A Simple Dendritic Neural Network Model-Based Approach for Daily PM2.5 Concentration Prediction

被引:15
|
作者
Song, Zhenyu [1 ]
Tang, Cheng [2 ]
Ji, Junkai [3 ]
Todo, Yuki [4 ]
Tang, Zheng [2 ]
机构
[1] Taizhou Univ, Coll Comp Sci & Technol, Taizhou 225300, Peoples R China
[2] Univ Toyama, Fac Engn, Toyama 9308555, Japan
[3] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
[4] Kanazawa Univ, Sch Elect & Comp Engn, Kanazawa, Ishikawa 9201192, Japan
基金
美国国家科学基金会;
关键词
air quality forecasting; neural network; environment; PM2.5; concentration; PRINCIPAL COMPONENT ANALYSIS; FINE PARTICULATE MATTER; PM10; OPTIMIZATION; COMPUTATION; ALGORITHM; FORECAST; MACHINE; MASS;
D O I
10.3390/electronics10040373
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Air pollution in cities has a massive impact on human health, and an increase in fine particulate matter (PM2.5) concentrations is the main reason for air pollution. Due to the chaotic and intrinsic complexities of PM2.5 concentration time series, it is difficult to utilize traditional approaches to extract useful information from these data. Therefore, a neural model with a dendritic mechanism trained via the states of matter search algorithm (SDNN) is employed to conduct daily PM2.5 concentration forecasting. Primarily, the time delay and embedding dimensions are calculated via the mutual information-based method and false nearest neighbours approach to train the data, respectively. Then, the phase space reconstruction is performed to map the PM2.5 concentration time series into a high-dimensional space based on the obtained time delay and embedding dimensions. Finally, the SDNN is employed to forecast the PM2.5 concentration. The effectiveness of this approach is verified through extensive experimental evaluations, which collect six real-world datasets from recent years. To the best of our knowledge, this study is the first attempt to utilize a dendritic neural model to perform real-world air quality forecasting. The extensive experimental results demonstrate that the SDNN offers very competitive performance relative to the latest prediction techniques.
引用
收藏
页码:1 / 21
页数:23
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