天气温度预测模型数据

可以用于温度预测,输入相对湿度、大气压亏缺、风速、温度、光合光子通量密度、浅层土壤含水量、深层土壤含水量、地外辐射、短波入射辐射输出为温度。该模型帮助解决了温度和相关环境指标以及贝壳杉液流的关系建模的问题。将采集的数据使用传统算法，多元线性回归算法等方式以预测用于温度。该模型通过输入相对湿度、大气压亏缺、风速、贝壳杉液流、光合光子通量密度、浅层土壤含水量、深层土壤含水量、地外辐射、短波入射辐射，来输出预测的温度。

This dataset can be utilized for temperature prediction. It takes relative humidity, atmospheric pressure deficit, wind speed, air temperature, photosynthetic photon flux density, shallow soil moisture content, deep soil moisture content, extraterrestrial radiation, and shortwave incident radiation as input variables, and outputs temperature as the target variable.
This model addresses the challenge of modeling the associations between temperature, relevant environmental indicators, and kauri sap flow.
Collected data are processed using conventional algorithms including multiple linear regression for temperature prediction tasks.
This model takes relative humidity, atmospheric pressure deficit, wind speed, kauri sap flow, photosynthetic photon flux density, shallow soil moisture content, deep soil moisture content, extraterrestrial radiation, and shortwave incident radiation as inputs to produce predicted temperature values.