Department of Computer Engineering, Corlu Engineering Faculty, Tekirdag Namık Kemal University, 59860, Çorlu, Tekirdag, Turkey.
Engineering Faculty, Department of Environmental Engineering, Istanbul University-Cerrahpasa, 34320, Avcilar, Istanbul, Turkey.
Environ Monit Assess. 2021 Apr 21;193(5):287. doi: 10.1007/s10661-021-09091-1.
Nowadays, pollutants continue to be released into the atmosphere in increasing amounts with each passing day. Some of them may turn into more harmful forms by accumulating in different layers of the atmosphere at different times and can be transported to other regions with atmospheric events. Particulate matter (PM) is one of the most important air pollutants in the atmosphere, and it can be released into the atmosphere by natural and anthropogenic processes or can be formed in the atmosphere as a result of chemical reactions. In this study, it was aimed to predict PM and PM components measured in an industrial zone selected by adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), classification and regression trees (CART), random forest (RF), k-nearest neighbor (KNN), and extreme learning machine (ELM) methods. To this end, in the first stage of the study, the dataset consisting of air pollutants and meteorological data was created, the temporal and qualitative evaluation of these data was performed, and the PM (PM and PM) components were modeled using the "R" software environment by artificial intelligence methods. The ANFIS model was more successful in predicting the PM (R = 0.95, RMSE = 5.87, MAE = 4.75) and PM (R = 0.97, RMSE = 3.05, MAE = 2.18) values in comparison with other methods. As a result of the study, it was clearly observed that the ANFIS model could be used in the prediction of air pollutants.
如今，污染物的排放量与日俱增，持续不断地排放到大气中。其中一些污染物可能会在不同的时间和不同的大气层中积累，转变成更具危害性的形式，并通过大气事件被输送到其他地区。颗粒物 (PM) 是大气中最重要的空气污染物之一，它可以通过自然和人为过程释放到大气中，也可以在大气中由于化学反应而形成。在这项研究中，旨在使用自适应神经模糊推理系统 (ANFIS)、支持向量回归 (SVR)、分类回归树 (CART)、随机森林 (RF)、k-最近邻 (KNN) 和极限学习机 (ELM) 方法预测在选定的工业区中测量的 PM 和 PM 成分。为此，在研究的第一阶段，创建了由空气污染物和气象数据组成的数据集，对这些数据进行了时间和定性评估，并使用人工智能方法通过“R”软件环境对 PM（PM 和 PM）成分进行建模。与其他方法相比，ANFIS 模型在预测 PM（R=0.95，RMSE=5.87，MAE=4.75）和 PM（R=0.97，RMSE=3.05，MAE=2.18）值方面更为成功。通过这项研究，可以清楚地观察到 ANFIS 模型可用于空气污染物的预测。
