Rainfall-Runoff Process Predicting Using the Hybrid Model of Particle Swarm Optimization-Wavelet Support Vector Machine (Case study: Silakhor Plain)
2023
Komasi, Mahdi | Sharghi, soroush
Rainfall-runoff modeling and predicting play an essential role in water resource managing, urban planning, reservoir operating, etc. Support vector machine (SVM), as one of the new models of artificial intelligence, has high capability and flexibility in predicting hydrological data. In this research, the idea of rainfall-runoff process modeling using the hybrid model of Particle Swarm Optimization-Wavelet Transform-Support Vector Machine (PSO-WT-SVM) is proposed. There are constant parameters in the SVM algorithm that should be appropriately determined by the user, whereas a wrong choice of these parameters results in a significant reduction in the model performance. In order to solve this problem, the Particle Swarm Optimization (PSO) algorithm is employed to find the best values of SVM constant parameters introducing the PSO-SVM hybrid model. In the next step, applying the Wavelet Transform (WT) pre-processing method on the raw data, this research aims at proposing PSO-WT-SVM hybrid model. Finally, the daily rainfall-runoff time series of the Silakhor plain located in Lorastan province are modeled and forecasted using the SVM single model, PSO-SVM, and PSO-WT-SVM hybrid models. The models' accuracy is assessed using DC and RMSE criteria. The results indicate that PSO-SVM and PSO-WT-SVM hybrid models with DC of 0.72 and 0.89, respectively, supersede the SVM single model with DC of 0.57 in the verification step for Silakhor plain rainfall-runoff time series modeling.
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