DC 欄位 值 語言 DC.contributor 企業管理學系 zh_TW DC.creator 施又升 zh_TW DC.creator You-Sheng Shih en_US dc.date.accessioned 2022-6-30T07:39:07Z dc.date.available 2022-6-30T07:39:07Z dc.date.issued 2022 dc.identifier.uri http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=109421052 dc.contributor.department 企業管理學系 zh_TW DC.description 國立中央大學 zh_TW DC.description National Central University en_US dc.description.abstract 美國的玉米是全球產量最大的作物，故美國的玉米產量足以牽動整個穀物市場的價格。大多數預測美國玉米產量的研究聚焦於植被指數對產量的影響，而本研究根據植被覆蓋率越高且種植面積越大則作物產量越大的假設，將針對植被指數和遙測面積建立迴歸模型預測美國玉米產量。使用的迴歸模型有多元線性迴歸、偏最小二乘迴歸、逐步迴歸以及利用高斯核的支持向量迴歸，最後實驗結果以高斯核(Radial basis function kernel)的支持向量迴歸表現最佳，?2值為 0.94。 zh_TW dc.description.abstract Unite States of America harvests the largest crop of maize in the world. The volume it grows, therefore, critically affects many countries and industries. Predicting the yields thus have discussed by prior studies. Recently, with the conveniently available of Satellite images, several research has attempted to make prediction vegetation index on yield. However, this research argues that besides vegetation index, the data of telemetry area are also needed, as higher vegetation coverage and larger planting area lead to greater crop yields. This research therefore, strives to derive 9 years of data for 4 most important states to train various regression models, which include multivariable linear regression, partial least squares regression, stepwise regression, and support vector regression with Gaussian kernel. The result shows that the support vector regression with Gaussian kernel (Radial basis function kernel) performed the best, with R^2 value reaches 0.94. en_US DC.subject 衛星遙測 zh_TW DC.subject 遙測面積 zh_TW DC.subject 植被指數 zh_TW DC.subject 迴歸分析 zh_TW DC.subject Satellite telemetry en_US DC.subject Telemetry area en_US DC.subject Vegetation Index en_US DC.subject Regression Analysis en_US DC.title 以衛星資訊建立預測玉米產量之模型 zh_TW dc.language.iso zh-TW zh-TW DC.title Predicting maize yields with satellite information en_US DC.type 博碩士論文 zh_TW DC.type thesis en_US DC.publisher National Central University en_US