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課程名稱	類神經網路於水文系統之建構與應用 Artificial Neural Networks in Hydrology
開課學期	106-1
授課對象	工學院水利工程組
授課教師	林國峰
課號	CIE8026
課程識別碼	521 D4030
班次
學分	3.0
全/半年	半年
必/選修	選修
上課時間	星期四10,A,B(17:30~20:10)
上課地點	土224
備註	限本系所學生(含輔系、雙修生) 總人數上限：6人
Ceiba 課程網頁	http://ceiba.ntu.edu.tw/1061CIE8026_
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課程概述	本課程探討類神經網路在水文系統的建構與應用。類神經網路具有模擬高度非線性系統的能力，且具有優良的計算效率，因此近年來已經被廣泛的應用在各種科學領域。本課程介紹倒傳遞類神經網路、幅狀基底類神經網路、支持向量機及自組織映射圖等不同的網路，除了瞭解不同網路的演算法及特性，並進一步對各模式的能力與差異進行分析。本課程針對數個研究案例進行討論，包含了颱風降雨預報、颱風洪水預報、水庫入流量預報、降雨逕流模擬、雨型設計、地下水位預報、地下水參數推估以及降雨量的空間推估等，藉由不同的研究案例研討類神經網路模式的特性以及在建構與操作時可能面臨的問題。
課程目標	培養以類神經網路研究水文相關問題之能力
課程要求	完成研究報告一篇並於課堂簡報。
預期每週課後學習時數
Office Hours
指定閱讀	Lin GF, Chen LH, 2004, A non-linear rainfall-runoff model using radial basis function network, Journal of Hydrology, Vol. 289, Issues 1-4, pp. 1-8. Lin GF, Chen LH, 2005, Application of artificial neural network to typhoon rainfall forecasting, Hydrological Processes, Vol. 19, Issue 9, pp. 1825-1837. Lin GF, Wu MC, 2007, A SOM-based approach to estimating design hyetographs of ungauged sites, Journal of Hydrology, Vol. 339, Issues 3-4, pp. 216-226. Lin GF, Chen GR, Huang PY, Chou YC, 2009, Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods, Journal of Hydrology, Vol. 372, Issues 1-4, pp. 17-29. Lin GF, Chen GR, Wu MC, Chou YC, 2009, Effective forecasting of hourly typhoon rainfall using support vector machines, Water Resources Research, Vol. 45, Article Number W08440. Lin GF, Wu MC, 2009, A hybrid neural network model for typhoon-rainfall forecasting, Journal of Hydrology, Vol. 375, Issues 3-4, pp. 450-458. Lin GF, Huang PY, Chen GR, 2010, Using typhoon characteristics to improve the long lead-time flood forecasting of a small watershed, Journal of Hydrology, Vol. 380, Issues 3-4, pp. 450-459. Lin GF, Chen GR, Huang PY, 2010, Effective typhoon characteristics and their effects on hourly reservoir inflow forecasting, Advances in Water Resources, Vol. 33, Issue 8, pp. 887-898. Lin GF, Wu MC, 2011, An RBF network with a two-step learning algorithm for developing a reservoir inflow forecasting model, Journal of Hydrology, Vol. 405, Issues 3-4, pp. 439-450. Lin GF, Chou YC, Wu MC, 2013, Typhoon flood forecasting using integrated two-stage support vector machine approach, Journal of Hydrology, Vol. 486, pp. 334-342. Lin GF, Jhong BC, Chang CC, 2013, Development of an effective data-driven model for hourly typhoon rainfall forecasting, Journal of Hydrology, Vol. 495, pp. 52-63. Lin GF, Huang PK, Lin HY, 2013, Forecasting tropical cyclone intensity change in the western North Pacific, Journal of Hydroinformatics, Vol. 15, No. 3, pp. 952-966. Lin GF, Lin HY, Chou YC, 2013, Development of a real-time regional inundation forecasting model for the inundation warning system, Journal of Hydroinformatics, Vol. 15, No. 4, pp. 1391-1407. Wu MC, Lin GF, Lin HY, 2014, Improving the forecasts of extreme streamflow by support vector regression with the data extracted by self-organizing map, Hydrological Processes, Vol. 28, Issue 2, pp. 386-397. Lin GF, Jhong BC, 2015, A real-time forecasting model for the spatial distribution of typhoon rainfall, Journal of Hydrology, Vol. 521, pp. 302-313. Wu MC, Lin GF, 2015, An hourly streamflow forecasting model coupled with an enforced learning strategy, Water, Vol. 7, Issue 11, pp. 5876-5895. Jhong BC, Wang JH, Lin GF, 2016, Improving the long lead-time inundation forecasts using effective typhoon characteristics, Water Resources Management, Vol. 30, Issue 12, pp. 4247-4271. Lin GF, Chang MJ, Wu JT, 2017, A hybrid statistical downscaling method based on the classification of rainfall patterns, Water Resources Management, Vol. 31, Issue 1, pp. 377-401. Wu MC, Lin GF, 2017, The very short-term rainfall forecasting for a mountainous watershed by means of an ensemble numerical weather prediction system in Taiwan, Journal of Hydrology, Vol. 546, pp. 60-70. Jhong BC, Wang JH, Lin GF, 2017, An integrated two-stage support vector machine approach to forecast inundation maps during typhoons, Journal of Hydrology, Vol. 547, pp. 236-252. Lin GF, Chang MJ, Huang YC, Ho JY, 2017, Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression, Engineering Geology. Vol. 224, 62–74. Lin GF*, Chang MJ, Wang CF, 2017, A novel spatiotemporal statistical downscaling method for hourly rainfall, Water Resources Management. Vol. 31, Issue 11, pp. 3465-3489.
參考書目	Govindaraju RS, Rao AR, 2000, Artificial Neural Networks in Hydrology. Kluwer Academic Publishers, Boston.
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