A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Hybrid Wavelet–Genetic Programming Approach to Optimize ANN Modeling of Rainfall–Runoff Process
In this paper, the wavelet analysis was linked to the genetic programming (GP) concept for constructing a hybrid model to detect the seasonality patterns in the rainfall–runoff time process. This approach was used to determine the dominant input variables of an artificial neural network (ANN) rainfall–runoff model via a sensitivity analysis. In this way, the main time series of two variables, rainfall and runoff, were decomposed into some multi frequency time series by the wavelet transform. Then, these decomposed time series were imposed as input data to the GP to optimize the input structure of ANN model. This methodology was utilized in daily and monthly timescale modeling for two watersheds with distinct climatologic regimes. The obtained results were compared favorably to ANN and GP models. The obtained results showed that the proposed model can monitor both short and long term patterns due to the use of multiscale time series of rainfall and runoff data as the GP inputs. Moreover, using the proposed sensitivity analysis, the number of input variables in the ANN modeling was decreased.
Hybrid Wavelet–Genetic Programming Approach to Optimize ANN Modeling of Rainfall–Runoff Process
In this paper, the wavelet analysis was linked to the genetic programming (GP) concept for constructing a hybrid model to detect the seasonality patterns in the rainfall–runoff time process. This approach was used to determine the dominant input variables of an artificial neural network (ANN) rainfall–runoff model via a sensitivity analysis. In this way, the main time series of two variables, rainfall and runoff, were decomposed into some multi frequency time series by the wavelet transform. Then, these decomposed time series were imposed as input data to the GP to optimize the input structure of ANN model. This methodology was utilized in daily and monthly timescale modeling for two watersheds with distinct climatologic regimes. The obtained results were compared favorably to ANN and GP models. The obtained results showed that the proposed model can monitor both short and long term patterns due to the use of multiscale time series of rainfall and runoff data as the GP inputs. Moreover, using the proposed sensitivity analysis, the number of input variables in the ANN modeling was decreased.
Hybrid Wavelet–Genetic Programming Approach to Optimize ANN Modeling of Rainfall–Runoff Process
Nourani, Vahid (author) / Komasi, Mehdi (author) / Alami, Mohammad Taghi (author)
Journal of Hydrologic Engineering ; 17 ; 724-741
2011-09-15
182012-01-01 pages
Article (Journal)
Electronic Resource
English
Hybrid Wavelet-Genetic Programming Approach to Optimize ANN Modeling of Rainfall-Runoff Process
| Online Contents | 2012
Hybrid Wavelet-Genetic Programming Approach to Optimize ANN Modeling of Rainfall-Runoff Process
| British Library Online Contents | 2012
Hybrid Wavelet Neuro-Fuzzy Approach for Rainfall-Runoff Modeling
| British Library Online Contents | 2016
Hybrid Wavelet Neuro-Fuzzy Approach for Rainfall-Runoff Modeling
| Online Contents | 2016