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Entropy 2015, 17(3), 1023-1041; doi:10.3390/e17031023

Mining Informative Hydrologic Data by Using Support Vector Machines and Elucidating Mined Data according to Information Entropy

Department of Water Resources Engineering and Conservation, Feng Chia University, No. 100, Wenhua Road, Taichung City 40724, Taiwan
Academic Editor: Hwa-Lung Yu
Received: 8 January 2015 / Revised: 9 February 2015 / Accepted: 27 February 2015 / Published: 2 March 2015
(This article belongs to the Special Issue Entropy and Space-Time Analysis in Environment and Health)
View Full-Text   |   Download PDF [1546 KB, uploaded 2 March 2015]   |  

Abstract

The support vector machine is used as a data mining technique to extract informative hydrologic data on the basis of a strong relationship between error tolerance and the number of support vectors. Hydrologic data of flash flood events in the Lan-Yang River basin in Taiwan were used for the case study. Various percentages (from 50% to 10%) of hydrologic data, including those for flood stage and rainfall data, were mined and used as informative data to characterize a flood hydrograph. Information on these mined hydrologic data sets was quantified using entropy indices, namely marginal entropy, joint entropy, transinformation, and conditional entropy. Analytical results obtained using the entropy indices proved that the mined informative data could be hydrologically interpreted and have a meaningful explanation based on information entropy. Estimates of marginal and joint entropies showed that, in view of flood forecasting, the flood stage was a more informative variable than rainfall. In addition, hydrologic models with variables containing more total information were preferable to variables containing less total information. Analysis results of transinformation explained that approximately 30% of information on the flood stage could be derived from the upstream flood stage and 10% to 20% from the rainfall. Elucidating the mined hydrologic data by applying information theory enabled using the entropy indices to interpret various hydrologic processes. View Full-Text
Keywords: informative data; support vector machines; information entropy; flood informative data; support vector machines; information entropy; flood
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Chen, S.-T. Mining Informative Hydrologic Data by Using Support Vector Machines and Elucidating Mined Data according to Information Entropy. Entropy 2015, 17, 1023-1041.

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