Research Article Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka Miyuru B. Gunathilake , 1,2 Chamaka Karunanayake, 1 Anura S. Gunathilake, 3 Niranga Marasingha, 2,3 Jayanga T. Samarasinghe, 4 Isuru M. Bandara, 3 and Upaka Rathnayake 1 1 Department of Civil Engineering, Faculty of Engineering, Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri Lanka 2 Central Engineering Services (Pvt) Limited, Bauddhaloka Mawatha, Colombo 7, Sri Lanka 3 Central Engineering Consultancy Bureau, Bauddahloka Mawatha, Colombo 7, Sri Lanka 4 Faculty of Engineering, Sri Lanka Technological Campus, Padukka, Sri Lanka Correspondence should be addressed to Miyuru B. Gunathilake; miyurubandaragunathilake@gmail.com Received 16 December 2020; Revised 28 April 2021; Accepted 22 May 2021; Published 28 May 2021 Academic Editor: Jun He Copyright©2021MiyuruB.Gunathilakeetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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. erefore, 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. e hydrological model was developed using the Hydrologic Engineering Centre-Hydrologic Modelling System (HEC- HMS), while the data-driven ANN model was developed in MATLAB. e rainfall and streamflows’ data for 2003–2010 period have been used. e 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. e 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. e 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. 1. Introduction Streamflow is one of the responses of integrated atmospheric and topographic processes. Developing the flow hydrograph using observed streamflow measurements is an important task. Many methods (velocity-area methods, formed con- striction methods, and noncontact measurement methods) are available to measure streamflow rates [1]. However, continuous streamflow measurements are not always available in developing nations, mainly due to associated costs for installment and maintenance of hydrological networks [2]. In addition, fine resolution spatial data sets including land use and soil data are not always available in these regions. erefore, computational models to estimate Hindawi Applied Computational Intelligence and So Computing Volume 2021, Article ID 6683389, 9 pages https://doi.org/10.1155/2021/6683389