Bera Biswajit, Bhattacharjee Sumana, Sengupta Nairita, Saha Soumik
Department of Geography, Sidho-Kanho-Birsha University, Ranchi Road, P.O. Purulia Sainik School, 723104, India.
Department of Geography, Jogesh Chandra Chaudhuri College (University of Calcutta), 30, Prince Anwar Shah Road, Kolkata 700 033, India.
Environ Chall (Amst). 2021 Aug;4:100155. doi: 10.1016/j.envc.2021.100155. Epub 2021 May 24.
Kolkata is the third densely populated city of India and Kolkata stands in the World's 25 most polluted cities along with 10 worse polluted cities in India. The relevant study claims that due to the imposition of lockdown during COVID-19 pandemic, the atmospheric pollution level has been significantly reduced over the metropolitan city Kolkata like other cities of the world. The main objective of this study is to predict the concentration of PM using multiple linear regression (MLR) and artificial neural network (ANN) models and similarly, to compare the accuracy level of two models. The concentration of PM data has been obtained from state pollution control board, Govt. of West Bengal and daily meteorological data have been collected from the world weather website. The results show that non-linear artificial neural network model is more rational compared with multiple linear regression model due to its high precision and accuracy level (in respect to RMSE, MAE and R). In this research artificial neural network (ANN) model exhibited higher accuracy during the training and testing phases (root mean square error (RMSE), mean absolute error (MAE) and R indicate 3.74, 1.14 and 0.91 respectively in training phase and 2.55, 4.32 and 0.69 in testing phase respectively). This model (ANN)) can be applied to predict the concentration of PM during the execution of urban air quality management plan.
加尔各答是印度人口第三密集的城市，并且加尔各答跻身于世界25个污染最严重的城市之列，同时也是印度污染最严重的10个城市之一。相关研究表明，由于在新冠疫情期间实施了封锁措施，与世界其他城市一样，加尔各答大都市的大气污染水平已大幅降低。本研究的主要目的是使用多元线性回归（MLR）和人工神经网络（ANN）模型预测PM浓度，同样地，比较这两种模型的准确度。PM浓度数据取自西孟加拉邦政府的国家污染控制委员会，每日气象数据则从世界天气网站收集。结果表明，非线性人工神经网络模型由于其高精度和准确度水平（相对于均方根误差（RMSE）、平均绝对误差（MAE）和R），与多元线性回归模型相比更为合理。在本研究中，人工神经网络（ANN）模型在训练和测试阶段表现出更高的准确度（训练阶段的均方根误差（RMSE）、平均绝对误差（MAE）和R分别为3.74、1.14和0.91，测试阶段分别为2.55、4.32和0.69）。该模型（ANN）可应用于在城市空气质量管理计划执行期间预测PM浓度。
