Rainfall - runoff modeling using artificial neural network method
Rainfall and surface runoff are the driving forces behind all stormwater studies and designs. The relationship is known to be highly non-linear and complex that is dependent on numerous factors. In order to overcome the problems on the non-linearity and lack of information in rainfall-runoff modelli...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2005
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/18659/1/NorIrwanAhmadNorPFKA2005.pdf http://eprints.utm.my/id/eprint/18659/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:62406?queryType=vitalDismax&query=modeling+using+artificial+neural+network+method+&public=true |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Rainfall and surface runoff are the driving forces behind all stormwater studies and designs. The relationship is known to be highly non-linear and complex that is dependent on numerous factors. In order to overcome the problems on the non-linearity and lack of information in rainfall-runoff modelling, this study introduced the Artificial Neural Network (ANN) approach to model the dynamic of rainfall-runoff processes. The ANN method behaved as the black-box model and proven could handle the non-linearity processes in complex system. Numerous structures of ANN models were designed to determine the relationship between the daily and hourly rainfall against corresponding runoff. Therefore, the desired runoff could be predicted using the rainfall data, based on the relationship established by the ANN training computation. The ANN architecture is simple and it considers only the rainfall and runoff data as variables. The internal processes that control the rainfall to runoff transformation will be translated into ANN weights. Once the architecture of the network is defined, weights are calculated so as to represent the desired output through a learning process where the ANN is trained to obtain the expected results. Two types of ANN architectures are recommended and they are namely the multilayer perceptron (MLP) and radial basis function (RBF) networks. Several catchments such as Sungai Bekok, Sungai Ketil, Sungai Klang and Sungai Slim were selected to test the methodology. The model performance was evaluated by comparing to the actual observed flow series. Further, the ANN results were compared against the results produced from the application of HEC-HMS, XP-SWMM and multiple linear regression (MLR). It had been found that the ANN could predict runoff accurately, with good correlation between the observed and predicted values compared to the MLR, XP-SWMM and HEC-HMS models. Obviously, the ANN application to model the daily and hourly streamflow hydrograph was successful. |
|---|