Boosting algorithms for projecting streamflow in the Lower Godavari Basin for different climate change scenarios
The present study investigates the ability of five boosting algorithms, namely Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Light Gradient Boosting (LGBoost), Natural Gradient Boosting (NGBoost), and eXtreme Gradient Boosting (XGBoost) for simulating streamflow in the Lower Godavar...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2024-02-01
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| Series: | Water Science and Technology |
| Subjects: | |
| Online Access: | http://wst.iwaponline.com/content/89/3/613 |
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| author | Bhavesh Rahul Mishra Rishith Kumar Vogeti Rahul Jauhari K. Srinivasa Raju D. Nagesh Kumar |
| author_facet | Bhavesh Rahul Mishra Rishith Kumar Vogeti Rahul Jauhari K. Srinivasa Raju D. Nagesh Kumar |
| author_sort | Bhavesh Rahul Mishra |
| collection | DOAJ |
| description | The present study investigates the ability of five boosting algorithms, namely Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Light Gradient Boosting (LGBoost), Natural Gradient Boosting (NGBoost), and eXtreme Gradient Boosting (XGBoost) for simulating streamflow in the Lower Godavari Basin, India. Monthly rainfall, temperatures, and streamflow from 1982 to 2020 were used for training and testing. Kling Gupta Efficiency (KGE) was deployed to assess the ability of the boosting algorithms. It was observed that all the boosting algorithms had shown good simulating ability, having KGE values of AdaBoost (0.87, 0.85), CatBoost (0.90, 0.78), LGBoost (0.95, 0.93), NGBoost (0.95, 0.95), and XGBoost (0.91, 0.90), respectively, in training and testing. Thus, all the algorithms were used for projecting streamflow in a climate change perspective for the short-term projections (2025–2050) and long-term projections (2051–2075) for four Shared Socioeconomic Pathways (SSPs). The highest streamflow for all four SSPs in the case of NGBoost is more than the historical scenario (9382 m3/s), whereas vice-versa for the remaining four. The effect of ensembling the outputs of five algorithms is also studied and compared with that of individual algorithms.
HIGHLIGHTS
All the boosting algorithms have shown good simulating ability and were used in projecting streamflows for short-term projections (2025–2050) and long-term projections (2051–2075).;
The highest streamflow for all four SSPs in the case of NGBoost is more than the historical scenario (9382 m3/s), whereas vice-versa for the remaining four.;
The effect of ensembling the outputs of five algorithms is also studied and compared with that of individual algorithms.; |
| first_indexed | 2024-03-08T00:26:10Z |
| format | Article |
| id | doaj.art-cb0fd0fc262143b1a26ebf26a89cea5c |
| institution | Directory Open Access Journal |
| issn | 0273-1223 1996-9732 |
| language | English |
| last_indexed | 2024-03-08T00:26:10Z |
| publishDate | 2024-02-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Science and Technology |
| spelling | doaj.art-cb0fd0fc262143b1a26ebf26a89cea5c2024-02-15T16:19:38ZengIWA PublishingWater Science and Technology0273-12231996-97322024-02-0189361363410.2166/wst.2024.011011Boosting algorithms for projecting streamflow in the Lower Godavari Basin for different climate change scenariosBhavesh Rahul Mishra0Rishith Kumar Vogeti1Rahul Jauhari2K. Srinivasa Raju3D. Nagesh Kumar4 Department of Electrical and Electronics Engineering, BITS Pilani Hyderabad Campus, Hyderabad, India Department of Civil Engineering, BITS Pilani Hyderabad Campus, Hyderabad, India Department of Computer Science and Information Systems, BITS Pilani Hyderabad Campus, Hyderabad, India Department of Civil Engineering, BITS Pilani Hyderabad Campus, Hyderabad, India Department of Civil Engineering, Indian Institute of Science, Bangalore, India The present study investigates the ability of five boosting algorithms, namely Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Light Gradient Boosting (LGBoost), Natural Gradient Boosting (NGBoost), and eXtreme Gradient Boosting (XGBoost) for simulating streamflow in the Lower Godavari Basin, India. Monthly rainfall, temperatures, and streamflow from 1982 to 2020 were used for training and testing. Kling Gupta Efficiency (KGE) was deployed to assess the ability of the boosting algorithms. It was observed that all the boosting algorithms had shown good simulating ability, having KGE values of AdaBoost (0.87, 0.85), CatBoost (0.90, 0.78), LGBoost (0.95, 0.93), NGBoost (0.95, 0.95), and XGBoost (0.91, 0.90), respectively, in training and testing. Thus, all the algorithms were used for projecting streamflow in a climate change perspective for the short-term projections (2025–2050) and long-term projections (2051–2075) for four Shared Socioeconomic Pathways (SSPs). The highest streamflow for all four SSPs in the case of NGBoost is more than the historical scenario (9382 m3/s), whereas vice-versa for the remaining four. The effect of ensembling the outputs of five algorithms is also studied and compared with that of individual algorithms. HIGHLIGHTS All the boosting algorithms have shown good simulating ability and were used in projecting streamflows for short-term projections (2025–2050) and long-term projections (2051–2075).; The highest streamflow for all four SSPs in the case of NGBoost is more than the historical scenario (9382 m3/s), whereas vice-versa for the remaining four.; The effect of ensembling the outputs of five algorithms is also studied and compared with that of individual algorithms.;http://wst.iwaponline.com/content/89/3/613boostingkgelower godavari basinsspstreamflow |
| spellingShingle | Bhavesh Rahul Mishra Rishith Kumar Vogeti Rahul Jauhari K. Srinivasa Raju D. Nagesh Kumar Boosting algorithms for projecting streamflow in the Lower Godavari Basin for different climate change scenarios Water Science and Technology boosting kge lower godavari basin ssp streamflow |
| title | Boosting algorithms for projecting streamflow in the Lower Godavari Basin for different climate change scenarios |
| title_full | Boosting algorithms for projecting streamflow in the Lower Godavari Basin for different climate change scenarios |
| title_fullStr | Boosting algorithms for projecting streamflow in the Lower Godavari Basin for different climate change scenarios |
| title_full_unstemmed | Boosting algorithms for projecting streamflow in the Lower Godavari Basin for different climate change scenarios |
| title_short | Boosting algorithms for projecting streamflow in the Lower Godavari Basin for different climate change scenarios |
| title_sort | boosting algorithms for projecting streamflow in the lower godavari basin for different climate change scenarios |
| topic | boosting kge lower godavari basin ssp streamflow |
| url | http://wst.iwaponline.com/content/89/3/613 |
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