Training Artificial Neural Network Using Hybrid Optimization Algorithm for Rainfall-Runoff Forecasting

Rainfall-runoff modeling is very important for Water Resources Management because accurate and timely prediction can avoid accidents, such as the life risk, economic losses, etc. This paper proposed the novel hybrid optimization algorithm to combine Neural Network (NN) for rainfall-runoff modeling, namely HGASA-NN. Firstly, a novel and specialized hybrid optimization strategy by incorporating Simulated Annealing algorithm (SA) into Genetic Algorithm (GA) was used to train the initial connection weights and thresholds of NN. Secondly, the Back Propagation (BP) algorithm was adjusted the final weights and biases. Finally, a numerical example of daily rainfall-runoff data was used to elucidate the forecasting performance of the proposed HGASA-NN model. The HGASA-NN can make use of not only strong global searching ability of the GASA, but also strong local searching ability of the BP algorithm. The forecasting results indicate that the proposed model yields more accurate forecasting results than the BP-NN and pure GA training NN model. Therefore, the HGASA-NN model is a promising alternative for rainfall-runoff forecasting.

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An exploration of artificial neural network rainfall-runoff forecasting combined with wavelet decomposition

Journal of Environmental Engineering and Science ◽

10.1139/s03-071 ◽

2004 ◽

Vol 3 (Supplement 1) ◽

pp. S121-S128 ◽

Cited By ~ 94

Author(s):

François Anctil ◽

Doha Guy Tape

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wavelet Decomposition ◽

Rainfall Runoff ◽

Runoff Forecasting ◽

Artificial Neural

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APPLICABILITY OF ARTIFICIAL NEURAL NETWORK MODEL FOR SIMULATION OF MONTHLY RUNOFF IN COMPARISON WITH SOM OTHER TRADITIONAL MODELS

Science and Technology Development Journal ◽

10.32508/stdj.v12i4.2237 ◽

2009 ◽

Vol 12 (4) ◽

pp. 94-106 ◽

Cited By ~ 1

Author(s):

Duc Van Le

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Research Trend ◽

Rainfall Runoff ◽

Ann Model ◽

Back Propagation Algorithm ◽

Runoff Forecasting ◽

Artificial Neural ◽

Artificial Neural Network Ann

Artificial Neural Network (ANN) model along with Back Propagation Algorithm (BPA) has been applied in many fields, especially in hydrology and water resources management to simulate or forecast rainfall runoff process, discharge and water level - time series, and other hydrological variables. Several researches have recently been focusing to compare the applicability of ANN model with other theory-driven and data-driven approaches. The comparison of ANN with M5 model trees for rainfall-runoff forecasting, with ARMAX models for deriving flow series, with AR models and regression models for forecasting and estimating daily river flows have been carried out. The better results that were implemented by ANN model have been concluded. So, this research trend is continued for the comparison of ANN model with Tank, Harmonic, Thomas and Fiering models in simulation of the monthly runoffs at Dong Nai river basin, Viet Nam. The results proved ANN being the best choice among these models, if suitable and enough data sources were available.

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