Development of a PSO-ANN Model for Rainfall-Runoff Response in Basins, Case Study: Karaj Basin
Publish place: Civil Engineering Journal، Vol: 3، Issue: 1
Publish Year: 1395
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_CEJ-3-1_003
تاریخ نمایه سازی: 6 شهریور 1396
Abstract:
Successful daily river flow forecasting is necessary in water resources planning and management. A reliable rainfall-runoff model can provide useful information for water resources planning and management. In this study, particle swarm optimization algorithm (PSO) as a metaheuristic approach is employed to train artificial neural network (ANN). The proposed PSO-ANN model is applied to simulate the rainfall runoff process in Karaj River for one and two days ahead. In this regard, different combinations of the input variables including flow and rainfall time series in previous days have been taken under consideration in order to obtain the best model s performances. To evaluate efficiency of the PSO algorithm in training ANNs, separate ANN models are developed using Levenberg-Marquardt (LM) training algorithm and the results are compared with those of the PSO-ANN models. The comparison reveals superiority of the PSO algorithm than the LM algorithm in training the ANN models. The best model for 1 and 2 days ahead runoff forecasting has R2 of 0.88 and 0.78. Results of this study shows that a reliable prediction of runoff in 1 and 2 days ahead can be achieved using PSO-ANN model. Overall, results of this study revealed that an acceptable prediction of the runoff up to two days ahead can be achieved by applying the PSO-ANN model
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Authors
Meysam Motahari
MSc, Department of Water Engineering, Imam Khomeini International University, Qazvin, Iran.
Hamed Mazandaranizadeh
Assistant Professor, Department of Water Engineering, Imam Khomeini International University, Qazvin, Iran.