Volume 25, Issue 79 (12-2025)
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Bosak A, Hejazizadeh Z, Heydari Tashekaboud A. (2025). Modeling and Prediction of PM₁₀ Concentrations in Ahvaz Using a Hybrid Statistical and Deep Learning Approach. jgs. 25(79),
URL: http://jgs.khu.ac.ir/article-1-4357-en.html
URL: http://jgs.khu.ac.ir/article-1-4357-en.html
1- Department of Natural Geography, Faculty of Geographical Sciences, Khwarazmi University, Tehran, Iran., bosak.a.69@gmail.com , bosak.a.69@gmail.com
2- Department of Natural Geography, Faculty of Geographical Sciences, Khwarazmi University, Tehran, Iran., hedjazizadeh@yahoo.com
3- Institute of Artificial Intelligence, Shaoxing University, Shaoxing, China & Department of Geography & Urban Planning; Ferdowsi University of Mashhad,, Heydariakbar@gmail.com
2- Department of Natural Geography, Faculty of Geographical Sciences, Khwarazmi University, Tehran, Iran., hedjazizadeh@yahoo.com
3- Institute of Artificial Intelligence, Shaoxing University, Shaoxing, China & Department of Geography & Urban Planning; Ferdowsi University of Mashhad,, Heydariakbar@gmail.com
Abstract: (3177 Views)
Air pollution has significant impacts on human health, environmental quality, and the sustainable development of cities. This study aimed to evaluate PM10 using meteorological data from the city of Ahvaz through statistical methods and artificial neural networks. Daily meteorological data and air quality control station data for 4485 days (from 2011 to 2023) were obtained from the National Meteorological Organization and the Khuzestan Department of Environment. Initially, the data were processed and refined, and their normality was assessed using the Kolmogorov-Smirnov test. Given the non-normality of the data, Spearman's and Kendall's Tau-b methods were employed to examine their correlations. The time series and statistical information of the data were obtained using Python programming language. Furthermore, to predict future PM10 levels, the Multilayer Perceptron (MLP) neural network method was utilized. The results of these analyses indicated a significant correlation between meteorological variables and PM10. The Spearman and Kendall Tau-b correlations showed that PM10 had a positive and significant correlation with wind speed (0.094 and 0.061) and temperature (0.284 and 0.187) at a 99% confidence level. Conversely, PM10 exhibited a negative and significant correlation with visibility (-0.408 and -0.300), wind direction (-0.048 and -0.034), precipitation (-0.159 and -0.125), and relative humidity (-0.259 and -0.173) at the 99% confidence level. For future PM10 predictions, the MLP neural network was used. The model was of the Sequential type with an input layer consisting of 6 neurons, three hidden layers of Dense type with 16, 32, and 64 neurons, and an output layer with a linear activation function. The mean squared error (MSE) for the training set was 0.0034, and for the validation data, it was 0.0012. For the test set, the obtained validation accuracy was mse_mlp=0.0048 and val_loss=0.0012. The results indicate a significant direct or inverse correlation between meteorological data and PM10. Additionally, the outcomes of the MLP neural network demonstrated that the network provided satisfactory performance and acceptable predictions for PM10 data in Ahvaz.
Keywords: Air Pollution, Time Series, Neural Network, Multilayer Perceptron (MLP), PM10, Regression.
Type of Study: Research |
Subject:
climatology
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