摘要
为提高基于天气预报的参考作物蒸散量(ETo)预报模型精度,通过引入序数(ORD),独热(O-H),目标(TAR)和CatBoost(CAT)4种编码方法对天气类型和风力等级进行数值化处理,结合Light Gradient Boosting Decision Machine(LGB)算法构建了基于天气预报类别特征的ETo预报模型.结果表明,同时引入编码处理的天气类型和风力等级数据可以有效提升LGB3模型精度(R^(2)较LGB1提升-0.97%~9.36%),提升排名为O-H>CAT>TAR>ORD.单独引入天气类型数据的LGB4能够获得与LGB3模型相近的精度,而单独引入风力等级对LGB5模型精度贡献不显著,甚至可能会引入噪声而降低精度.因此采用O-H编码处理天气类型和风力等级数据扩展输入维度,可以提高模型精度,适用于缺少气象站或数据种类不全地区的ETo精准预测.
To improve the accuracy of reference evapotranspiration(ETo)prediction model based on public weather forecast,weather types and wind levels were numerically processed by introducing four encoding methods,namely,Ordinal(ORD),One-Hot(O-H),Target(TAR)and CatBoost(CAT)encoding.The Light Gradient Boosting Decision Machine(LGB)algorithm was combined with the above methods to build the ETo prediction model based on weather forecast category features.The results show that the accuracy of LGB3 model can be improved effectively by introducing encoded weather type and wind level data(R^(2)improved by-0.97%~9.36%compared with LGB1),and the improvement rank is O-H>CAT>TAR>ORD.LGB4 with additional encoded weather type data alone can obtain similar accuracy to LGB3 model,while introducing only wind level has no significant contribution to LGB5 model accuracy and may even introduce noise reduction accuracy.Therefore,using O-H encoding to pre-process weather type and wind level data can expand input dimension to improve the model accuracy and it can be recommended for precise ETo prediction in regions with lack of meteorological station or incomplete data type.
作者
吴天傲
李江
张薇
郭维华
缴锡云
WU Tian’ao;LI Jiang;ZHANG Wei;GUO Weihua;JIAO Xiyun(State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Hohai University,Nanjing 210098,China;College of Agricultural Science and Engineering,Hohai University,Nanjing 210098,China;Cooperative Innovation Center for Water Safety&Hydro Science,Hohai University,Nanjing 210098,China)
出处
《应用基础与工程科学学报》
EI
CSCD
北大核心
2022年第6期1402-1419,共18页
Journal of Basic Science and Engineering
基金
国家自然科学基金项目(52009030)
江苏省研究生科研创新计划(KYCX21_0537)
关键词
参考作物蒸散量
天气预报
机器学习
数据预处理
类别特征编码
reference evapotranspiration
weather forecast
machine learning
data pre-processing
categorical feature encoding
