Title

集水區水文監測網絡最佳配置之理論發展與模式建構

Translated Titles

The development of optimal hydrometric networks model in a Watershed

DOI

10.6342/NTU.2012.01500

Authors

蘇荷婷

Key Words

資訊理論 ; 熵 ; 雨量站網評估 ; Information Theory ; Entropy ; Raingauge Network Assessment

PublicationName

臺灣大學土木工程學研究所學位論文

Volume or Term/Year and Month of Publication

2012年

Academic Degree Category

碩士

Advisor

游景雲

Content Language

繁體中文

Chinese Abstract

台灣地區近幾年由於逐漸面臨氣候變遷極端降雨的威脅,對於集水區水文資訊掌握相對的必須提高,才能進行更好的集水區管理與水庫操作維護。而水文資料的正確性與完整性則需仰賴完善的地表水文監測體系之設置,因此在未來調整及增設相關水文測站以因應所需是必然的。   而站網的設置必須需瞭解既有的監測站網所接受的資訊度,並考慮水文資料的空間與時間分佈特性,藉此推估集水區全域之水文特性,才能建立合理有效的水文監測站網。故本研究主要利用資訊熵理論為基礎,以發展最佳化水文站網空間評估模式為目標。不同於以往以熵理論規劃設計站網之研究,本研究所提出的方法中,分別考量了空間中非等向性的資訊傳遞特性,以及於多維度的測站組合下,彼此資訊量重疊性之估算,進一步發展二維的資訊傳遞模型與多變量資訊近似演算法,用以推估集水區內未設測站區域的水文資訊,建立資訊等值圖,作為評估資訊量空間分佈之基礎。   本研究發展之模式以石門水庫集水區雨量站網為應用案例,評估其資訊量於集水區內的分佈特性,並結合最佳化空間搜尋理論之最陡坡度法,以集水區監測總資訊量最大化為目標函數,推演出適用於石門水庫集水區測站網路分佈之演算模式,藉由一套具備理論基礎之模式來提供測站設置決策參考。其現階段模式以分析六個雨量測站之資訊空間分佈為主,分別討論降雨資訊量之空間變異性、不同降雨資料型態以及極端降雨事件對集水區雨量站網設置之影響。

English Abstract

Recently, Taiwan is facing more and more severe climate challenges. For better watershed management and reservoir operation, must understanding sufficient hydrometric information of watershed. Due to the accuracy and completeness of hydrometric data was depended on the hydrometric monitoring network design, so the development of efficient hydrometric networks for basin-monitoring is required. The establishment of a reasonable and effective hydrological monitoring network which need to consider not only the spatial and temporal characteristic of hydrological data, but also the measure efficiency of existing network. Based on information theory, this study proposes an optimal hydrometric network evaluation model. Different from previous researches, two factors, (1) the anisotropic information delivery and (2) the effect of transinformation among multi-gauges, are considered to incorporate in the method proposed in the study. A two dimensional information delivered model and multivariate information approximation algorithm is developed for the estimation the information content at ungauged location. And construct the information contour, as the basis for assessment the spatial distribution of information. We applied this approach to the Shihmen Reservoir watershed to understand the information distribution in this region. And steepest descent method was combined with the objective function which using maximum total information as criterion. Base on previous theory, a spatial optimization algorithm can be developed to perform the site selection of hydrometric network for decision making. The stage of this model was focus on analyzing the information of six stations in the watershed. The spatial variability of precipitation data, the difference of data type and the extreme rainfall events would be discuss in this study.

Topic Category 工學院 > 土木工程學研究所
工程學 > 土木與建築工程
Reference
  1. 1. Al-Zahrani, M., & Husain, T. (1998). An algorithm for designing a precipitation network in the south-western region of Saudi Arabia. Journal of Hydrology, 205(3-4), 205-216.
    連結:
  2. 2. Alfonso, L., Lobbrecht, A., & Price, R. (2010). Optimization of water level monitoring network in polder systems using information theory. Water Resources Research, 46(12), W12553.
    連結:
  3. 3. Amorocho, J., & Espildora, B. (1973). Entropy in the assessment of uncertainty in hydrologic systems and models. Water Resources Research, 9(6), 1511-1522.
    連結:
  4. 4. Harmancioglu, N., & Yevjevich, V. (1987). Transfer of hydrologic information among river points. Journal of Hydrology, 91(1-2), 103-118.
    連結:
  5. 5. Harmancioglu, N., Alpaslan, N., & Singh, V. (1994). Assessment of the entropy principle as applied to water quality monitoring network design. Stochastic and statistical methods in hydrology and environmental engineering, 3, 135-148.
    連結:
  6. 6. Husain, T. (1987). Hydrologic network design formulation. Canadian Water Resources Journal, 12(1), 44-63.
    連結:
  7. 7. Husain, T. (1989). HYDROLOGIC UNCERTAINTY MEASURE AND NETWORK DESIGN. JAWRA Journal of the American Water Resources Association, 25(3), 527-534.
    連結:
  8. 8. Huang, J., Cai, Y., & Xu, X. (2007). A hybrid genetic algorithm for feature selection wrapper based on mutual information. Pattern Recognition Letters, 28(13), 1825-1844.
    連結:
  9. 9. Kwak, N., & Choi, C. H. (2002). Input feature selection for classification problems. Neural Networks, IEEE Transactions on, 13(1), 143-159.
    連結:
  10. 10. Krstanovic, P., & Singh, V. (1992). Evaluation of rainfall networks using entropy: I. Theoretical development. Water Resources Management, 6(4), 279-293.
    連結:
  11. 11. Krstanovic, P., & Singh, V. (1992). Evaluation of rainfall networks using entropy: II. Application. Water Resources Management, 6(4), 295-314.
    連結:
  12. 12. Karamouz, M., Nokhandan, A. K., Kerachian, R., & Maksimovic, Č. (2009). Design of on-line river water quality monitoring systems using the entropy theory: a case study. Environmental monitoring and assessment, 155(1), 63-81.
    連結:
  13. 13. Li, C., Singh, V. P., & Mishra, A. K. (2012). Entropy theory-based criterion for hydrometric network evaluation and design: Maximum information minimum redundancy. Water Resources Research, 48(5), W05521.
    連結:
  14. 14. Loucks, D. P., Van Beek, E., Stedinger, J. R., Dijkman, J. P. M., & Villars, M. T. (2005). Water resources systems planning and management: an introduction to methods, models and applications: Paris: UNESCO.
    連結:
  15. 15. Mishra, A. K., & Coulibaly, P. (2009). Developments in hydrometric network design: A review. Rev. Geophys, 47.
    連結:
  16. 16. Mogheir, Y., De Lima, J., & Singh, V. (2004). Characterizing the spatial variability of groundwater quality using the entropy theory: I. Synthetic data. Hydrological Processes, 18(11), 2165-2179.
    連結:
  17. 17. Mogheir, Y., De Lima, J., & Singh, V. (2004). Characterizing the spatial variability of groundwater quality using the entropy theory: II. Case study from Gaza Strip. Hydrological Processes, 18(13), 2579-2590.
    連結:
  18. 18. Masoumi, F., & Kerachian, R. (2010). Optimal redesign of groundwater quality monitoring networks: a case study. Environmental monitoring and assessment, 161(1), 247-257.
    連結:
  19. 19. Markus, M., Vernon Knapp, H., & Tasker, G. D. (2003). Entropy and generalized least square methods in assessment of the regional value of streamgages. Journal of Hydrology, 283(1-4), 107-121.
    連結:
  20. 20. Owlia, R. R., Abrishamchi, A., & Tajrishy, M. (2011). Spatial–temporal assessment and redesign of groundwater quality monitoring network: a case study. Environmental monitoring and assessment, 172(1), 263-273.
    連結:
  21. 21. Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell system technical journal, 27(7), 379–423.
    連結:
  22. 22. Shannon, C. E. (1949). Communication theory of secrecy systems. Bell system technical journal, 28(4), 656-715.
    連結:
  23. 23. Shannon, C. E., & Weaver, W. (1949). The mathematical theory of information.
    連結:
  24. 24. Singh, V. (1997). The use of entropy in hydrology and water resources. Hydrological Processes, 11(6), 587-626.
    連結:
  25. 28. Yang, Y., & Burn, D. H. (1994). An entropy approach to data collection network design. Journal of Hydrology, 157(1-4), 307-324.
    連結:
  26. 30. 呂冠德. (2009). 石門水庫上游集水區雨量監測站設置之探討. 臺灣大學生物環境系統工程學研究所學位論文(2009 年).
    連結:
  27. 31. 陳茹蕙. (2007). 以連續機率分佈熵及克利金建構雨量站網. 臺北科技大學土木與防災研究所學位論文(2007 年).
    連結:
  28. 25. Sarlak, N., & Sorman, A. (2006). Evaluation and selection of stream flow network stations using entropy methods. Turkish Journal of Engineering Environmental Science, 30, 91-100.
  29. 26. Srinivasa, S. (2008). A review on multivariate mutual information. Univ. of Notre Dame, Notre Dame, Indiana.
  30. 27. World Meteorological Organization (WMO) (1970), Guide to Hydrometeorological Practices, Tech. Pap. 82, Geneva.
  31. 29. 王如意, & 易任. (1989). 應用水文學.
  32. 32. 侯如真, & 鄭克聲. (2003). 訊息熵應用於雨量站網評估之理論探討. Journal of Taiwan Water Conservancy, 51(2).
  33. 33. 江介倫, & 鄭克聲. (2000). 訊息熵在雨量站網設計之應用.