Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India
This research aims to forecast maximum temperatures and the frequency of heatwave days across four different temperature zones (Zone 1, 2, 3 and 4) in India. These four zones are categorized based on the 30-year average maximum temperatures (T30AMT) during the summer months of April, May, and June (...
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Elsevier
2024-12-01
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| Series: | Applied Computing and Geosciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S259019742400051X |
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| author | Srikanth Bhoopathi Nitish Kumar Somesh Manali Pal |
| author_facet | Srikanth Bhoopathi Nitish Kumar Somesh Manali Pal |
| author_sort | Srikanth Bhoopathi |
| collection | DOAJ |
| description | This research aims to forecast maximum temperatures and the frequency of heatwave days across four different temperature zones (Zone 1, 2, 3 and 4) in India. These four zones are categorized based on the 30-year average maximum temperatures (T30AMT) during the summer months of April, May, and June (AMJ). Two Machine Learning (ML) algorithms eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) are employed to achieve this goal. The study utilizes nine key atmospheric variables namely air temperature, geopotential height, relative humidity, U-wind, V-wind, soil moisture, solar radiation, sea surface temperature, and mean sea level pressure at a daily scale spanning from 1991 to 2020 for the months of March, April, May, and June as predictors. The India Meteorological Department daily maximum temperature data spanning from 1991 to 2020 for the months of AMJ serves as the predictands. ML models are developed using spatially averaged atmospheric variables and daily maximum temperature across the grids falling within each temperature zone. Results indicate that for a 7-day lead time, SVR outperforms XGBoost in Zone-1 (T30AMT > 38 °C) and Zone-2 (T30AMT: 35.01 °C–38 °C) by more accurately capturing peak temperatures during training and testing. Conversely, for a 15-day lead time in Zone-1, XGBoost better predicts temperature peaks in both phases. In Zone-3 (T30AMT: 30 °C–35 °C) and Zone-4 (T30AMT < 30 °C) for both lead times, the performance of both models decline, indicating models and input variables are more effective in predicting higher temperatures typical of Zone-1 and 2 but less so in Zone-3 and 4. In a nutshell, the study attempts to highlight the capability of advanced ML techniques combined with spatial climate data to enhance the prediction of extreme heatwave events. These insights can aid in heatwave preparedness, climate management, and adaptation strategies for different Indian temperature zones. |
| format | Article |
| id | doaj-art-e82053a262014dffbb0a4e577cc028bb |
| institution | OA Journals |
| issn | 2590-1974 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Computing and Geosciences |
| spelling | doaj-art-e82053a262014dffbb0a4e577cc028bb2025-08-20T01:57:58ZengElsevierApplied Computing and Geosciences2590-19742024-12-012410020410.1016/j.acags.2024.100204Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of IndiaSrikanth Bhoopathi0Nitish Kumar1 Somesh2Manali Pal3Department of Civil Engineering, NIT Warangal, Telangana, 506004, IndiaDepartment of Civil Engineering, NIT Warangal, Telangana, 506004, IndiaDepartment of Civil Engineering, NIT Warangal, Telangana, 506004, IndiaCorresponding author.; Department of Civil Engineering, NIT Warangal, Telangana, 506004, IndiaThis research aims to forecast maximum temperatures and the frequency of heatwave days across four different temperature zones (Zone 1, 2, 3 and 4) in India. These four zones are categorized based on the 30-year average maximum temperatures (T30AMT) during the summer months of April, May, and June (AMJ). Two Machine Learning (ML) algorithms eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) are employed to achieve this goal. The study utilizes nine key atmospheric variables namely air temperature, geopotential height, relative humidity, U-wind, V-wind, soil moisture, solar radiation, sea surface temperature, and mean sea level pressure at a daily scale spanning from 1991 to 2020 for the months of March, April, May, and June as predictors. The India Meteorological Department daily maximum temperature data spanning from 1991 to 2020 for the months of AMJ serves as the predictands. ML models are developed using spatially averaged atmospheric variables and daily maximum temperature across the grids falling within each temperature zone. Results indicate that for a 7-day lead time, SVR outperforms XGBoost in Zone-1 (T30AMT > 38 °C) and Zone-2 (T30AMT: 35.01 °C–38 °C) by more accurately capturing peak temperatures during training and testing. Conversely, for a 15-day lead time in Zone-1, XGBoost better predicts temperature peaks in both phases. In Zone-3 (T30AMT: 30 °C–35 °C) and Zone-4 (T30AMT < 30 °C) for both lead times, the performance of both models decline, indicating models and input variables are more effective in predicting higher temperatures typical of Zone-1 and 2 but less so in Zone-3 and 4. In a nutshell, the study attempts to highlight the capability of advanced ML techniques combined with spatial climate data to enhance the prediction of extreme heatwave events. These insights can aid in heatwave preparedness, climate management, and adaptation strategies for different Indian temperature zones.http://www.sciencedirect.com/science/article/pii/S259019742400051XDaily maximum temperatureHeatwaveHeatwave daysXGBoost and support vector regression |
| spellingShingle | Srikanth Bhoopathi Nitish Kumar Somesh Manali Pal Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India Applied Computing and Geosciences Daily maximum temperature Heatwave Heatwave days XGBoost and support vector regression |
| title | Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India |
| title_full | Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India |
| title_fullStr | Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India |
| title_full_unstemmed | Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India |
| title_short | Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India |
| title_sort | evaluating the performances of svr and xgboost for short range forecasting of heatwaves across different temperature zones of india |
| topic | Daily maximum temperature Heatwave Heatwave days XGBoost and support vector regression |
| url | http://www.sciencedirect.com/science/article/pii/S259019742400051X |
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