Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka
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 network...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2021/6683389 |
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| author | Miyuru B. Gunathilake Chamaka Karunanayake Anura S. Gunathilake Niranga Marasingha Jayanga T. Samarasinghe Isuru M. Bandara Upaka Rathnayake |
| author_facet | Miyuru B. Gunathilake Chamaka Karunanayake Anura S. Gunathilake Niranga Marasingha Jayanga T. Samarasinghe Isuru M. Bandara Upaka Rathnayake |
| author_sort | Miyuru B. Gunathilake |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a019a9cdc2694a2b849163511d60efe5 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-a019a9cdc2694a2b849163511d60efe52025-02-03T06:47:01ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322021-01-01202110.1155/2021/66833896683389Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri LankaMiyuru B. Gunathilake0Chamaka Karunanayake1Anura S. Gunathilake2Niranga Marasingha3Jayanga T. Samarasinghe4Isuru M. Bandara5Upaka Rathnayake6Department of Civil Engineering, Faculty of Engineering, Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri LankaDepartment of Civil Engineering, Faculty of Engineering, Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri LankaCentral Engineering Consultancy Bureau, Bauddahloka Mawatha, Colombo 7, Sri LankaCentral Engineering Services (Pvt) Limited, Bauddhaloka Mawatha, Colombo 7, Sri LankaFaculty of Engineering, Sri Lanka Technological Campus, Padukka, Sri LankaCentral Engineering Consultancy Bureau, Bauddahloka Mawatha, Colombo 7, Sri LankaDepartment of Civil Engineering, Faculty of Engineering, Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri LankaAccurate 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.http://dx.doi.org/10.1155/2021/6683389 |
| spellingShingle | 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 |
| title | Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka |
| title_full | Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka |
| title_fullStr | Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka |
| title_full_unstemmed | Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka |
| title_short | Hydrological Models and Artificial Neural Networks (ANNs) to Simulate Streamflow in a Tropical Catchment of Sri Lanka |
| title_sort | hydrological models and artificial neural networks anns to simulate streamflow in a tropical catchment of sri lanka |
| url | http://dx.doi.org/10.1155/2021/6683389 |
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