| Summary: | 碩士 === 逢甲大學 === 土木及水利工程所 === 91 === To apply deterministic model for system simulation, the assumption that systematic input-output relationship should be held in the whole event is often required. However, this assumption does not apply for nonlinear time-variant system which possible different outputs can be found for the various input structures. Under such circumstance, decision maker has to face the risk of system uncertainty which a deterministic model falls to handle in forecasting application.
For this reason, this study first utilized Back-Propagation Network (BPN) as main structure to develop Decision Group Back-Propagation Network (DGBPN) in order to create numerous BPN models which are qualified by the accuracy criterion of fitting learning data. The model then chooses the suitable model(s) from these BPN models to compute their corresponding outputs such that this model can avoid the risk of forecasting by single deterministic model.
With validation tests at Wu-Xi watershed, DGBPN performed stably and concluded fair forecast results. Allover, this research developed the methodology to provide the decision maker with trustworthy on flood forecasting.
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