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Title: Hydrological models and Artificial Neural Networks (ANNs) to simulate streamflow in a tropical catchment of Sri Lanka
Authors: Gunathilake, M. B
Karunanayake, C
Gunathilake, A. S
Samarasinghe, T
Bandara, I. M
Rathnayake, U. S
Keywords: Hydrological Models
Artificial Neural Networks (ANNs)
Simulate Streamflow
Tropical Catchment
Sri Lanka
Issue Date: May-2021
Publisher: 10.1155/2021/6683389
Citation: Miyuru B. Gunathilake, Chamaka Karunanayake, Anura S. Gunathilake, Niranga Marasingha, Jayanga T. Samarasinghe, Isuru M. Bandara, Upaka Rathnayake, "Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka", Applied Computational Intelligence and Soft Computing, vol. 2021, Article ID 6683389, 9 pages, 2021.
Series/Report no.: Applied Computational Intelligence and Soft Computing;Vol 2021 Issue May Pages 1-9
Abstract: Accurate streamflow estimations are essential for planning and decision-making of many development activities related to water resources. Hydrological modelling is a frequently adopted and a matured technique to simulate streamflow compared to the data driven models such as artificial neural networks (ANNs). In addition, usage of ANNs is minimum to simulate streamflow in the context of Sri Lanka. Therefore, this study presents an intercomparison between streamflow estimations from conventional hydrological modelling and ANN analysis for Seethawaka River Basin located in the upstream part of the Kelani River Basin, Sri Lanka. The hydrological model was developed using the Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS), while the data-driven ANN model was developed in MATLAB. The rainfall and streamflows’ data for 2003–2010 period have been used. The simulations by HEC-HMS were performed by four types of input rainfall data configurations, including observed rainfall data sets and three satellite-based precipitation products (SbPPs), namely, PERSIANN, PERSIANN-CCS, and PERSIANN-CDR. The ANN model was trained using three well-known training algorithms, namely, Levenberg–Marquadt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG). Results revealed that the simulated hydrological model based on observed rainfall outperformed those of based on remotely sensed SbPPs. BR algorithm-based ANN algorithm was found to be superior among the data-driven models in the context of ANN model simulations. However, none of the above developed models were able to capture several peak discharges recorded in the Seethawaka River. The results of this study indicate that ANN models can be used to simulate streamflow to an acceptable level, despite presence of intensive spatial and temporal data sets, which are often required for hydrologic software. Hence, the results of the current study provide valuable feedback for water resources’ planners in the developing region which lack multiple data sets for hydrologic software.
URI: http://localhost:80/handle/123456789/859
Appears in Collections:Department of Civil Engineering-Scopes
Research Papers - Department of Civil Engineering
Research Papers - Open Access Research
Research Papers - SLIIT Staff Publications

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