Artificial neural network for typhoon rainfall and flood forecasting
Date Issued
2006
Date
2006
Author(s)
Tsai, Fei-Yu
DOI
zh-TW
Abstract
Abstract
The main objective of this thesis aims at clarifying whether typhoon is a vital factor to be considered when forecasting runoff. To advance the research, I construct, three different runoff forecast models that each consists of several distinct input values. For the first forecast model, denoted ANN1, I incorporate variables such as the measurable typhonic data, rainfall, and runoff to be the input values of the radial basis function network. In the second model, first I acquire the forecasted rainfall value through integrating the typhonic data and rainfall information into the back-propagation n network and Semivariogram thesis. Then I combine the outputted rainfall value with the runoff to assemble the second forecast model, named ANN2. For the third forecast model, marked ANN3, I plug in only the observed rainfall value and runoff as the input values of the radial basis function network. In conclusion, I compare three models on the basis of their predicting capability and accuracy level. The result reveals that ANN2 appears to be the most reliable model among others in forecasting the typhoon-related runoff. Moreover, the research goes on to attest the hypothesis that typhoon is indeed a crucial factor for forecasting runoff.
Subjects
類神經網路
降雨
流量
artificial neural network
typhoon
rainfall
flood
forecasting
Type
thesis
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