Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios
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) wit...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/8451812 |
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| author | Abbas Yeganeh-Bakhtiary Hossein EyvazOghli Naser Shabakhty Bahareh Kamranzad Soroush Abolfathi |
| author_facet | Abbas Yeganeh-Bakhtiary Hossein EyvazOghli Naser Shabakhty Bahareh Kamranzad Soroush Abolfathi |
| author_sort | Abbas Yeganeh-Bakhtiary |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-672bbc18af54459a9eec6586ab242374 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-672bbc18af54459a9eec6586ab2423742025-02-03T05:57:24ZengWileyComplexity1099-05262022-01-01202210.1155/2022/8451812Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change ScenariosAbbas Yeganeh-Bakhtiary0Hossein EyvazOghli1Naser Shabakhty2Bahareh Kamranzad3Soroush Abolfathi4School of Civil EngineeringSchool of Civil EngineeringSchool of Civil EngineeringHakubi Center for Advanced ResearchSchool of EngineeringAssessment 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%.http://dx.doi.org/10.1155/2022/8451812 |
| spellingShingle | Abbas Yeganeh-Bakhtiary Hossein EyvazOghli Naser Shabakhty Bahareh Kamranzad Soroush Abolfathi Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios Complexity |
| title | Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios |
| title_full | Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios |
| title_fullStr | Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios |
| title_full_unstemmed | Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios |
| title_short | Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios |
| title_sort | machine learning as a downscaling approach for prediction of wind characteristics under future climate change scenarios |
| url | http://dx.doi.org/10.1155/2022/8451812 |
| work_keys_str_mv | AT abbasyeganehbakhtiary machinelearningasadownscalingapproachforpredictionofwindcharacteristicsunderfutureclimatechangescenarios AT hosseineyvazoghli machinelearningasadownscalingapproachforpredictionofwindcharacteristicsunderfutureclimatechangescenarios AT nasershabakhty machinelearningasadownscalingapproachforpredictionofwindcharacteristicsunderfutureclimatechangescenarios AT baharehkamranzad machinelearningasadownscalingapproachforpredictionofwindcharacteristicsunderfutureclimatechangescenarios AT soroushabolfathi machinelearningasadownscalingapproachforpredictionofwindcharacteristicsunderfutureclimatechangescenarios |