基于机器学习的雷达回波与降雨分析
Relationship Analysis of Radar Echo and Rainfall Based on Machine Learning
DOI: 10.12677/SEA.2021.101006, PDF,  被引量   
作者: 黄建伟, 段 勇, 杨 堃, 于 霞：沈阳工业大学信息科学与工程学院，辽宁 沈阳
关键词: XGBoost；集成学习；降雨分析；机器学习；雷达回波；XGBoost； Ensemble Learning； Rainfall Analysis； Radar Echo
摘要: 多普勒天气雷达产生的雷达回波数据是降雨分析及预测的重要依据，针对如何有效利用雷达回波进行降雨等级分析问题，本文研究了一种基于XGBoost集成学习算法的雷达回波与降雨关系分析模型。本文使用辽宁省气象台提供的历年雷达和降雨气象观测数据，经过数据解码、清洗、匹配后，使用经网格搜索算法优化后的XGBoost方法训练，建立多层雷达回波数据与降雨等级的分类关系。最后通过实验结果表明，基于XGBoost方法得到的结果更接近实际，能够较好地反映云团雷达回波和降雨的关系。
Abstract: Radar echo data generated by Doppler weather radar is an important basis for rainfall analysis and prediction. Aiming at the problem of how to make effective use of radar echo for rainfall grade analysis, this paper studies an analysis model of the relationship between radar echo and rainfall based on XGBoost ensemble learning algorithm. In this paper, we use the radar and rainfall mete-orological observation data provided by Liaoning Meteorological Station over the years. After data decoding, cleaning and matching, we use XGBoost method optimized by grid search algorithm to establish the classification relationship between multi-layer radar echo data and rainfall level. Fi-nally, the experimental results show that the results based on XGBoost method are closer to reality and can better reflect the relationship between cloud radar echo and rainfall.
文章引用：黄建伟, 段勇, 杨堃, 于霞. 基于机器学习的雷达回波与降雨分析[J]. 软件工程与应用, 2021, 10(1): 35-43. https://doi.org/10.12677/SEA.2021.101006

参考文献

[1] 王慧媛. 基于深度学习的短时定量降水预测研究[D]: [学位论文]. 金华: 浙江师范大学, 2020.
[2] Suzana, R. and Wardah, T. (2011) Radar Hydrology: New Z/R Relationships for Klang River Basin, Malaysia. Proceedings of 2011 International Conference on Environment Science and Engineering, Bali Island, 1-3 April 2011.
[3] 汪瑛, 冯业荣, 蔡锦辉, 胡胜. 雷达定量降水动态分级Z-I关系估算方法[J]. 热带气象学报, 2011, 27(4): 601-608.
http://dx.chinadoi.cn/10.3969/j.issn.1004-4965.2011.04.018
[4] Brandes, E.A. (1975) Optimizing Rainfall Es-timates with the Aid of Radar. Journal of Applied Meteorology and Climatology, 14, 1339-1345. [Google Scholar] [CrossRef] 
[5] Sakaino, H. (2013) Spa-tio-Temporal Image Pattern Prediction Method Based on a Physical Model with Time-Varying Optical Flow. IEEE Transactions on Geoscience and Remote Sensing, 51, 3023-3036. [Google Scholar] [CrossRef] 
[6] Lee, S., Cho, S. and Wong, M. (1998) Rainfall Prediction Us-ing Artificial Neural Networks. Journal of Geographic Information and Decision Analysis, 2, 233-242.
[7] Kufigowski, R.J. and Barros, A.P. (1998) Localized Precipitation Forecasts from a Numerical Weather Prediction Model Using Artificial Neural Networks. Weather and Forecasting, 13, 1194-1204. [Google Scholar] [CrossRef] 
[8] Luk, K.C., Ball, J.E. and Sharma, A. (2001) An Application of Artificial Neural Networks for Rainfall Forecasting. Mathematical and Computer Modelling, 33, 683-693. [Google Scholar] [CrossRef] 
[9] Chau, K.W. and Wu, C.L. (2010) A Hybrid Model Coupled with Singular Spectrum Analysis for Daily Rainfall Prediction. Journal of Hydroinformatics, 12, 458-473. [Google Scholar] [CrossRef] 
[10] 时玮域. 基于机器学习方法的雾天气预测研究[D]: [硕士学位论文]. 沈阳: 沈阳工业大学, 2020.
[11] 王训师. XGBoost机器学习模型在缺血性卒中后早期认知损害诊断中的应用研究[D]: [博士学位论文]. 杭州: 浙江大学, 2018.
[12] 王晓晖, 张亮, 李俊清, 孙玉翠, 田捷, 韩睿毅. 基于遗传算法与随机森林的XGBoost改进方法研究[J]. 计算机科学, 2020, 47(Z2): 454-458+463.
[13] Zhang, X.M., Yan, C., Gao, C., Malin Bradley, A. and Chen, Y. (2020) Predicting Missing Values in Medical Data via XGBoost Regression. Journal of Healthcare Informatics Research, 4, 383-394. [Google Scholar] [CrossRef] [PubMed]
[14] 陈逸伦. 基于多源卫星数据的云团和雨团识别及其特征研究[D]: [博士学位论文]. 合肥: 中国科学技术大学, 2019.
[15] Kato, S., Rose, F.G., Rutan, D.A., Thorsen, T.J., Loeb, N.G., Doelling, D.R., et al. (2018) Surface Irradiances of Edition 4.0 Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Data Product. Journal of Climate, 31, 4501-4527. [Google Scholar] [CrossRef] 
[16] Guo, J.Q., Dai, Y.Z., Wang, C.X., Wu, H., Xu, T.Y. and Lin, K. (2020) A Physiological Data-Driven Model for Learners’ Cognitive Load Detection using HRV-PRV Feature Fusion and Optimized XGBoost Classification. Software: Practice and Experience, 50, 2046-2064. [Google Scholar] [CrossRef] 

为你推荐