Prediction of Flood Warning by Support Vector Machine with Locally Linear Embedding and Simulated Annealing in case of Bajhang River Basin
Date Issued
2014
Date
2014
Author(s)
Lee, Cheng-Lun
Abstract
The issue of the floods is important in Taiwan. It is because the narrow and high topography of the island make lots of rivers steep in Taiwan. The tropical depression likes typhoon always causes rivers to flood. Prediction of river flow and river stage under the depression rainfall circumstances is important for government to announce the warning of flood. Every time typhoon passed through Taiwan, there were always floods along some rivers. The warning is classified to three levels according to the warning water levels in Taiwan. Propose of this study is to predict the level of floods warning from the information of precipitation and recorded river stage. To classify the extent of floods warning by the above-mentioned information and modeling the problems, a machine learning model, Support vector machine (SVM), is used. Simulated annealing (SA) is a probabilistic heuristic algorithm to find out the optimization parameter in SVM model. Another manifold learning algorithm which called locally linear embedding (LLE) is used to replace the function of kernel transform in SVM. The result shows that LLE is work for replacing kernel transform, and it’s a new idea for combine these three methods together. This model can predict the level of flood warning by precipitation and recorded river stage, and it can make government announce the warning of flood in the better timing that can keep the danger of flood from residents along the rivers.
Subjects
River flood
River stage prediction model
Support vector machine
Simulated Annealing
Locally linear embedding
Type
thesis
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