%0 Journal Article %1 rahseparpredicting %A Rahsepar, Mahmood %D 2014 %J Civil Engineering and Urban Planning: An International Journal (CiVEJ) %K architecture planning %N 01 %P 01-13 %T PREDICTING WEEKLY DISCHARGE USING ARTIFICIAL NEURAL NETWORK (ANN) OPTIMIZED BY ARTIFICIAL BEE COLONY (ABC) ALGORITHM: A CASE STUDY %U https://airccse.com/civej/papers/1114civej01.pdf %V 01 %X Water surface management in the field if hydrology has been considered as an important issue due to many recently drought years specially in warm and dry regions like south of iran. This paper is conducted to propose ANN-ABC mixture algorithm in order to forecast the future discharge of Tang-e Karzin hydrometric station located in sub domain of salman farsi dam. A Feed Forward Neural Network (FFNN) was utilized to forecast the future discharge of a case study station using the 36 past years discharge information. Moreover, Artificial Bee Colony (ABC) optimization algorithm was applied within the training phase of the ANN network to optimize the weights of the MLP network. Through substitution of parameters of hidden layer (kind of activation function and number of neurons) of the neural network, best combination of parameters found in the first phase of the algorithm. Next in the second phase, artificial bee colony algorithm was used to find the global solution of the objective function of the problem. Simulation results indicated that ABC algorithm could significantly improve the neural network results.

@article{rahseparpredicting, abstract = {Water surface management in the field if hydrology has been considered as an important issue due to many recently drought years specially in warm and dry regions like south of iran. This paper is conducted to propose ANN-ABC mixture algorithm in order to forecast the future discharge of Tang-e Karzin hydrometric station located in sub domain of salman farsi dam. A Feed Forward Neural Network (FFNN) was utilized to forecast the future discharge of a case study station using the 36 past years discharge information. Moreover, Artificial Bee Colony (ABC) optimization algorithm was applied within the training phase of the ANN network to optimize the weights of the MLP network. Through substitution of parameters of hidden layer (kind of activation function and number of neurons) of the neural network, best combination of parameters found in the first phase of the algorithm. Next in the second phase, artificial bee colony algorithm was used to find the global solution of the objective function of the problem. Simulation results indicated that ABC algorithm could significantly improve the neural network results.}, added-at = {2019-06-06T07:40:52.000+0200}, author = {Rahsepar, Mahmood}, biburl = {https://www.bibsonomy.org/bibtex/2173a82f934c444656632be5d862f216a/civej}, interhash = {16794eec89a2da4ab33223498cf695f0}, intrahash = {173a82f934c444656632be5d862f216a}, issn = {2394-5818}, journal = {Civil Engineering and Urban Planning: An International Journal (CiVEJ)}, keywords = {architecture planning}, month = {June}, number = 01, pages = {01-13}, timestamp = {2021-02-11T12:34:06.000+0100}, title = {PREDICTING WEEKLY DISCHARGE USING ARTIFICIAL NEURAL NETWORK (ANN) OPTIMIZED BY ARTIFICIAL BEE COLONY (ABC) ALGORITHM: A CASE STUDY}, url = {https://airccse.com/civej/papers/1114civej01.pdf}, volume = 01, year = 2014 }