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Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks
Joint Authors
Karunanayake, Chamaka
Gunathilake, Miyuru B.
Rathnayake, Upaka
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-07
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
Prediction 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.
American Psychological Association (APA)
Karunanayake, Chamaka& Gunathilake, Miyuru B.& Rathnayake, Upaka. 2020. Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks. Applied Computational Intelligence and Soft Computing،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1126034
Modern Language Association (MLA)
Karunanayake, Chamaka…[et al.]. Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks. Applied Computational Intelligence and Soft Computing No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1126034
American Medical Association (AMA)
Karunanayake, Chamaka& Gunathilake, Miyuru B.& Rathnayake, Upaka. Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks. Applied Computational Intelligence and Soft Computing. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1126034
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126034