A coupling model of genetic algorithm and RBF neural network for the prediction of PM2.5 concentration
LIANG Ze1, WANG Yue-yao1, YUE Yuan-wen2, WEI Fei-li1, JIANG Hong3, LI Shuang-cheng1
1. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China;
2. School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China;
3. School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
We developed a coupling model combining the radial basis function (RBF) artificial neural network and the genetic algorithm to predict the average PM2.5 concentrations in Beijing in the next 24hours. This model mainly used air pollutant concentration data obtained by air quality monitoring stations as inputs, and relied on the genetic algorithm to determine parameters such as the number of hidden layer neurons and the spread constant. The model had a good prediction performance (R-square up to 0.75) with less data inputs because it does not need meteorological or geographical information for its training process. Further improvements can be made by using multi-source data and increasing sample size in the training process to enhance the accuracy and robustness of the model for the prediction of air pollution in different situations.
梁泽, 王玥瑶, 岳远紊, 韦飞黎, 姜虹, 李双成. 耦合遗传算法与RBF神经网络的PM2.5浓度预测模型[J]. 中国环境科学, 2020, 40(2): 523-529.
LIANG Ze, WANG Yue-yao, YUE Yuan-wen, WEI Fei-li, JIANG Hong, LI Shuang-cheng. A coupling model of genetic algorithm and RBF neural network for the prediction of PM2.5 concentration. CHINA ENVIRONMENTAL SCIENCECE, 2020, 40(2): 523-529.
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