Prediction of rainfall-runoff processes through black-box techniques

• Main Author: Lee, Liang Cen
• Other Authors: Qin Xiaosheng
• Format: Final Year Project
• Language: English
• Published: 2015
• Subjects: DRNTU::Engineering::Civil engineering
• Online Access: http://hdl.handle.net/10356/63502
• Institution: Nanyang Technological University

Hydrological forecasting techniques have been dramatically developed today. However, traditional predicting methods confront difficulties due to the diverse applications in water resource and management with complex and non-linear rainfall-runoff relationships associated with it. This project focused on artificial neural network (ANN) model which attempts to process the data in manner of black-box-based models, providing more reliable but time-efficient estimates regarding rainfall-runoff information. Without considering all the influencing factors in the process of calibration, ANN provides a more systematic alternative to simulate behaviors of historical data and operate adaptively by its predictive capability. Therefore, ANN method has currently been achieved an improvement of accuracy and flexibility compared with existing traditional and linear regression methods. In this study, the historical data ranging from 1970 to 1985, extracted from Kootenay River Watershed at state of British Columbia in Canada, was used to assess the ability of ANN. The extracted data comprised daily precipitation, daily minimum and maximum temperature, daily sunlight radiation and daily runoff from three hydrometric stations and three meteorological stations located along Kootenay River. Levenberg-Marquardt Backpropagation (LMBP) was used as training algorithm in ANN model. In order to generate an optimum prediction, different numbers and combinations of data were used as inputs to maximize the efficiency of ANN model. Two evaluators, including coefficient of determination (R2) and mean squared error (MSE), were used to assess the performance of ANN model. This study indicated that ANN model was able to produce favorable outcomes and it was simple to be learnt and used in complex hydrological forecasting by using Graphical User Interface (GUI) provided in neural network toolbox of Matlab R2013a. The significance of daily runoff data was highlighted in this study while the daily precipitation data was unable to generate reasonable outputs with a value of R2 closing to 1. The results also showed that the more inputs parameters used would help in gaining more accurate outcomes.