# Usage of multiple RTL features for Earthquake prediction

arXiv: https://arxiv.org/abs/1905.10805 · HTML: https://mlsanity.com/paper/1905.10805/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

## Built on by (most cited first, 2 of 2)

- [Long-term drought prediction using deep neural networks based on geospatial weather data](https://mlsanity.com/paper/2309.06212/) (2023)
- [Predicting spatial distribution of Palmer Drought Severity Index](https://mlsanity.com/paper/2208.14833/) (2022)

## Abstract (arXiv)

We construct a classification model that predicts if an earthquake with the magnitude above a threshold will take place at a given location in a time range 30-180 days from a given moment of time. A common approach is to use expert forecasts based on features like Region-Time-Length (RTL) characteristics. The proposed approach uses machine learning on top of multiple RTL features to take into account effects at various scales and to improve prediction accuracy. For historical data about Japan earthquakes 1992-2005 and predictions at locations given in this database the best model has precision up to ~ 0.95 and recall up to ~ 0.98.