基于改进沙猫群优化算法优化CatBoost模型的气温和风速偏差订正  

Improved SCSO-Optimized CatBoost for Bias Correctionof Temperature and Wind Speed

作　　者：沈天行 秦华旺 SHEN Tian-xing;QIN Hua-wang(Electronics and Information Engineering College,Nanjing University of Information Science and Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学电子与信息工程学院,南京210044

出　　处：《科学技术与工程》2024年第34期14716-14725,共10页Science Technology and Engineering

摘　　要：当前环境下,气象要素的准确预报在农业生产,社会生活和交通运输方面起到了越来越重要的作用,因此提出了一种改进的沙猫群算法(sand cat swarm optimization, SCSO),用于优化CatBoost模型,以解决传统气温和风速预测不准确的问题。研究数据涵盖了南京地区2012年1月1日—2014年12月31日的气象数据,利用ERA5再分析数据作为真实数据。首先,将数据划分为训练集和验证集,利用SCSO优化CatBoost模型,以订正24、48、72 h刻预报的气温和风速。为了克服SCSO易陷入局部最优解和收敛速度慢的问题,采用Halton Sequence搜索算法初始化沙猫群位置,并引入莱维飞行和三角游走策略优化寻优过程。在迭代中,采用LOBL策略和边界突变算子确保不会陷入局部最优解。最后,利用改进的SCSO优化CatBoost的超参数,并结合K折交叉验证提高参数的可靠性和泛化性。结果表明,改进的SCSO-CatBoost模型相比XGBoost、LightGBM、传统GBDT、随机森林、支持向量机和线性回归模型具有更高的准确性和优越性,在24 h的气温和风速预测中均方根误差分别提升了0.514 5和0.174 9,在48、72 h的提升也十分显著。为提升气象要素预报准确性提供了科学依据和技术支持。In the current context,accurate prediction of meteorological elements plays an increasingly important role in agricultural production,social life,and transportation.Therefore,an improved SCSO(sand cat swarm optimization)algorithm gets proposed to optimize the CatBoost model,addressing the issue of inaccurate traditional temperature and wind speed predictions.Meteorological data from the Nanjing area covering the period from January 1,2012,to December 31,2014,was utilized,with ERA5 reanalysis data serving as ground truth.The data was divided into training and validation sets.The SCSO optimizes the CatBoost model for correcting temperature and wind speed forecasts at 24,48,and 72-hour intervals.To overcome issues of SCSO easily falling into local optima and slow convergence,the Halton Sequence search algorithm initializes the sand cat swarm positions.Levy flight and triangle walk strategies are introduced to optimize the search process.During iterations,the LOBL strategy and boundary mutation operator were employed to ensure avoidance of local optima.Results indicate that the improved SCSO-CatBoost model exhibits higher accuracy and superiority compared to XGBoost,LightGBM,traditional GBDT,Random Forest,Support Vector Machine,and Linear Regression models.Root mean square errors for temperature and wind speed predictions at 24 hours stand at 0.5145 and 0.1749,respectively.Significant improvements occur at 48 and 72 hours as well.This study provides a scientific basis and technical support for enhancing the accuracy of meteorological element forecasts.

关 键 词：CatBoost 沙猫群优化算法 神经网络 PYTHON 气象预测 偏差订正 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]