Harun, Sobri and Shamsuddin, Siti Mariyam and K. K., Kuok (2010) Particle swarm optimization feedforward neural network for modeling runoff. International Journal of Environmental Science and Technology, 7 (1). 67 - 78. ISSN 2227-2763
Official URL: http://www.ijens.org/92010-3838%20IJCEE-IJENS.pdf.
Owing to the complexity of the hydrological process, Backpropagation Neural Network (BPNN) is the single superior model that is able to calibrate the rainfall-runoff relationship accurately using only rainfall and runoff data. However, BPNN convergence rate is relatively slow and being trapped at the local minima. Therefore, a new evolutionary algorithm (EA) namely Particle swarm optimization (PSO) is proposed to train the feedforward neural network. This Particle Swarm Optimization Feedforward Neural Network (PSONN) is applied to model the hourly rainfall-runoff relationship for Bedup Basin. With the input data of current rainfall, antecedent rainfall, antecedent runoff, the optimal configuration of PSONN successfully simulate current runoff accurately with R=0.975 and E2=0.9605 for training data set and R=0.947 and E2=0.9461 for testing data set. Meanwhile, PSONN also proved its ability to predict the runoff accurately at the lead- time of 3, 6, 9 and 12-hour ahead.
|Uncontrolled Keywords:||modeling runoff, Backpropagation neural network (BPNN), Particle swarm optimization feedforward neural network (PSONN), coefficient of correlation (R), Nash-Sutcliffe coefficient (E2)|
|Subjects:||T Technology > TA Engineering (General). Civil engineering (General)|
|Deposited By:||Liza Porijo|
|Deposited On:||18 Jul 2012 01:47|
|Last Modified:||03 Aug 2012 08:02|
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