Jeyran Askari; Aslan Egdernezhad
Abstract
Background and Aim: Groundwater is one of the most important water resources on earth, and groundwater level and groundwater salinity studies are very important to protect and plan the water resources, especially in the arid and semiarid areas, such as Iran. Groundwater quantitative and qualitative testing ...
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Background and Aim: Groundwater is one of the most important water resources on earth, and groundwater level and groundwater salinity studies are very important to protect and plan the water resources, especially in the arid and semiarid areas, such as Iran. Groundwater quantitative and qualitative testing is time-consuming and costly. Therefore, using the models to simulate the quantity and quality of groundwater has become common.Materials and Methods: In recent decades, the artificial intelligence models were tested for the simulation of aquifers in terms of the complex and nonlinear properties of groundwater systems. The present study stimulated the groundwater level and groundwater salinity parameters of Dezful-Andimeshk plain using ANN and ANN + GA models, and finally compared their results with measured data. The data collected for input to two models include meteorological data and groundwater quality parameters gathered from 2011 to 2018.Results: The results showed that the optimal model is to simulate ANN + GA (Artificial Neural Network + Genetic Algorithm) groundwater level with sigmoid tangent stimulus function, and the optimal model is to simulate ANN + GA groundwater salinity with sigmoid logarithm stimulus function. MAE and RMSE statistics have the minimum and has maximum value for the model (In test phase, for the groundwater level RMSE=7.47, MAE=9.5 and R2=0.979 and for the groundwater salinity RMSE=6.81, MAE=7.74, and R2=0.99).Conclusion: Therefore, optimizing the artificial neural network model using a genetic algorithm is very useful, effective and reduces errors and saves time and money.
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.
Ali Reza Karimiyan; Aslan Egdernezhad
Abstract
Abstract Background and Aim: Because of their high effectiveness and fewer expenses than other methods, groundwater models have been developed and used by hydrogeologists as water resource management tools. In this regard, many models have been developed, which propose better management to protect ...
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Abstract Background and Aim: Because of their high effectiveness and fewer expenses than other methods, groundwater models have been developed and used by hydrogeologists as water resource management tools. In this regard, many models have been developed, which propose better management to protect water resources. Most of these models require input parameters that are hardly available or their measurements are time-consuming and expensive. Among them, Artificial Neural Network (ANN) models inspired by the human brain are a better choice. Materials and Methods: The present study simulated the groundwater level and salinity in Ramhormoz plain using ANN and ANN+PSO models and compared their results with the measured data. The data collected as inputs of the two models included minimum temperature, maximum temperature, average temperature, wind speed at 2 m altitude, minimum relative humidity, maximum relative humidity, average relative humidity, and sunshine hours gathered from 2011 to 2017. Results: The results indicated that the highest prediction accuracy of groundwater level and salinity was achieved by the ANN-PSO model with the logarithm sigmoid activation function. Thus, the MAE and RMSE statistics had the minimum and R^2 had the maximum value for the model. Conclusion: Considering the high efficiency of artificial neural network models with Particle Swarm Optimization algorithm training, it can be used to make managerial decisions, ensure the results of monitoring, and reduce costs. Keywords: Groundwater Level; Simulation; Groundwater Salinity; Artificial Neural Networks Model
Alireza Ehsanzadeh; Farhad Nejadkoorki; Ali Taleb
Abstract
Background and objective: Air pollution in Tehran, because of high concentration of pollutants, has caused various diseases and many problems concerning the public health and welfare of citizens and also damages to the environment and living organisms. Materials & Methods: Air Quality Index ...
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Background and objective: Air pollution in Tehran, because of high concentration of pollutants, has caused various diseases and many problems concerning the public health and welfare of citizens and also damages to the environment and living organisms. Materials & Methods: Air Quality Index (AQI) is a key tool to monitor the air quality, to realize the effects of air pollution on health and to choose methods against air pollution. This study aimed at modeling and estimation AQI by CART algorithm and adaptive boosting algorithm (AdaBoost). Hourly data on concentration of air pollutants and meteorological parameters related to Gholhak stations in Tehran was used for modeling and estimation of AQI. Results: The results showed that CART model had better performance than AdaBoost model. To evaluate these models, root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE) and correlation coefficient (R) of the CART model for the test, were respectively, 0.75, 0.101, 0.563, and 0.99 when compared to the AdaBoost model (RMSE=7.1, MAE=5.11, MSE=50.52 and R=0.95) which implies the absolute superiority of the CART model than the AdaBoost model. Conclusion:The results of this study showed that regression decision tree model can be used as an efficient model for modelling and estimation of urban air quality index.