{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:53:20Z","timestamp":1771264400578,"version":"3.50.1"},"reference-count":53,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T00:00:00Z","timestamp":1622073600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2021,5,27]]},"abstract":"<jats:p>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\u2019 data for 2003\u20132010 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\u2013Marquadt (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\u2019 planners in the developing region which lack multiple data sets for hydrologic software.<\/jats:p>","DOI":"10.1155\/2021\/6683389","type":"journal-article","created":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T19:37:52Z","timestamp":1622230672000},"page":"1-9","source":"Crossref","is-referenced-by-count":36,"title":["Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7052-1942","authenticated-orcid":true,"given":"Miyuru B.","family":"Gunathilake","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Faculty of Engineering, Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri Lanka"},{"name":"Central Engineering Services (Pvt) Limited, Bauddhaloka Mawatha, Colombo 7, Sri Lanka"}]},{"given":"Chamaka","family":"Karunanayake","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Faculty of Engineering, Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri Lanka"}]},{"given":"Anura S.","family":"Gunathilake","sequence":"additional","affiliation":[{"name":"Central Engineering Consultancy Bureau, Bauddahloka Mawatha, Colombo 7, Sri Lanka"}]},{"given":"Niranga","family":"Marasingha","sequence":"additional","affiliation":[{"name":"Central Engineering Services (Pvt) Limited, Bauddhaloka Mawatha, Colombo 7, Sri Lanka"},{"name":"Central Engineering Consultancy Bureau, Bauddahloka Mawatha, Colombo 7, Sri Lanka"}]},{"given":"Jayanga T.","family":"Samarasinghe","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Sri Lanka Technological Campus, Padukka, Sri Lanka"}]},{"given":"Isuru M.","family":"Bandara","sequence":"additional","affiliation":[{"name":"Central Engineering Consultancy Bureau, Bauddahloka Mawatha, Colombo 7, Sri Lanka"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7341-9078","authenticated-orcid":true,"given":"Upaka","family":"Rathnayake","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Faculty of Engineering, Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri Lanka"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/s13201-016-0488-y"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2018.10.038"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2019.134308"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1111\/j.1752-1688.1998.tb05961.x"},{"key":"5","volume-title":"Hydrologic Modeling System HEC-HMS: Technical Reference Manual","author":"A. D. Feldman","year":"2000"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1029\/94jd00483"},{"key":"7","volume-title":"HBV Light, Version 2; User\u2019s Manual","author":"J. Seibert","year":"2005"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/s1474-7065(02)00051-7"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-65114-w"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1007\/s12665-015-5059-2"},{"issue":"4","key":"11","first-page":"7017","article-title":"Estimation of baseflow parameters of variable infiltration capacity model with soil and topography properties for predictions in ungauged basins","volume":"8","author":"Z. Bao","year":"2011","journal-title":"Hydrology and Earth System Sciences Discussions"},{"key":"12","first-page":"1","article-title":"Review-artificial intelligence based modelling of hydrological processes","volume-title":"","author":"J. S. Alagha"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/4820136"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1080\/1943815x.2019.1707233"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1007\/s10661-015-4381-1"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8862067"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8627824"},{"key":"18","article-title":"Relationships between climatic factors to the paddy yeild: a case study from North-Western province of Sri Lanka","author":"I. L. Wickramasinghe"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.2166\/ws.2020.062"},{"issue":"5","key":"20","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1061\/(ASCE)1084-0699(2007)12:5(532)","article-title":"Streamflow forecasting using different artificial neural network algorithms","volume":"12","author":"\u00d6. Ki\u015fi","year":"2007","journal-title":"Journal of Hydrologic Engineering"},{"issue":"7","key":"21","doi-asserted-by":"crossref","first-page":"2191","DOI":"10.1007\/s13762-014-0613-0","article-title":"Successive-station monthly streamflow prediction using different artificial neural network algorithms","volume":"12","author":"A. D. Mehr","year":"2014","journal-title":"International Journal of Environmental Science and Technology"},{"key":"22","first-page":"84","article-title":"Ensemble forecast for monthly reservoir inflow; a dynamic neural network approach","author":"N. Basnayake"},{"key":"23","first-page":"218","article-title":"Application of models with different types of modelling methodologies for river flow forecasting","volume":"282","author":"H. Hapuarachchi","year":"2003","journal-title":"Weather Radar Information and Distributed Hydrological Modelling"},{"key":"24","first-page":"85","article-title":"Comparison of two hydrological model applications for stream flow predictions in the upper Kotmale basin","author":"P. Hunukumbura"},{"key":"25","first-page":"15","volume-title":"\u2018An Artificial Neural Network Model for River Flow Forecasting\u2019, 28th Proceedings of the Technical Sessions","author":"U. Selventhiran","year":"2012"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1016\/s0378-3774(02)00128-2"},{"key":"27","volume-title":"Harmonized World Soil Database (Internet)","author":"FAO","year":"2012"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.5194\/hess-22-5801-2018"},{"key":"29","volume-title":"Hydrologic Modeling System HEC-HMS: Quick Start Guide","author":"M. J. Fleming","year":"2013"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1002\/hyp.9220"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.15406\/ijh.2017.01.00027"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.2166\/nh.2016.133"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)HE.1943-5584.0000846"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2013.03.006"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.4038\/engineer.v52i3.7361"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.4038\/engineer.v43i2.6979"},{"key":"37","doi-asserted-by":"publisher","DOI":"10.4038\/engineer.v48i1.6843"},{"key":"38","doi-asserted-by":"publisher","DOI":"10.3390\/w10050642"},{"key":"39","doi-asserted-by":"publisher","DOI":"10.3390\/hydrology5030044"},{"key":"40","volume-title":"Applied Hydrology","author":"D. V. Maidment","year":"1988"},{"key":"41","doi-asserted-by":"publisher","DOI":"10.3390\/app10041486"},{"key":"42","doi-asserted-by":"publisher","DOI":"10.1007\/s12665-019-8163-x"},{"key":"43","article-title":"Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples","volume":"37","author":"L. Sun","year":"2019","journal-title":"Engineering with Computers"},{"key":"44","doi-asserted-by":"publisher","DOI":"10.3390\/app10051761"},{"key":"45","doi-asserted-by":"publisher","DOI":"10.13031\/2013.23153"},{"key":"46","doi-asserted-by":"publisher","DOI":"10.3390\/rs9100998"},{"key":"47","doi-asserted-by":"publisher","DOI":"10.3390\/w10070876"},{"key":"48","doi-asserted-by":"publisher","DOI":"10.1080\/02626667.2014.959446"},{"key":"49","first-page":"315","article-title":"Comparison of different rainfall-runoff models performance: a case study of Liqvan catchment, Iran","volume":"57","author":"S. N. Loyeh","year":"2017","journal-title":"European Water"},{"key":"50","doi-asserted-by":"publisher","DOI":"10.1080\/02626667.2016.1154149"},{"key":"51","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-1694(03)00225-7"},{"key":"52","doi-asserted-by":"publisher","DOI":"10.3390\/w10111665"},{"key":"53","doi-asserted-by":"publisher","DOI":"10.3390\/w12092400"}],"container-title":["Applied Computational Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2021\/6683389.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2021\/6683389.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2021\/6683389.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T19:38:07Z","timestamp":1622230687000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/acisc\/2021\/6683389\/"}},"subtitle":[],"editor":[{"given":"Jun","family":"He","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,5,27]]},"references-count":53,"alternative-id":["6683389","6683389"],"URL":"https:\/\/doi.org\/10.1155\/2021\/6683389","relation":{},"ISSN":["1687-9732","1687-9724"],"issn-type":[{"value":"1687-9732","type":"electronic"},{"value":"1687-9724","type":"print"}],"subject":[],"published":{"date-parts":[[2021,5,27]]}}}