| Summary: | 碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 102 === Urban flood control is a crucial task in developed cities, which faces a great challenge of fast rising peak flows resulting from urbanization and climate change. Therefore, it is imperative to construct an efficient and accurate model to forecast the water levels of rivers in urban areas during flood periods. Artificial intelligence (AI) techniques possess an outstanding ability to handle highly non-linear complex systems and are implemented to make real-time water level forecasts in this study. The Yu-Cheng pumping station located in Taipei City, Taiwan, is selected as the study area. The purpose of this study is to construct water-level forecasting models and a real-time operating strategy of pumps.
The first part of the study is dedicated to water level forecasting. Hydrological data were collected and fully explored by statistical techniques to identify the time span of rainfall affecting the rise of the water level in the floodwater storage pond (FSP) at the pumping station. Effective factors (rainfall stations) that significantly affect the FSP water level are extracted by the Gamma test (GT). For model construction, one static artificial neural network (ANN) (backpropagation neural network-BPNN) and two dynamic ANNs (Elman neural network - Elman NN; nonlinear autoregressive network with exogenous inputs - NARX network) are used to construct multi-step-ahead FSP water level forecasting models. A third-order autoregressive model (AR3) is used to construct multi-step-ahead river water level forecasting models.
The second part of this study is dedicated to operating strategy. Historical operating records of pumps at the pumping station were collected. Effective factors that affect the pumping operation are explored through the correlation analysis between the forecasted FSP and river water levels and historical hydrographs and operating records. The adaptive network-based fuzzy inference system (ANFIS) is used to construct multi-step-ahead operation strategy models in consideration of water level forecasting models.
The results demonstrate that the dynamic NARX network is superior to the two other comparative ANN models in making FSP water level forecasting; the AR3 model can accurately make river water level forecasting; and the ANFIS can suggest an operating strategy that suitably and reliably simulates historical pumping operations. The proposed methodology can provide managers and operators of pumping stations with a guideline for making suitable real-time pumping operation in response to drastic water level variations during flood periods, which is beneficial to urban flood control management.
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