Image of Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India

Text

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 (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.

Ketersediaan
217551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
13 hlm PDF, 11.612 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.24, December 2024
Subjek
Heatwave
Daily maximum temperature
Heatwave days
XGBoost and support vector regression
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • 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 (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.
Komentar
Anda harus masuk sebelum memberikan komentar