博碩士論文 109421052 完整後設資料紀錄

DC 欄位 語言
DC.contributor企業管理學系zh_TW
DC.creator施又升zh_TW
DC.creatorYou-Sheng Shihen_US
dc.date.accessioned2022-6-30T07:39:07Z
dc.date.available2022-6-30T07:39:07Z
dc.date.issued2022
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/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.descriptionNational Central Universityen_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.abstractUnite 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.subjectSatellite telemetryen_US
DC.subjectTelemetry areaen_US
DC.subjectVegetation Indexen_US
DC.subjectRegression Analysisen_US
DC.title以衛星資訊建立預測玉米產量之模型zh_TW
dc.language.isozh-TWzh-TW
DC.titlePredicting maize yields with satellite informationen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明