Investigating Shihmen Watershed Rainfall-Runoff Mechanisms and Modeling Flood Forecasting by Artificial Neural Networks
Date Issued
2012
Date
2012
Author(s)
Lin, Guei-Hong
Abstract
Investigating the rainfall-runoff processes over watersheds during typhoon periods is an important subject for water resources management. Therefore it is necessary to investigate the rainfall-runoff processes of watersheds in detail for establishing a precise flow forecast model. This study aims to investigate rainfall-runoff processes from the upstream to downstream areas of the Shihmen Reservoir watershed and then construct a multi-step-ahead flow forecast model for predicting the future 1-5 hour flow for the Shihmen Reservoir during typhoon periods. First of all, the characteristics of the rainfall-runoff processes in this watershed are investigated, results indicate: (1) based on the flow hydrographs of the areas from upstream to downstream in this watershed, it shows time lags at each stages are different: the time lag (20-25 hours) between the starting point of the rising limb and the peak point in the downstream area is longer than that (15-20 hours) in the up- and mid-stream areas while the time lag (20-25 hours) between the peak point and the starting point of the recession limb in the downstream area is longer than that (15-20 hours) in the up- and mid-stream areas; (2) according to the correlation analysis, the time lags between rainfall and runoff for big flow events are shorter than those of small flow events by 1-2 hours; and (3) it shows a positive correlation between the accumulative rainfall within a month prior to a typhoon event and the base flow of the flow station. In the second part, the artificial neural network (ANN), an effective data manipulation and prediction tool, is introduced in this study. The Back Propagation Neural Network (BPNN) model is developed for multi-step-ahead (1-5 hours) flow forecasting. Flow and rainfall data are used as the inputs to the BPNN. The time lags between rain gauge stations and flow stations and the time lags between flow stations are taken into consideration simultaneously as well. The results indicate that the inclusion of rainfall data, besides flow data, into the BPNN indeed helps to increase the accuracy of flow forecasts (the improvement rate for the RMSE is about 20%). In the recession limb, the regression analysis is used to find the optimal regression function and its forecast result is compared with that of the BPNN. The results indicate the regression analysis performs better than the BPNN in the recession limb, and the regression analysis can effectively reduce the vibration occurred in the recession limb for the ANN model.
Keywords: Rainfall-Runoff mechanism, Fuzzy Inference System (FIS), Back Propagation Neural Network (BPNN), Multistep-ahead flow forecasting.
Subjects
Rainfall-Runoff mechanism
Fuzzy Inference System (FIS)
Back Propagation Neural Network (BPNN)
Multistep-ahead flow forecasting
Type
thesis
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