Smart City Gnosys
Smart city article details
| Title | Machine Learning-Based Intelligent Weather Modification Forecast In Smart City Potential Area |
|---|---|
| ID_Doc | 36053 |
| Authors | Chao Z. |
| Year | 2023 |
| Published | Computer Science and Information Systems, 20, 2 |
| DOI | http://dx.doi.org/10.2298/CSIS220717018C |
| Abstract | It is necessary to improve the efficiency of meteorological service monitoring in smart cities and refine the prediction of extreme weather in smart cities continuously. Firstly, this paper discusses the weather prediction model of artificial influence under Machine Learning (ML) technology and the weather prediction model under the Decision Tree (DT) algorithm. Through ML technology, meteorological observation systems and meteorological data management platforms are developed. The DT algorithm receives and displays the real meteorological signals of extreme weather. Secondly, Artificial Intelligence (AI) technology stores and manages the data generated in the meteorological detection system. Finally, the lightning monitoring system is used to monitor the meteorological conditions of Shaanxi Province from September to December 2021. In addition, the different meteorological intelligent forecast performance of the intelligent forecast meteorological model is verified and analyzed through the national meteorological forecast results from 2018 to 2019. The results suggest that the ML algorithm can couple bad weather variation with the existing mesoscale regional prediction methods to improve the weather forecast accuracy; the AI system can analyze the laws of cloud layer variation along with the existing data and enhance the operational efficiency of urban weather modification. By comparison, the proposed model outperforms the traditional one by 35.26%, and the maximum, minimum, and average prediction errors are 5.95%, 0.59%, and 3.76%, respectively. This exploration has a specific practical value for improving smart city weather modification operation efficiency. © 2023, ComSIS Consortium. All rights reserved. |
| Author Keywords | Artificial intelligence; Decision tree; Intelligent forecast; Machine learning; Weather modification operation |
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