Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 25, issue 10
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.
https://doi.org/10.5194/nhess-25-3713-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 10
Research article
 | 
01 Oct 2025
Research article |  | 01 Oct 2025

An ensemble random forest model for seismic energy forecasting

Sukh Sagar Shukla, Jaya Dhanya, Praveen Kumar, Priyanka, and Varun Dutt

Viewed

Total article views: 1,536 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,008 258 270 1,536 8 42 61
  • HTML: 1,008
  • PDF: 258
  • XML: 270
  • Total: 1,536
  • Supplement: 8
  • BibTeX: 42
  • EndNote: 61
Views and downloads (calculated since 24 Oct 2024)
Cumulative views and downloads (calculated since 24 Oct 2024)

Viewed (geographical distribution)

Total article views: 1,536 (including HTML, PDF, and XML) Thereof 1,508 with geography defined and 28 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 22 Oct 2025
Download
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint