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Machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios
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Yeganeh-Bakhtiary, Abbas, Eyvaz Oghli, Hossein, Shabakhty, Naser, Kamranzad, Bahareh and Abolfathi, Soroush (2022) Machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios. Complexity, 2022 . 8451812. doi:10.1155/2022/8451812 ISSN 1076-2787. [ 🗎 Public]. [ (✓) hoa:511 ]
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WRAP-Machine-learning-downscaling-approach-prediction-wind-characteristics-future-climate-change-scenarios-22.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (3521Kb) | Preview |
Official URL: https://doi.org/10.1155/2022/8451812
Abstract
Assessment of climate change impacts on wind characteristics is crucial for the design, operation, and maintenance of coastal and offshore infrastructures. In the present study, the Model Output Statistics (MOS) method was used to downscale a Coupled Model Intercomparison Project Phase 5 (CMIP5) with General Circulation Model (GCM) results for a case study in the North Atlantic Ocean, and a supervised machine learning method (M5’ Decision Tree model) was developed for the first time to establish a statistical relationship between predicator and predicant. To do so, the GCM simulation results and altimeter remote sensing data were employed to examine the capabilities of the M5’DT model in predicting future wind speed and identifying spatiotemporal trends in wind characteristics. For this purpose, three classes of M5′ models were developed to study the annual, seasonal, and monthly variations of wind characteristics. The developed decision tree (DT) models were employed to statistically downscale the Beijing Normal University Earth System Model (BNU-ESM) global climate model output. The M5′ models are calibrated and successfully validated against the GCM simulation results and altimeter remote sensing data. All the proposed models showed firm outputs in the training section. Predictions from the monthly model with a 70/30 training to test ratio demonstrated the best model performance. The monthly prediction model highlighted the decreasing trend in wind speed relative to the control period in 2030 to 2040 for the case study location and across all three future climate change scenarios tested within this study. This reduction in wind speed reduces wind energy by 13% to 19%.
| Item Type: | Journal Article | ||||||
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| Subjects: | G Geography. Anthropology. Recreation > GC Oceanography T Technology > TA Engineering (General). Civil engineering (General) T Technology > TC Hydraulic engineering. Ocean engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| Library of Congress Subject Headings (LCSH): | Winds, Winds -- Speed, Climatic changes -- Mathematical models, Weathering -- Mathematical models, Machine learning | ||||||
| Journal or Publication Title: | Complexity | ||||||
| Publisher: | Wiley-Blackwell Publishing, Inc | ||||||
| ISSN: | 1076-2787 | ||||||
| Official Date: | 23 August 2022 | ||||||
| Dates: |
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| Volume: | 2022 | ||||||
| Article Number: | 8451812 | ||||||
| DOI: | 10.1155/2022/8451812 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 23 August 2022 | ||||||
| Date of first compliant Open Access: | 23 August 2022 | ||||||
| Open Access Version: |
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