Memorial University DAI: Flood forecasting on the Humber River using an artificial neutral network approach

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Title	Flood forecasting on the Humber River using an artificial neutral network approach
Author	Cai, Haijie, 1983-
Description	Thesis (M. Eng.)--Memorial University of Newfoundland, 2010. Engineering and Applied Science
Date	2010.
Pagination	xi, 98 leaves : ill., maps
Subject	Flood forecasting--Newfoundland and Labrador--Humber River Watershed; Neural networks (Computer science); Stream measurements--Newfoundland and Labrador--Humber River Watershed--Computer simulation;
Degree	M. Eng.
Degree Grantor	Memorial University of Newfoundland. Faculty of Engineering and Applied Science
Discipline	Engineering and Applied Science
Language	Eng
Spatial Coverage	Canada--Newfoundland and Labrador--Humber River Basin
Notes	Includes bibliographical references (leaves 94-98)
Abstract	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 River Basin. Two types of ANN were considered, general regression neural network (GRNN) and the back propagation neural network (BPNN). GRNN is a nonparametric method with no training parameters to be adjusted during the training process. BPNN on the other hand has several parameters such as the learning rate, momentum, and calibration interval, which can be adjusted during the training to improve the model. A design of experiment (DOE) approach is used to study the effects of the various inputs and network parameters at various stages of the network development to obtain an optimal model. One day ahead forecasts were obtained from the two ANNs using air temperature, precipitation, cumulative degree-days, and flow data all suitably lagged (i.e. of 1 day or 2 day before) as inputs. It was found that the GRNN model produced slightly better forecasts than the BPNN for the Upper Humber and both models performed equally well for the Lower Humber. The ANN approach also produced much better forecasts than the routing model developed by the WRMD but was not much better than the dynamic regression model except for the Upper Humber.
Type	Text
Format	Image/jpeg; Application/pdf
Source	Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries
Local Identifier	a3295725
Rights	The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.
Collection	Electronic Theses and Dissertations
Scanning Status	Completed
PDF File	(11.24 MB) -- http://collections.mun.ca/PDFs/theses/Cai_Haijie.pdf
CONTENTdm file name	131782.cpd