (MLP) and radial base (RBF) neural networks were used as a means for predicting the outflow dust from the main cogeneration of Sabzevar cement factory located in Khorasan Razavi Province. Method: the concentration of dust from the main cement chimney in the study area was measured through field measurements. Then, the parameters of the production line (temperature, speed of gas output, voltage, fuel, raw materials, and time of sampling) were used as input data to the nerve networks to predict the concentration of dust. The values obtained from the implementation of the models were compared with the results of field measurements as a superior model selection. Results: The analysis of figures and statistical parameters showed that the mean squared errors for the two MLP and RBF models were as much as 1.787 and 21.263, respectively, and the correlation coefficients were as much as 0.99693 and 0.95811, respectively, which indicates a lower error and greater correlation between the MLP and RBF model in predicting the concentration of dust. Conclusion: Because of the high ability of perceptron nervous networks to predict dust concentration, this model can be a convenient and fast solution to predict the amount of dust in the industry.