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dc.contributor.authorKarunanayake, C-
dc.contributor.authorGunathilake, M. B-
dc.contributor.authorRathnayake, U. S-
dc.date.accessioned2022-01-31T06:13:15Z-
dc.date.available2022-01-31T06:13:15Z-
dc.date.issued2020-11-
dc.identifier.citationChamaka Karunanayake, Miyuru B. Gunathilake, Upaka Rathnayake, "Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks", Applied Computational Intelligence and Soft Computing, vol. 2020, Article ID 8821627, 11 pages, 2020. https://doi.org/10.1155/2020/8821627en_US
dc.identifier.issn1687-9724-
dc.identifier.urihttp://localhost:80/handle/123456789/856-
dc.description.abstractPrediction of water resources for future years takes much attention from the water resources planners and relevant authorities. However, traditional computational models like hydrologic models need many data about the catchment itself. Sometimes these important data on catchments are not available due to many reasons. Therefore, artificial neural networks (ANNs) are useful soft computing tools in predicting real-world scenarios, such as forecasting future water availability from a catchment, in the absence of intensive data, which are required for modeling practices in the context of hydrology. These ANNs are capable of building relationships to nonlinear real-world problems using available data and then to use that built relationship to forecast future needs. Even though Sri Lanka has an extensive usage of water resources for many activities, including drinking water supply, irrigation, hydropower development, navigation, and many other recreational purposes, forecasting studies for water resources are not being carried out. Therefore, there is a significant gap in forecasting water availability and water needs in the context of Sri Lanka. Thus, this paper presents an artificial neural network model to forecast the inflows of one of the most important reservoirs in northern Sri Lanka using the upstream catchment’s rainfall. Future rainfall data are extracted using regional climate models for the years 2021–2050 and the inflows of the reservoir are forecasted using the validated neural network model. Several training algorithms including Levenberg–Marquardt (LM), BFGS quasi-Newton (BFG), scaled conjugate gradient (SCG) have been used to find the best fitting training algorithm to the prediction process of the inflows against the measured inflows. Results revealed that the LM training algorithm outperforms the other tests algorithm in developing the prediction model. In addition, the forecasted results using the projected climate scenarios clearly showcase the benefit of using the forecasting model in solving future water resource management to avoid or to minimize future water scarcity. Therefore, the validated model can effectively be used for proper planning of the proposed drinking water supply scheme to the nearby urban city, Jaffna in northern Sri Lanka.en_US
dc.language.isoenen_US
dc.publisherHindawi 10.1155/2020/8821627en_US
dc.relation.ispartofseriesApplied Computational Intelligence and Soft Computing;Vol 2020 Issue Nov-
dc.subjectInflow Forecasten_US
dc.subjectIranamadu Reservoiren_US
dc.subjectSri Lankaen_US
dc.subjectProjected Climate Scenariosen_US
dc.subjectArtificial Neural Networksen_US
dc.titleInflow forecast of Iranamadu reservoir, Sri Lanka under projected climate scenarios using artificial neural networksen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1155/2020/8821627en_US
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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