Articles | Volume 25, issue 10
https://doi.org/10.5194/nhess-25-3713-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-3713-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Oct 2025
Research article |
| 01 Oct 2025
| 01 Oct 2025
An ensemble random forest model for seismic energy forecasting
Sukh Sagar Shukla
CORRESPONDING AUTHOR
School of Civil and Environmental Engineering, Indian Institute of Technology Mandi, Himachal Pradesh, India
Jaya Dhanya
School of Civil and Environmental Engineering, Indian Institute of Technology Mandi, Himachal Pradesh, India
Praveen Kumar
School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Himachal Pradesh, India
Priyanka
School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Himachal Pradesh, India
Varun Dutt
School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Himachal Pradesh, India
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Praveen Kumar, Priyanka Priyanka, Kala Venkata Uday, and Varun Dutt
Nat. Hazards Earth Syst. Sci., 24, 1913–1928, https://doi.org/10.5194/nhess-24-1913-2024, https://doi.org/10.5194/nhess-24-1913-2024, 2024
Short summary
Short summary
Our study focuses on predicting soil movement to mitigate landslide risks. We develop machine learning models with oversampling techniques to address the class imbalance in monitoring data. The dynamic ensemble model with K-means SMOTE (synthetic minority oversampling technique) achieves high precision, high recall, and a high F1 score. Our findings highlight the potential of these models with oversampling techniques to improve soil movement predictions in landslide-prone areas.
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Short summary
Earthquakes are among the most disastrous natural calamities due to the release of accumulated strain energy from continuous tectonic movements. They have the potential to cause havoc in terms of both economic losses and loss of life. This paper presents a methodology to predict earthquakes in terms of seismic energy release globally using an ensemble machine learning technique, and then the approach is tested for one of the most seismically active regions of the world: the Western Himalayas.
Earthquakes are among the most disastrous natural calamities due to the release of accumulated...
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