Flood forecasting on the Humber River using an artificial neutral network approach

• Main Author: Cai, Haijie
• Format: Thesis
• Language: English
• Published: Memorial University of Newfoundland 2010
• Subjects: Newfoundland
• Online Access: https://research.library.mun.ca/9180/; https://research.library.mun.ca/9180/1/Cai_Haijie.pdf

In order to provide flood warnings to the residents living along the various sections of the Humber River Basin, the Water Resources Management Division (WRMD) of Department of Environment and Conservation, Government of Newfoundland and Labrador has generated flow forecasts for this basin over the years by means of several rainfall-runoff models. The first model used is the well-known Streamflow Synthesis and Reservoir Regulation Model (SSARR) which is a deterministic model that accounts for some or all of the hydrologic factors responsible for runoff in the basin. However, the accuracy of the model became worse over the years. Although it was calibrated well in the beginning, recalibration of the model has not been very successful. In addition, the model cannot take into account the snowmelt effect from the Upper Humber basin. The next model is the Dynamic Regression model, a statistically based model that uses the time series of historic flows and climate data of the basin to generate a forecast. This model was tried during the late 1990s to early 2000s. This model was found to provide better forecasts than the SSARR model, but it also does not take into account the snowmelt effect from the upper regions of the Humber River. The third model tried by the WRMD was an in-house Routing model. This method uses a series of water balance equations which can be easily implemented on a spread sheet at each gauging station. However, calibration is done subjectively and the forecast obtained for the snowy region of the Upper Humber is still a problem. In view of the foregoing issues with the above models, a better model that is easy to use and calibrate, provides accurate forecasts, and one that can take into account the snowmelt effects is required. Since 2008, the WRMD has been using the statistically based Dynamic Regression Model on an interim basis until a replacement model could be developed. -- This thesis presents the development of artificial neural network (ANN) models for river flow forecasting for the Humber ...