Application of Machine Learning to Attribution and Prediction of Seasonal Precipitation and Temperature Trends in Canberra, Australia
Southeast Australia is frequently impacted by drought, requiring monitoring of how the various factors influencing drought change over time. Precipitation and temperature trends were analysed for Canberra, Australia, revealing decreasing autumn precipitation. However, annual precipitation remains st...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Climate |
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| Online Access: | https://www.mdpi.com/2225-1154/8/6/76 |
| _version_ | 1797565525689106432 |
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| author | Joshua Hartigan Shev MacNamara Lance M. Leslie |
| author_facet | Joshua Hartigan Shev MacNamara Lance M. Leslie |
| author_sort | Joshua Hartigan |
| collection | DOAJ |
| description | Southeast Australia is frequently impacted by drought, requiring monitoring of how the various factors influencing drought change over time. Precipitation and temperature trends were analysed for Canberra, Australia, revealing decreasing autumn precipitation. However, annual precipitation remains stable as summer precipitation increased and the other seasons show no trend. Further, mean temperature increases in all seasons. These results suggest that Canberra is increasingly vulnerable to drought. Wavelet analysis suggests that the El-Niño Southern Oscillation (ENSO) influences precipitation and temperature in Canberra, although its impact on precipitation has decreased since the 2000s. Linear regression (LR) and support vector regression (SVR) were applied to attribute climate drivers of annual precipitation and mean maximum temperature (TMax). Important attributes of precipitation include ENSO, the southern annular mode (SAM), Indian Ocean Dipole (DMI) and Tasman Sea SST anomalies. Drivers of TMax included DMI and global warming attributes. The SVR models achieved high correlations of 0.737 and 0.531 on prediction of precipitation and TMax, respectively, outperforming the LR models which obtained correlations of 0.516 and 0.415 for prediction of precipitation and TMax on the testing data. This highlights the importance of continued research utilising machine learning methods for prediction of atmospheric variables and weather pattens on multiple time scales. |
| first_indexed | 2024-03-10T19:14:26Z |
| format | Article |
| id | doaj.art-cf10e7cb06e64ecd9d5d97de8b2c8e5e |
| institution | Directory Open Access Journal |
| issn | 2225-1154 |
| language | English |
| last_indexed | 2024-03-10T19:14:26Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Climate |
| spelling | doaj.art-cf10e7cb06e64ecd9d5d97de8b2c8e5e2023-11-20T03:34:10ZengMDPI AGClimate2225-11542020-06-01867610.3390/cli8060076Application of Machine Learning to Attribution and Prediction of Seasonal Precipitation and Temperature Trends in Canberra, AustraliaJoshua Hartigan0Shev MacNamara1Lance M. Leslie2School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, New South Wales 2007, AustraliaSchool of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, New South Wales 2007, AustraliaSchool of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, New South Wales 2007, AustraliaSoutheast Australia is frequently impacted by drought, requiring monitoring of how the various factors influencing drought change over time. Precipitation and temperature trends were analysed for Canberra, Australia, revealing decreasing autumn precipitation. However, annual precipitation remains stable as summer precipitation increased and the other seasons show no trend. Further, mean temperature increases in all seasons. These results suggest that Canberra is increasingly vulnerable to drought. Wavelet analysis suggests that the El-Niño Southern Oscillation (ENSO) influences precipitation and temperature in Canberra, although its impact on precipitation has decreased since the 2000s. Linear regression (LR) and support vector regression (SVR) were applied to attribute climate drivers of annual precipitation and mean maximum temperature (TMax). Important attributes of precipitation include ENSO, the southern annular mode (SAM), Indian Ocean Dipole (DMI) and Tasman Sea SST anomalies. Drivers of TMax included DMI and global warming attributes. The SVR models achieved high correlations of 0.737 and 0.531 on prediction of precipitation and TMax, respectively, outperforming the LR models which obtained correlations of 0.516 and 0.415 for prediction of precipitation and TMax on the testing data. This highlights the importance of continued research utilising machine learning methods for prediction of atmospheric variables and weather pattens on multiple time scales.https://www.mdpi.com/2225-1154/8/6/76machine learningprecipitationtemperaturesoutheast Australiaattributionprediction |
| spellingShingle | Joshua Hartigan Shev MacNamara Lance M. Leslie Application of Machine Learning to Attribution and Prediction of Seasonal Precipitation and Temperature Trends in Canberra, Australia Climate machine learning precipitation temperature southeast Australia attribution prediction |
| title | Application of Machine Learning to Attribution and Prediction of Seasonal Precipitation and Temperature Trends in Canberra, Australia |
| title_full | Application of Machine Learning to Attribution and Prediction of Seasonal Precipitation and Temperature Trends in Canberra, Australia |
| title_fullStr | Application of Machine Learning to Attribution and Prediction of Seasonal Precipitation and Temperature Trends in Canberra, Australia |
| title_full_unstemmed | Application of Machine Learning to Attribution and Prediction of Seasonal Precipitation and Temperature Trends in Canberra, Australia |
| title_short | Application of Machine Learning to Attribution and Prediction of Seasonal Precipitation and Temperature Trends in Canberra, Australia |
| title_sort | application of machine learning to attribution and prediction of seasonal precipitation and temperature trends in canberra australia |
| topic | machine learning precipitation temperature southeast Australia attribution prediction |
| url | https://www.mdpi.com/2225-1154/8/6/76 |
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