Evaluation of machine learning tools as a statistical downscaling tool : temperatures projections for multi-stations for Thames River Basin, Canada
Many impact studies require climate change information at a finer resolution than that provided by global climate models (GCMs). This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely single conjunctive rule learner, decision table, M5 model...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/97526 http://hdl.handle.net/10220/11866 |
| _version_ | 1811678190575288320 |
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| author | Burn, Donald H. Ojha, C. S. P. Goyal, Manish Kumar |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Burn, Donald H. Ojha, C. S. P. Goyal, Manish Kumar |
| author_sort | Burn, Donald H. |
| collection | NTU |
| description | Many impact studies require climate change information at a finer resolution than that provided by global climate models (GCMs). This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely single conjunctive rule learner, decision table, M5 model tree, and REPTree, and explores the impact of climate change on maximum and minimum temperatures (i.e., predictands) of 14 meteorological stations in the Upper Thames River Basin, Ontario, Canada. The data used for evaluation were large-scale predictor variables, extracted from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset and the simulations from third generation Canadian coupled global climate model. Data for four grid points covering the study region were used for developing the downscaling model. M5 model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. Hence, this technique was applied to project predictands generated from GCM using three scenarios (A1B, A2, and B1) for the periods (2046–2065 and 2081–2100). A simple multiplicative shift was used for correcting predictand values. The potential of the downscaling models in simulating predictands was evaluated, and downscaling results reveal that the proposed downscaling model can reproduce local daily predictands from large-scale weather variables. Trend of projected maximum and minimum temperatures was studied for historical as well as downscaled values using GCM and scenario uncertainty. There is likely an increasing trend for T max and T min for A1B, A2, and B1 scenarios while decreasing trend has been observed for B1 scenarios during 2081–2100. |
| first_indexed | 2024-10-01T02:49:19Z |
| format | Journal Article |
| id | ntu-10356/97526 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:49:19Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/975262020-03-07T11:43:44Z Evaluation of machine learning tools as a statistical downscaling tool : temperatures projections for multi-stations for Thames River Basin, Canada Burn, Donald H. Ojha, C. S. P. Goyal, Manish Kumar School of Civil and Environmental Engineering Many impact studies require climate change information at a finer resolution than that provided by global climate models (GCMs). This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely single conjunctive rule learner, decision table, M5 model tree, and REPTree, and explores the impact of climate change on maximum and minimum temperatures (i.e., predictands) of 14 meteorological stations in the Upper Thames River Basin, Ontario, Canada. The data used for evaluation were large-scale predictor variables, extracted from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset and the simulations from third generation Canadian coupled global climate model. Data for four grid points covering the study region were used for developing the downscaling model. M5 model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. Hence, this technique was applied to project predictands generated from GCM using three scenarios (A1B, A2, and B1) for the periods (2046–2065 and 2081–2100). A simple multiplicative shift was used for correcting predictand values. The potential of the downscaling models in simulating predictands was evaluated, and downscaling results reveal that the proposed downscaling model can reproduce local daily predictands from large-scale weather variables. Trend of projected maximum and minimum temperatures was studied for historical as well as downscaled values using GCM and scenario uncertainty. There is likely an increasing trend for T max and T min for A1B, A2, and B1 scenarios while decreasing trend has been observed for B1 scenarios during 2081–2100. 2013-07-18T04:42:18Z 2019-12-06T19:43:34Z 2013-07-18T04:42:18Z 2019-12-06T19:43:34Z 2011 2011 Journal Article Goyal, M. K., Burn, D. H., & Ojha, C. S. P. (2012). Evaluation of machine learning tools as a statistical downscaling tool: temperatures projections for multi-stations for Thames River Basin, Canada. Theoretical and Applied Climatology, 108(3-4), 519-534. 0177-798X https://hdl.handle.net/10356/97526 http://hdl.handle.net/10220/11866 10.1007/s00704-011-0546-1 en Theoretical and applied climatology © 2011 Springer-Verlag. |
| spellingShingle | Burn, Donald H. Ojha, C. S. P. Goyal, Manish Kumar Evaluation of machine learning tools as a statistical downscaling tool : temperatures projections for multi-stations for Thames River Basin, Canada |
| title | Evaluation of machine learning tools as a statistical downscaling tool : temperatures projections for multi-stations for Thames River Basin, Canada |
| title_full | Evaluation of machine learning tools as a statistical downscaling tool : temperatures projections for multi-stations for Thames River Basin, Canada |
| title_fullStr | Evaluation of machine learning tools as a statistical downscaling tool : temperatures projections for multi-stations for Thames River Basin, Canada |
| title_full_unstemmed | Evaluation of machine learning tools as a statistical downscaling tool : temperatures projections for multi-stations for Thames River Basin, Canada |
| title_short | Evaluation of machine learning tools as a statistical downscaling tool : temperatures projections for multi-stations for Thames River Basin, Canada |
| title_sort | evaluation of machine learning tools as a statistical downscaling tool temperatures projections for multi stations for thames river basin canada |
| url | https://hdl.handle.net/10356/97526 http://hdl.handle.net/10220/11866 |
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