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Air Quality Forecast Based on Principal Component Analysis-Genetic Algorithm and Back Propagation Model

Authors: Bin Mu, Site Li, Shijin Yuan


Under the circumstance of environment deterioration, people are increasingly concerned about the quality of the environment, especially air quality. As a result, it is of great value to give accurate and timely forecast of AQI (air quality index). In order to simplify influencing factors of air quality in a city, and forecast the city’s AQI tomorrow, this study used MATLAB software and adopted the method of constructing a mathematic model of PCA-GABP to provide a solution. To be specific, this study firstly made principal component analysis (PCA) of influencing factors of AQI tomorrow including aspects of weather, industry waste gas and IAQI data today. Then, we used the back propagation neural network model (BP), which is optimized by genetic algorithm (GA), to give forecast of AQI tomorrow. In order to verify validity and accuracy of PCA-GABP model’s forecast capability. The study uses two statistical indices to evaluate AQI forecast results (normalized mean square error and fractional bias). Eventually, this study reduces mean square error by optimizing individual gene structure in genetic algorithm and adjusting the parameters of back propagation model. To conclude, the performance of the model to forecast AQI is comparatively convincing and the model is expected to take positive effect in AQI forecast in the future.

Keywords: Genetic Algorithm, Principal Component Analysis, AQI forecast, back propagation neural network model

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[1] Kanchan Prasad, Amit Kumar Gorai, Pramila Goyal, Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time (J). Atmospheric Environment, 128(2016): 246-262, 2016.
[2] Camillo Silibello, Alessio D'Allura, Sandro Finardi, Application of bias adjustment techniques to improve air quality forecasts (J). Atmospheric Pollution Research, 6(6): 928–938, 2015.
[3] Yang Zhang, Chaopeng Hong, Khairunnisa Yahya, Comprehensive evaluation of multi-year real-time air quality forecasting using an online-coupled meteorology-chemistry model over southeastern United States (J). Atmospheric Environment, 138 (2016): 162–182, 2016.
[4] Zhuo Jinwu, MATLAB in the application of mathematical modeling (M). Beijing: Beihang University press, 2014.
[5] Wang Xiaochuan, Shi Feng, Yu Lei, Li Yang, MATLAB neural network 43 case analysis (M). Beijing: Beihang University press, 2013
[6] Meng Dong, fan Zhongjun, Wang Jiazhen, chaos genetic algorithm to the BP neural network improved (J). Mathematical theory and application, 34 (1): 102-110, 2014.
[7] Liu Yuanyuan, Lian Jijian, Zhu Yun, Application of improved BP neural network based on Genetic Algorithm in prediction of chaotic runoff time series(J).Hydrology, 27 (2): 45-48, 2007.
[8] Li Song, Liu Lijun, de Yongle, Chaos prediction of short time traffic flow based on genetic algorithm optimized BP neural network (J).Control and decision, 26 (10): 1581-1585, 2011.
[9] Gao Daqi, teachers of linear basis function to the three layer neural network (J). Journal of computer, 21 (1) 80-86, 1998.
[10] Park, Yang-Byung, Yoo, Jun-Su, Park, Hae-Soo, A genetic algorithm for the vendor-managed inventory routing problem with lost sales (J). Expert systems with applications, 53, 149-159, 2016.
[11] Ardjmand, Young, WA, Weckman, GR, Applying genetic algorithm to a new bi-objective stochastic model for transportation, location, and allocation of hazardous materials (J). Expert systems with applications, 51, 49-58, 2016.
[12] Contreras-Bolton, Carlos, Gatica, Gustavo, A multi-operator genetic algorithm for the generalized minimum spanning tree problem (J). Expert systems with applications, 50, 1-8, 2016.
[13] Hwang, Inyoung,, Park, Hyung-Min, Chang, Joon-Hyuk, Ensemble of deep neural networks using acoustic environment classification for statistical model-based voice activity detection (J). Computer speech and language, 38, 1-12, 2016.
[14] Barat, Cecile, Ducottet, Christophe, String representations and distances in deep Convolutional Neural Networks for image classification (J). Pattern recognition, 54, 104-115, 2016.
[15] Li, Fanjun, Qiao, Junfei, Han, Honggui, A self-organizing cascade neural network with random weights for nonlinear system modeling (J). Applied soft computing, 42, 184-193, 2016.
[16] LiuYan, Cheng Zhi-long, Xu Jing, Improvement and Validation of Genetic Programming Symbolic Regression Technique of Silva and Applications in Deriving Heat Transfer Correlations (J). Heat transfer engineering, 37(10), 862-874, 2016.
[17] Prabhu, M. Venkatesh, Karthikeyan R, Modeling and optimization by response surface methodology and neural network-genetic algorithm for decolorization of real textile dye effluent using Pleurotus ostreatus: a comparison study (J). Desalination and water treatment, 57(28), 13005-13019, 2016.
[18] Khoshbin, Fatemeh, Bonakdari, Hossein, Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs (J) Engineering optimization, 48(6), 933-948.