Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/95372
Title: Time-lagged prediction of rainfall-runoff processes through black-box and regression techniques
Authors: Li, Siyu.
Keywords: DRNTU::Engineering::Civil engineering::Water resources
Issue Date: 2012
Source: Li, S. (2012). Time-lagged prediction of rainfall-runoff processes through black-box and regression techniques. Final year project report, Nanyang Technological University.
Abstract: This paper focuses on the study of comparison among various black box and regression-based techniques in determining the rainfall-runoff relations. Without considering the temperature, topography or other parameters of the study area, simply using the data of rainfall and runoff to predict the future runoff is the key characteristic of these techniques. Ignoring the parameters of temperature, topography may lead to inaccuracy in forecasting future runoff, but the use of black-box or regression-based techniques provide a simple and fast way to determine the runoff. Three methods were compared in this study: autoregressive integrated moving average (ARIMA), regression, and artificial neural network (ANN). ARIMA model is a traditional approach to handling the rainfall-runoff model, which is highly fitted to the time series data. The main characteristic of this model is that, instead of considering two parameters, rainfall and runoff, it only uses one parameter, which is runoff, to forecast future runoff value. The multiple-linear regression model and the non-linear (quadratic) regression model comprise both parameters. The only difference between this two regression models is the order of parameter. Furthermore, artificial neural network (ANN) model are also studied to compare each method’s strengths and weakness.
URI: https://hdl.handle.net/10356/95372
http://hdl.handle.net/10220/9407
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:OAPS (CEE)

Files in This Item:
File Description SizeFormat 
Cwr18.pdf1.77 MBAdobe PDFThumbnail
View/Open

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.