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教育部委託研究計畫      計畫執行:國立臺灣大學圖書館
 
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機構 日期 題名 作者
臺大學術典藏 2022-09-21T23:29:55Z Application of hybrid machine learning model for flood hazard zoning assessments Chen, Chieh Lin; Huang, I. Hang; Huang, Yun Ru; GWO-FONG LIN; Wang, Jhih Huang
臺大學術典藏 2022-03-22T08:27:50Z An optimal integration of multiple machine learning techniques to real-time reservoir inflow forecasting Huang I.-H;Chang M.-J;Lin G.-F.; Huang I.-H; Chang M.-J; Lin G.-F.; GWO-FONG LIN
臺大學術典藏 2022-03-22T08:27:50Z An optimal integration of multiple machine learning techniques to real-time reservoir inflow forecasting Huang I.-H;Chang M.-J;Lin G.-F.; Huang I.-H; Chang M.-J; Lin G.-F.; GWO-FONG LIN
臺大學術典藏 2022-03-22T08:27:49Z The effect of porosity change in bentonite caused by decay heat on radionuclide transport through buffer material Liang S.-Y;Lin W.-S;Lin G.-F;Liu C.-W;Fan C.; Liang S.-Y; Lin W.-S; Lin G.-F; Liu C.-W; Fan C.; GWO-FONG LIN
臺大學術典藏 2022-03-22T08:27:49Z The effect of porosity change in bentonite caused by decay heat on radionuclide transport through buffer material Liang S.-Y;Lin W.-S;Lin G.-F;Liu C.-W;Fan C.; Liang S.-Y; Lin W.-S; Lin G.-F; Liu C.-W; Fan C.; GWO-FONG LIN
臺大學術典藏 2022-03-22T08:25:06Z The effect of porosity change in bentonite caused by decay heat on radionuclide transport through buffer material Liang S.-Y;Lin W.-S;Lin G.-F;Liu C.-W;Fan C.; Liang S.-Y; Lin W.-S; Lin G.-F; Liu C.-W; Fan C.; GWO-FONG LIN
臺大學術典藏 2022-03-22T08:25:06Z An optimal integration of multiple machine learning techniques to real-time reservoir inflow forecasting Huang I.-H;Chang M.-J;Lin G.-F.; Huang I.-H; Chang M.-J; Lin G.-F.; GWO-FONG LIN
臺大學術典藏 2021-09-21T23:19:22Z An optimal integration of multiple machine learning techniques to real-time reservoir inflow forecasting Huang, I. Hang; Chang, Ming Jui; GWO-FONG LIN
臺大學術典藏 2021-09-14T23:19:28Z The effect of porosity change in bentonite caused by decay heat on radionuclide transport through buffer material Liang, Suu Yan; Lin, Wen Sheng; GWO-FONG LIN; CHEN-WUING LIU; CHIHHAO FAN
臺大學術典藏 2021-08-05T02:36:40Z Real-time forecasting of suspended sediment concentrations reservoirs by the optimal integration of multiple machine learning techniques Huang C.-C;Chang M.-J;Lin G.-F;Wu M.-C;Wang P.-H.; Huang C.-C; Chang M.-J; Lin G.-F; Wu M.-C; Wang P.-H.; GWO-FONG LIN

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