Statistical Learning-Based Spatial Downscaling Models for Precipitation Distribution
The downscaling technique produces high spatial resolution precipitation distribution in order to analyze impacts of climate change in data-scarce regions or local scales. In this study, based on three statistical learning algorithms, such as support vector machine (SVM), random forest regression (R...
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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: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2022/3140872 |
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| author | Yichen Wu Zhihua Zhang M. James C. Crabbe Lipon Chandra Das |
| author_facet | Yichen Wu Zhihua Zhang M. James C. Crabbe Lipon Chandra Das |
| author_sort | Yichen Wu |
| collection | DOAJ |
| description | The downscaling technique produces high spatial resolution precipitation distribution in order to analyze impacts of climate change in data-scarce regions or local scales. In this study, based on three statistical learning algorithms, such as support vector machine (SVM), random forest regression (RF), and gradient boosting regressor (GBR), we proposed an efficient downscaling approach to produce high spatial resolution precipitation. In order to demonstrate efficiency and accuracy of our models over traditional multilinear regression (MLR) downscaling models, we did a downscaling analysis for daily observed precipitation data from 34 monitoring sites in Bangladesh. Validation revealed that R2 of GBR could reach 0.98, compared with RF (0.94), SVM (0.88), and multilinear regression (MLR) (0.69) models, so the GBR-based downscaling model had the best performance among all four downscaling models. We suggest that the GBR-based downscaling models should be used to replace traditional MLR downscaling models to produce a more accurate map of high-resolution precipitation for flood disaster management, drought forecasting, and long-term planning of land and water resources. |
| format | Article |
| id | doaj-art-3325c017d7e8461fb3cf7f731c1010c7 |
| institution | OA Journals |
| issn | 1687-9317 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-3325c017d7e8461fb3cf7f731c1010c72025-08-20T02:24:21ZengWileyAdvances in Meteorology1687-93172022-01-01202210.1155/2022/3140872Statistical Learning-Based Spatial Downscaling Models for Precipitation DistributionYichen Wu0Zhihua Zhang1M. James C. Crabbe2Lipon Chandra Das3Climate Modeling LaboratoryClimate Modeling LaboratoryWolfson CollegeClimate Modeling LaboratoryThe downscaling technique produces high spatial resolution precipitation distribution in order to analyze impacts of climate change in data-scarce regions or local scales. In this study, based on three statistical learning algorithms, such as support vector machine (SVM), random forest regression (RF), and gradient boosting regressor (GBR), we proposed an efficient downscaling approach to produce high spatial resolution precipitation. In order to demonstrate efficiency and accuracy of our models over traditional multilinear regression (MLR) downscaling models, we did a downscaling analysis for daily observed precipitation data from 34 monitoring sites in Bangladesh. Validation revealed that R2 of GBR could reach 0.98, compared with RF (0.94), SVM (0.88), and multilinear regression (MLR) (0.69) models, so the GBR-based downscaling model had the best performance among all four downscaling models. We suggest that the GBR-based downscaling models should be used to replace traditional MLR downscaling models to produce a more accurate map of high-resolution precipitation for flood disaster management, drought forecasting, and long-term planning of land and water resources.http://dx.doi.org/10.1155/2022/3140872 |
| spellingShingle | Yichen Wu Zhihua Zhang M. James C. Crabbe Lipon Chandra Das Statistical Learning-Based Spatial Downscaling Models for Precipitation Distribution Advances in Meteorology |
| title | Statistical Learning-Based Spatial Downscaling Models for Precipitation Distribution |
| title_full | Statistical Learning-Based Spatial Downscaling Models for Precipitation Distribution |
| title_fullStr | Statistical Learning-Based Spatial Downscaling Models for Precipitation Distribution |
| title_full_unstemmed | Statistical Learning-Based Spatial Downscaling Models for Precipitation Distribution |
| title_short | Statistical Learning-Based Spatial Downscaling Models for Precipitation Distribution |
| title_sort | statistical learning based spatial downscaling models for precipitation distribution |
| url | http://dx.doi.org/10.1155/2022/3140872 |
| work_keys_str_mv | AT yichenwu statisticallearningbasedspatialdownscalingmodelsforprecipitationdistribution AT zhihuazhang statisticallearningbasedspatialdownscalingmodelsforprecipitationdistribution AT mjamesccrabbe statisticallearningbasedspatialdownscalingmodelsforprecipitationdistribution AT liponchandradas statisticallearningbasedspatialdownscalingmodelsforprecipitationdistribution |