A Study of the Nested Hyper-Rectangles Learning Model for Water Resources Systems
Resource
中國農業工程學報38(3),27-37
Journal
中國農業工程學報
Pages
27-37
Date Issued
1992-09
Date
1992-09
Author(s)
Abstract
Prediction and categorization are important tasks of hydrologists. The nested hyper-rectangles learning model which is an examplarbased learning model is studied and applied to above tasks. The main idea of the model is "seeding" history data in Euclidean n-space,Eⁿ,as exemplars, then comparing new examples (data) to those seeding points, and finding the most similar example in memory. A dynamic adjustment for model's parameters in proposed with the "distance metric" which is used to determine the similarity, so the simulated system can be represented or predicted more and more accurate through the feedback of added examples. That is the model has ability to learn. In order to show the general characteristics of the model performances, a simple mathematical function is simulated by the model. It is then applied to three different hydrological systems. They are: (1) forecasting streamflow categorization; (2) estimating the missing record of daily precipitation; and (3) extending the annual streamflow. The results demonstrate the power of hyperrectangle learning model, and the model can be a very useful tool on hydrological system.
Subjects
水資源
系統
Type
journal article
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巢狀超矩型學習模式於水資源系統之研究.pdf
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