|
|
|
| Research on multisource temperature data fusion and accurate prediction model based on deep learning |
| Yue Hao |
| Huangmei County Meteorological Bureau,Huanggang 435500 |
|
|
|
|
Abstract Aiming at the limitations of temperature data from a single meteorological station,a deep fusion prediction system for multisource temperature data was constructed.A hybrid architecture combining Convolutional Neural Network (CNN) and Long ShortTerm Memory (LSTM) was adopted to collaboratively process multidimensional temperature information such as ground observation,satellite remote sensing,and numerical prediction.The attention mechanism was used to dynamically allocate the weights of different data sources to improve fusion accuracy,and a temperature change rate adaptive loss function was designed to optimize the prediction during periods of drastic temperature changes.Experimental results show that the prediction accuracy of the model during periods of rapid temperature changes is significantly superior to traditional methods,with the mean absolute error (MAE) ≤ 0.8℃.In addition,the model can adaptively identify and correct abnormal data to ensure the reliability of output results,providing a new technical path for accurate temperature prediction.
|
|
Received: 30 December 2025
|
|
|
|
|
|
| [1] |
沈天行. 基于人工智能的气温与风速偏差订正研究[D].南京:南京信息工程大学, 2025.
|
| [2] |
朱君,李奇临,赵美艳,等. 多源融合实况分析产品在重庆地区的适用性分析[J]. 气象水文海洋仪器, 2023, 40 (3):3841.
|
| [3] |
刘钊,张源源,曹雪芬,等. 基于多源观测数据对山东半岛南部及近海地区三维雷电探测效率评估[J]. 气象水文海洋仪器, 2022, 39 (4):6063,69.
|
| [4] |
陈岚. 基于多源数据融合的气象数据真实性检验研究与应用[D].成都:成都信息工程大学, 2023.
|
| [5] |
吕晓娟,赵奕安. 多源融合实况气温数据适用性评估分析[J]. 智慧农业导刊, 2022, 2 (4):1821.
|
| [6] |
江芸. 基于多源异构数据的深度学习气温估算[D].武汉:武汉大学, 2020.
|
|
|
|
2026, Vol. 43 
