Examination and prediction of fog and haze pollution using a Multi-variable Grey Model based on interval number sequences |
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| Institution: | 1. College of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, PR China;2. China Institute of Manufacturing Development, Nanjing University of Information Science and Technology, Nanjing 210044, PR China;1. School of Science, Southwest University of Science and Technology, Mianyang, 621010, China;2. Visual Computing and Virtual Reality Key Laboratory of Sichuan Province, Sichuan Normal University, Chengdu, 610068, China;3. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, 610500, China;4. College of Business Planning, Chongqing Technology and Business University, Chongqing, 400067, China;5. School of Mathematics Science, Sichuan Normal University, Chengdu, 610068, China;6. School of Science, Southwest Petroleum University, Chengdu, 610500, China;7. School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester M1 5GD England, United Kingdom;1. College of Sciences, Northeastern University, Shenyang, 110819, China;2. School of Economics and Business Administration, Central China Normal University Wuhan 430079, China;3. Institute of EduInfo Science and Engineering, Nanjing Normal University, Nanjing 210097, China;1. School of Sciences, Southwest Petroleum University, Chengdu, Sichuan 610500, China;2. School of Science, Southwest University of Science and Technology, Mianyang, Sichuan 621010, China;3. Research Institute of Exploration and Development of Xinjiang Oilfield Company, PetroChina, Karamay, Xinjiang 834000, China;1. School of Mathematics and Computing Science, Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, No. 1, Jinji Road, Guilin, Guangxi Province 541004, China;2. School of Mathematical Science, University of Electronic Science and Technology of China, Chengdu 611731, China;3. School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China;4. School of Sciences, Southwest Petroleum University, Chengdu 610500, China |
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| Abstract: | In this study, a new Multivariable Grey Model (1,m) aimed at interval grey number sequences with known possibility functions is built using the kernel and degree of greyness under new definitions. Based on the new model, formulae are deduced to calculate and predict the upper and lower bounds of interval grey numbers. Since the grey system model and fog- and haze-prone weather have the same characteristics of uncertainty, this model was applied to simulate and predict the measurable indicators of fog and haze in Nanjing, China. We selected visibility data and particulate matter data with a diameter of 2.5 µm to build a new Multivariable Grey Model (1,2) with a new kernel and degree of greyness sequence. In addition, we established the traditional Multivariable Grey Model (1,2) with the original kernel and degree of greyness and the Auto-Regressive Integrated Moving Average Model (1,1,0). The results show that the new Multivariable Grey Model (1,2) has the best simulation and prediction effects among the three models, with average relative errors of simulation and prediction at 1.32% and 0.32%, respectively. To further verify the validity and feasibility of the proposed model, we added another real-world example to establish the three models mentioned above. The results prove that the proposed model has evidently superior performance to another two models. |
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