Academic Journal of Environment & Earth Science, 2024, 6(4); doi: 10.25236/AJEE.2024.060407.
Huijun Yu
School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China
This paper evaluated the weight of influencing factors on the development of urban environmental quality by studying the relationship between existing environmental pollution and its influencing factors. The prediction models of the combination of water, atmosphere, and solid waste were constructed, based on the non-biased grey model (GM (1,1)), Back Propagation neural network (BPNN), Autoregressive Integrated Moving Average Model (ARIMA), and machine learning algorithms such as gradient descent were used to improve the fitting accuracy. The data from 2001 to 2020 were selected as the training set and the validation set to predict the environmental pollution trend from 2021 to 2028. Research shows that the BPNN has good performance of the three emissions in three models.
ARIMA; GM (1,1); BPNN; urban environmental quality
Huijun Yu. Prediction of urban environmental pollution based on the optimal prediction model. Academic Journal of Environment & Earth Science (2024), Vol. 6, Issue 4: 46-52. https://doi.org/10.25236/AJEE.2024.060407.
[1] FAN H, ZHAO C, YANG Y. A comprehensive analysis of the spatio-temporal variation of urban air pollution in China during 2014–2018 [J]. Atmospheric Environment, 2020, 220: 117066.
[2] KUERBAN M, WAILI Y, FAN F, et al. Spatio-temporal patterns of air pollution in China from 2015 to 2018 and implications for health risks [J]. Environmental Pollution, 2020, 258: 113659.
[3] YUAN C, LIU S, FANG Z J E. Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM (1, 1) model [J]. 2016, 100: 384-90.
[4] CHEN J, LIU Z, YIN Z, et al. Predict the effect of meteorological factors on haze using BP neural network [J]. Urban Climate, 2023, 51: 101630.
[5] CEYLAN Z, BULKAN S, ELEVLI S. Prediction of medical waste generation using SVR, GM (1,1) and ARIMA models: a case study for megacity Istanbul [J]. Journal of Environmental Health Science and Engineering, 2020, 18(2): 687-97.
[6] Reddy K , Krishna K .Vehicular pollution prediction using HWTO‐ANFIS model in urban areas of Hyderabad City[J].Environmental Progress & Sustainable Energy, 2023, 42.DOI:10.1002/ep.14082.
[7] ZENG B, DUAN H, ZHOU Y. A new multivariable grey prediction model with structure compatibility [J]. Applied Mathematical Modelling, 2019, 75: 385-97.
[8] Zhang X , Zhao M , Dong R .Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network[J].Applied Sciences, 2020, 10(3):1144.DOI: 10.3390/ app10031144.
[9] ZHANG G P. Time series forecasting using a hybrid ARIMA and neural network model [J]. Neurocomputing, 2003, 50: 159-75.
[10] Yang Y , Zhang C T , Zhang R .BME prediction of continuous geographical properties using auxiliary variables[J].Stochastic Environmental Research & Risk Assessment, 2016, 30(1):9-26.DOI:10.1007/s00477-014-1005-1.
[11] SUN W, HUANG C. A carbon price prediction model based on secondary decomposition algorithm and optimized back propagation neural network [J]. Journal of Cleaner Production, 2020, 243: 118671.
[12] LU X, ZHANG S, XING J, et al. Progress of Air Pollution Control in China and Its Challenges and Opportunities in the Ecological Civilization Era [J]. Engineering, 2020, 6(12): 1423-31.