| Title | Prediction method of environmental pollution in smart city based on neural network technology |
|---|---|
| ID_Doc | 38045 |
| Authors | Jiang, XJ; Zhang, P; Huang, JC |
| Title | Prediction method of environmental pollution in smart city based on neural network technology |
| Year | 2022 |
| Published | |
| Abstract | The expansion of urban population makes urban development face huge challenges, and the use of emerging technologies to solve urban problems has become the theme of modern urban development. The development of new technologies has accelerated the process of urban development, and smart cities have emerged under these conditions. One of the problems to be solved by smart cities is environmental pollution. The development of industrialized cities has caused a series of environmental problems such as smog and sewage. Therefore, pollution problems must be actively managed. The prediction of environmental pollution is also an important subject in pollution control. This paper is based on neural network technology to study the prediction method of environmental pollution in smart cities. This paper introduces some neural networks commonly used in the field of environmental pollution prediction, and also introduces the processing method of environmental pollution data. This paper also designs experiments. The first experiment is to compare the model in this paper with other three neural network models, and it is found that the model in this paper is smaller than other models in terms of ARE and MAE; the second experiment is to design an environmental pollution prediction system based on the model in this paper, and take sulfur dioxide as an example, use the system to predict the value of sulfur dioxide in two provinces. The results are as follows: the average error value of the system's prediction for Jiangxi Province is 0.31%, and the average error value for Hubei Province is 0.34%. In combination, the neural network designed in this paper compares the pre-accuracy of environmental pollution than other neural networks, and the system predictive accuracy is high. |
Similar Articles
| ID | Score | Article |
|---|---|---|
42312
|
Honarvar, AR; Sami, A Towards Sustainable Smart City by Particulate Matter Prediction Using Urban Big Data, Excluding Expensive Air Pollution Infrastructures(2019) | |
| 42305
|
Kataria, A; Puri, V AI- and IoT-based hybrid model for air quality prediction in a smart city with network assistance(2022)Iet Networks, 11, 6 | |
| 38552
|
Neo, EX; Hasikin, K; Lai, KW; Mokhtar, MI; Azizan, MM; Hizaddin, HF; Razak, SA; Yanto Artificial intelligence-assisted air quality monitoring for smart city management(2023) | |
| 42689
|
Mo, XY; Zhang, L; Li, H; Qu, ZX A Novel Air Quality Early-Warning System Based on Artificial Intelligence(2019)International Journal Of Environmental Research And Public Health, 16, 19 | |
| 41062
|
Mu, B; Li, ST; Yuan, SJ An Improved Effective Approach for Urban Air Quality Forecast(2017) | |
| 42301
|
Zhao, H Artificial intelligence-based public safety data resource management in smart cities(2023)Open Computer Science, 13, 1 | |
| 39581
|
Batool, T; Abbas, S; Alhwaiti, Y; Saleem, M; Ahmad, M; Asif, M; Elmitwally, NS Intelligent Model Of Ecosystem For Smart Cities Using Artificial Neural Networks(2021)Intelligent Automation And Soft Computing, 30, 2 | |
| 41377
|
Binbusayyis, A; Khan, MA; Ahmed, AMM; Emmanuel, WRS A deep learning approach for prediction of air quality index in smart city(2024)Discover Sustainability, 5, 1 |