以衛星資訊建立預測玉米產量之模型;Predicting maize yields with satellite information

NCUIR > School of Management at National Central University > Graduate Institute of Business Administration > Electronic Thesis & Dissertation > Item 987654321/88463

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Title:	以衛星資訊建立預測玉米產量之模型;Predicting maize yields with satellite information
Authors:	施又升 Shih, You-Sheng
Contributors:	企業管理學系
Keywords:	衛星遙測;遙測面積;植被指數;迴歸分析;Satellite telemetry;Telemetry area;Vegetation Index;Regression Analysis
Date:	2022-06-30
Issue Date:	2022-07-14 10:16:00 (UTC+8)
Publisher:	國立中央大學
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。;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.
Appears in Collections:	[Graduate Institute of Business Administration] Electronic Thesis & Dissertation

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