The early-warning system based on hybrid optimization algorithm and fuzzy synthetic evaluation model

Abstract Portending and mastering the atmospheric quality plays a significant role in polishing up people's daily life and preventing the environment against serious pollution because air pollution has exceeded the international warning level. Since, accurate contaminants concentration forecasting and objective evaluation, which is urgent to establish an early-warning system, is a critical issue for the social community. An early-warning framework based on two modules composed of forecasting part and assessment part is proposed and successfully adopted in this paper. The forecasting part is applied to a hybrid optimization model with improved Harmony Search Algorithm with PSO strategy to forecast air contaminants concentration, while the assessment part is applied to Fuzzy Synthetic Evaluation Model with entropy weight to evaluate the air quality levels. The proposed early-warning system was investigated in three cities of Jing-Jin-Ji region of China where there exists serious air pollution for the period August 1st 2015 to September 29st 2016. The findings showed that the forecasting model is greatly superior to statistical model and other models on the urban pollutant concentration data. According to the results of air quality assessment, the evaluation model based on the entropy technique can objectively assess the atmospheric quality level.

[1]  Pei Wang,et al.  An almost-parameter-free harmony search algorithm for groundwater pollution source identification. , 2013, Water science and technology : a journal of the International Association on Water Pollution Research.

[2]  Mohammad Hjouj Btoush,et al.  PM 10 Forecasting Using Soft Computing Techniques , 2014 .

[3]  Yunzhen Xu,et al.  Air quality early-warning system for cities in China , 2017 .

[4]  Hu-Chen Liu,et al.  A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method , 2015, Appl. Soft Comput..

[5]  Yang Liu,et al.  A self-adaptive harmony PSO search algorithm and its performance analysis , 2015, Expert Syst. Appl..

[6]  Viktor Pocajt,et al.  Forecasting human exposure to PM10 at the national level using an artificial neural network approach , 2013 .

[7]  A. Osses,et al.  Forecasting urban PM10 and PM2.5 pollution episodes in very stable nocturnal conditions and complex terrain using WRF–Chem CO tracer model , 2011 .

[8]  Sabit Cakmak,et al.  The modifying effect of socioeconomic status on the relationship between traffic, air pollution and respiratory health in elementary schoolchildren. , 2016, Journal of environmental management.

[9]  Shadi Ausati,et al.  Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM 2.5 , 2016 .

[10]  Lan Feng,et al.  Assessing coastal reclamation suitability based on a fuzzy-AHP comprehensive evaluation framework: A case study of Lianyungang, China. , 2014, Marine pollution bulletin.

[11]  Baolong Han,et al.  Urban ecological security assessment for cities in the Beijing–Tianjin–Hebei metropolitan region based on fuzzy and entropy methods , 2015 .

[12]  Ping Jiang,et al.  Two combined forecasting models based on singular spectrum analysis and intelligent optimized algorithm for short-term wind speed , 2016, Neural Computing and Applications.

[13]  Yan Dong,et al.  An improved harmony search based energy-efficient routing algorithm for wireless sensor networks , 2016, Appl. Soft Comput..

[14]  Selen Cakmakyapan,et al.  A new approach to the prediction of PM10 concentrations in Central Anatolia Region, Turkey , 2015 .

[15]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[16]  Xiaodong Li,et al.  Artificial Neural Network Models for Daily PM10 Air Pollution Index Prediction in the Urban Area of Wuhan, China , 2011 .

[17]  L. A. Cox,et al.  Socioeconomic and air pollution correlates of adult asthma, heart attack, and stroke risks in the United States, 2010–2013 , 2017, Environmental research.

[18]  Xiu Li Gao,et al.  Research on Motor Vehicle Exhaust Pollution Monitoring Technology , 2014 .

[19]  Karine Léger,et al.  Comparing urban air quality in Europe in real time a review of existing air quality indices and the proposal of a common alternative. , 2008, Environment international.

[20]  Ajith Kaduwela,et al.  Seasonal modeling of PM2.5 in California's San Joaquin Valley , 2014 .

[21]  Jeng-Fung Chen,et al.  Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach , 2015, Appl. Soft Comput..

[22]  Qi Li,et al.  Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation , 2015 .

[23]  Marko Nagode,et al.  The material characterization of the air spring bellow sealing layer , 2009 .

[24]  Anastasia K Paschalidou,et al.  Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management , 2011, Environmental science and pollution research international.

[25]  Hing Kai Chan,et al.  Recent Development in Big Data Analytics for Business Operations and Risk Management , 2017, IEEE Transactions on Cybernetics.

[26]  E. Handakas,et al.  Monitoring of air pollution levels related to Charilaos Trikoupis Bridge. , 2017, The Science of the total environment.

[27]  K. Zhou,et al.  Prediction of rock burst classification using cloud model with entropy weight , 2016 .

[28]  Valery M. Nakariakov,et al.  Empirical mode decomposition analysis of random processes in the solar atmosphere , 2016 .

[29]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[30]  Dezhi Sun,et al.  Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification , 2011 .

[31]  Xiaobo Zhang,et al.  Developing an early-warning system for air quality prediction and assessment of cities in China , 2017, Expert Syst. Appl..

[32]  C. L. Philip Chen,et al.  Data-intensive applications, challenges, techniques and technologies: A survey on Big Data , 2014, Inf. Sci..