臺灣博碩士論文加值系統

蒸發現象為影響水氣於水文循環分佈中重要之因素，在農業水資源管理上扮演重要角色。傳統經驗式利用氣象變數推估蒸發量，而忽略蒸發在自然界中呈高度非線性現象，故本研究利用具有分類特性的自組特徵映射網路架構一蒸發量推估模式。
本研究利用恆春氣象站的氣象變數作為模式輸入，透過自組特徵映射網路(SOM)學習，將相似特性的輸入資料聚為一類，並探討拓樸網路架構的潛在特性。自組特徵映射網路可快速有效將輸入的氣象變數分類，形成網路拓樸層，再將各聚類之中心點以線性迴歸方式與輸出層連結，可準確的推估蒸發量。另外建立強制型自組特徵映射網路(ESOM)以加強映射較為極端的案例空間，並與Modified Penman(FAO,1984)、Penman-Monteith(ICID,1994)等傳統經驗式進行比較。結果顯示，拓樸層架構能詳細說明輸入與輸出間映射的關係，且用SOM與ESOM可根據氣象變數作良好的推估；四種模式中以ESOM推估表現最好(RMSE=1.15mm/day，MAE=0.87 mm/day)，對於長期蒸發量的推估表現中，也是以ESOM表現最佳。研究再針對已建立的模式進行穩定性與適用性討論，結果顯示直接將網路用於其他地區會因區域蒸發量的差異造成模式推估值與實際觀測值有較為明顯的差異。

The phenomenon of evaporation is an important factor that affects the distribution of water in hydrological cycle and plays a key role in agriculture and water resource management. The tranditional evaporation formulas usally neglect the non-linear characteristics in the nature. In this study we propose the self-organizing map(SOM) network to estimate daily evaporation. First, the daily meteorological data from climate gauges were collected as inputs of the SOM and then classified into topology map based on their similarities to investigate their potential property. To effectively and accurately estimate the daily evaporation, the connected weights between the cluster in topology layer with output layer were trained by using the linear regression method. In addition, we bulit enforced Self-Organizing Map (ESOM) to strength mapping spaces for these extremely data and compared with Modified Penman (FAO,1984) and Penman-Monteith (ICID,1994). The results demonstrated that the topology structures of SOM and ESOM could give a meaningful map to present the clusters of meteorological variables and the networks could well estimate the daily evaporation based on the input meteorological variables used in this study. In comparing the performances of these four models, the ESOM provides the best performance (RMSE=1.15mm/day，MAE=0.87 mm/day). The ESOM performance is also well in estimating long term evaporation. We have the suitability of using these models in other areas where their evaporations are different widely from the original station, the estimation, however, are not well as the one we use in the built station. This result suggests that the network must be adequately trained before it is used to estimate the local evaporation.

摘要 I
Abstract II
目錄 IV
表目錄 VI
圖目錄 VII
第一章 緒論 1
1.1 研究動機與目的 1
1.2 研究方法 2
1.3 論文架構 3
第二章 文獻回顧 5
2.1 經驗式的應用 5
2.2 應用類神經網路推估蒸發散量 7
2.3 自組特徵映射網路之應用 9
三、理論概述 12
3.1 類神經網路概述 12
3.2 自組特徵映射網路 15
3.3強制型自組特徵映射網路 21
3.4 迴歸輸出 23
3.5 經驗公式之選用 25
3.5.1 Modified Penman 法 26
3.5.2 Penman-Monteith 法 27
3.5.3 估算式參數之決定 28
四、研究案例 32
4.1 研究區域 32
4.2 模式建立 34
4.3 結果討論 39
4.3.1 SOM的拓樸架構 39
4.3.2 ESOM的拓樸架構 43
4.3.3 模式結果比較 46
4.3.4 模式適用性探討 60
第五章 結論與建議 67
5.1 結論 67
5.2 建議 69
第六章 參考文獻 70

1.丁裕峰，2004，「併聯式類神經網路於水文事件分析與應用」，台灣大學生物環境工程研究所碩士論文。
2.王彥翔，2003，「自組特徵映射與學習向量化神經網路於河川流量之預測」，台灣大學生物環境工程研究所碩士論文。
3.甘俊二、陳清田、陳焜耀，1996，「台灣地區作物需水量推估模式之合適性研究」，農業工程學報，42(2)：8-19。
4.宋易倫、張德鑫、黃振昌，2006，「蒸發量Penman估算方程式風速函數型態之建立」，農業工程研討會。
5.杜榮鴻，2005，「應用MODIS影像估算潛勢蒸發散量之研究」，成功大學水利及海洋工程生研究所碩士論文。
6.張本初，1990，「作物需水量最佳模式之探討」，台灣大學農業工程研究所碩士論文。
7.張斐章、張麗秋，2005，「類神經網路」，東華書局。
8.連宛渝，2000，「氣候變遷對台灣水稻灌溉需水量及潛能產量之影響」，台灣大學生物環境工程研究所碩士論文。
9.陳姜琦，2002，「應用衛星搖測於區域蒸發散量之估算」，成功大學水利及海洋工程生研究所碩士論文。
10.陳清田，1991，「嘉義地區作物需水量推估之研究」，中國農業工程學報，37(1)：92-109。
11.曾柏凱，2004，「結合衛星遙測估算蒸發散量之應用探討」，成功大學水利及海洋工程生研究所碩士論文。
12.程澄元，2004，「類神經網路應用於推估蒸發散量之研究」，立德管理學院資源環境研究所碩士論文。
13.黃振昌，2003，「Penman-Monteith方程式日射-日照關係地域性參數之建立與評估」，中國農業工程學報，49(3)：79-91。
14.黃振昌、張德鑫、宋易倫，2005，「Penman-Monteith方程式蒸汽壓力差最佳計算式適用性評估：頻率分析法」，中央氣象局氣象學報，46(1)：13-30。
15.楊國珍，2004，「類神經模糊系統應用於蒸發量推估之研究」，成功大學水利及海洋工程研究所碩士論文。
16.Bos, M.G.., J. Vos, R. A. Feddes, 1996, “ A simulation model on crop irrigation water requirements.”, International Institute for Land Reclamation and improvement（ILRI）,46：61-87.
17.Chang, F. J., L. C. Chang, Y. S. Wang, 2006, “Enforced Self-Organizing Map Neural Networks for River Flood Forecasting.”, Hydrological Processes (in press).
18.Doorenbos, J. and W. O. Pruitt, 1984, “Guidelines for Predicting Crop Water Requirements.”, Irrigation and Drainage Paper 24,2nd Ed. FAO, Rome.
19.Grieu, S., F. Thiery, A. Traore, T. P. Nguyen, M. Barreau, M.Polit, 2006 , “KSOM and MLP neural networks for on-line estimating the efficiency of an activated sludge process.”, Chemical Engineering Journal of Hydrology, 116：1-11.
20.Hsu, K. L., H. V. Gupta, X. Gao, S. Sorooshian, and B. Imam, 2002, “Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis.”, Water resources research, 38(12)：10-26.
21.Keskin, M. E., O. Terzi, 2006, “Artificial neural network models of daily pan evaporation.” , Journal of Hydrology Engineering , 11(1)：65-70.
22.Keskin, M. E., O. Terzi, 2006, “Evaporation estimation models for lake Egirdir, Turkey.” , Hydrological processes, 20(11)：2381-2391.
23.Keskin, M. E., O. Terzi, and D. Taylan, 2004, “Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey.”, Hydrological science journal-journal des science, 49(6)：1001-1010.
24.Kisi, O., 2006, “Daily pan evaporation modelling using a neuro-fuzzy computing technique.”,Journal of Hydrology,329(3-4)：636-646.
25.Kohonen, T. , 1995, Self-Organizing Maps, Berlin: Springer-Verlag.
26.Kohonen, T. , 2001, Self-Organizing Maps, third ed. Springer ,Berlin.
27.Kohonen, T., 1982 “Self-Organizing Formation of Topologically Correct Feature Maps”, Biological Cybernetics, Vol. 43, pp. 59-69.
28.Kumar, M., N. S. Raghuwanshi, R. Singh, W. W. Wallender, W. O. Pruitt, 2002, “Estimating evapotranspiration using artificial neural network.”, Journal of Irrigation and Drainage Engineering,128(4)：224-233.
29.Kumar, M., N. S. Raghuwanshi, R. Singh, W. W. Wallender, W. O. Pruitt, 2002, “Estimating evapotranspiration using artificial neural network.”, Journal of Irrigation and Drainage Engineering,128(4)：224-233.
30.Lu, H. C., J. C. Hsieh, T. S. Chang, 2006 , “Prediction of daily maximum ozone concentrations from meteorological conditions using a two-stage neural network.”, Progress in Oceanography,81：124-139.
31.Moradkhani, H.,K. L. Hsu, H. V. Gupta, S. Sorooshian, 2004 , “Improved streamflow forecasting using self-organizing radial basis function artificial neural networks.”, Journal of Hydrology,295：246-262.
32.Odhiambo, L.O., R.E. Yoder, D.C. Yoder, J.W. Hines,2001, “Optimization of fuzzy evapotranspiration model through neural training with input-output examples.”, American Society of Agricultural Engineers,44(6)：1625-1633.
33.Penman, H. L., 1948, “Natural evaporation from open water, bare soil, and grass.”, Proceedings of the Royal Society of London,193(1032)：120-145.
34.Penman, H. L., 1956, “Estimating evaporation,” Trans. Am. Geoph. U. Vol. 37 No.1, pp. 43-50.
35.Penman, H. L., 1963, “Vegetation and hydrology,” Commend wealth Bureau of Soil, Hardened, Eng. Tech. Communication No. 53.
36.Regis, C., S. Frederic, C. Arthur, M. Sylvain, 2005, “Using self-organizing maps to investigate spatial patterns of non-native species.” , Biological Conservation,125：459-465.
37.Richardon, A. J., C. Risien, F. A. Shillington, 2003, “Using self-organizing map to identify patterns on satellite imagery.”, Progress in Oceanography,59：223-239.
38.Ritzema, H. P., 1994, “A simulation model on crop irrigation water requirements.” , International Institute for Land Reclamation and improvement（ILRI）,16：145-174.
39.Sudheer, K. P., A. K. Gosain, D. M. Rangan, S. M. Saheb, 2002, “Modelling evaporation using an artificial neural network algorithm.” , Hydrological Processes, 16：3189-3202.
40.Tadeusz, P., K. Andrzej, G. Maria, D. Malgorzata, 2006, “Patterning of impoundment impact on chironomid assemblages and their environment with use of the self-organizing map (SOM).” , ACTA Oecologica,30：312-321.
41.Terzi, O., M.E. Keskin, E.D. Taylan, 2006, “Estimating evaporation using ANFIS.”, Journal of Irrigation and Drainage Engineering,132(5)：503-507.
42.Xu, C. Y., V. P. Singh, 1998, “Dependence of evaporation on meteorological variables at different time-scales and intercomparison of estimation methods.” , Hydrological Processes, 12：429-442.
43.Xu, C.Y., L. Gong, T. Jiang, D. Chen, V.P. Singh, 2006, “Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment.” , Journal of Hydrology,327：81-93.