Air pollution was predicted in this study by using multiple linear regression and 42 different artificial neural network models in Iğdır/Turkey. Daily air quality data for the years 2016–2018 have been used in the modeling. In the prediction of the particulate matter which has 10 μm or less in diameter (PM₁₀) concentration, sulfur dioxide, nitrogen oxides, nitrogen monoxide, ozone, nitrogen dioxide, relative humidity, air pressure, wind direction and wind speed data were used as input parameters. In the artificial neural network structures, two different learning functions, three different transfer functions and seven different neuron numbers were examined in the MATLAB software. According to results, multiple linear regression did not predict the PM₁₀ concentration in the atmosphere. The R² value was determined as 0.543 for the multiple linear regression. In this model, the RMSE, MAE and R² were determined as 0.0488, 0.0248 and 0.9826, respectively. Since the R² value in this model was quite high, it was concluded that the model is suitable for the prediction of PM₁₀ concentration.

中文翻译：

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模拟火山地质区域特性和微气候条件下的空气污染水平

在这项研究中，通过使用多元线性回归和42个不同的人工神经网络模型在伊德尔/土耳其预测了空气污染。模型中使用了2016-2018年的每日空气质量数据。在预测直径（PM ₁₀）浓度为10μm或更小的颗粒物时，使用二氧化硫，氮氧化物，一氧化氮，臭氧，二氧化氮，相对湿度，空气压力，风向和风速数据输入参数。在人工神经网络结构中，在MATLAB软件中检查了两个不同的学习函数，三个不同的传递函数和七个不同的神经元数。根据结果​​，多元线性回归无法预测PM ₁₀大气中的浓度。多元线性回归的R ²值确定为0.543。在此模型中，RMSE，MAE和R ²分别确定为0.0488、0.0248和0.9826。由于该模型中的R ²值很高，因此可以得出结论，该模型适合预测PM ₁₀浓度。