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Comparative Study
. 2025 Jul 1;15(1):22431.
doi: 10.1038/s41598-025-04634-9.

Comparative analysis of machine learning approaches for heatwave event prediction in India

Ritesh Choudary V  1 , Anita Christaline Johnvictor  2 , Prem Sankar N  3
Affiliations
Comparative Study

Comparative analysis of machine learning approaches for heatwave event prediction in India

Ritesh Choudary V et al. Sci Rep. .

Abstract

Heatwaves, are identified as prolonged durations of unusually high temperatures, which pose significant threats to human health, animal health and agriculture. With the increasing frequency and intensity of heatwaves driven by climate change, accurate and early prediction of these extreme weather events is crucial for effective mitigation and adaptation. This research paper conducts a comparative analysis of various machine learning models for heatwave event classification using a time series dataset from a weather station in the equatorial region of India, specifically Chennai, Tamil Nadu. The study evaluates the performance of models including Random Forest, Convolutional Neural Networks, LightGBM, Long Short-Term Memory Networks, Transformer Networks, Support Vector Machines, Graph Neural Networks, Extreme Gradient Boosting and Autoencoders for Anomaly Detection in heatwave. The challenges posed by class imbalance and the limitations of traditional oversampling techniques are discussed, with insights into effective strategies for improving prediction accuracy. Accurate prediction of heatwaves enables mitigation plans to protect humans, animal and plants.

Keywords: Animal health; Classification; Climate change; Heatwaves; Human health; Machine learning.

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Conflict of interest statement

Declarations. Competing interests: The authors declare “No potential competing interest was reported by the author(s).”, The authors declare no conflict of interest with anyone.

Figures

Fig. 1
Fig. 1
Heat prediction workflow implemented in this research.
Fig. 2
Fig. 2
Temporal and spatial resolution of the dataset with 20 different parameters from Tambaram, Chennai, India.
Fig. 3
Fig. 3
Temperature distribution over the 14 years of the dataset.
Fig. 4
Fig. 4
The number of heat waves in each year.
Fig. 5
Fig. 5
Duration of the heatwaves for consecutive days.
Fig. 6
Fig. 6
Correlation matrix of features. (a) is Correlation matrix before feature selection. (b) is Correlation matrix after feature selection.
Fig. 7
Fig. 7
Radar chart for overall performance.
Fig. 8
Fig. 8
Predicted heatwave count for all nine models.
Fig. 9
Fig. 9
Radar chart for overall performance along with persistence prediction model.
See this image and copyright information in PMC

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