Samira Rahnama; Hossein Khozeymehnezhad; Abbas KhasheiSiuki
Abstract
Background and Aim:Due to the increasing demands of the human population to groundwater, protection and prevention of these water resources from pollution are necessary. The purpose of this study was to evaluate the vulnerability of groundwater aquifer in Kuchesfahan- Astane plain located in Gilan province ...
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Background and Aim:Due to the increasing demands of the human population to groundwater, protection and prevention of these water resources from pollution are necessary. The purpose of this study was to evaluate the vulnerability of groundwater aquifer in Kuchesfahan- Astane plain located in Gilan province using DRASTIC method and nonparametric models. Materials and Methods:In this study, seven layers were prepared for parameters in GIS software, and after weighting and combining standard ranks, the groundwater vulnerability maps for the study area were prepared. Nitrate data were used to validate the model in this region. Subsequently, by using the nonparametric models, Instance-Based Learning with parameter K (IBK) and the Tree Decision M5, the amount of nitrate was estimated. Meanwhile, Gamma test was conducted to find the best combination of input parameters. ResultsThe results revealed that the vulnerability of groundwater aquifer in this plain has 4 classes including 18.56 % in low vulnerability, 51.29 % in low to medium vulnerability, 28.46% in medium to high vulnerability, and 1.67% in high vulnerability classes. Also, the results showed that both of the nonparametric models have suitable estimates of the nitrate content, but the M5 decision tree model yielded the best results (R2=0.98). Conclusion:The results showed that nonparametric models are efficient method to estimate the aquifer vulnerability and provide accurate results to estimate the potential of contamination in the study area.This demonstrates the superiority of the M5 model over other aquatic vulnerability assessment methods.
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.