Earthquake detection and early warning prediction using folium and Geopandas

Figures & data

Figure 1. Machine learning pipeline for earthquake detection and prediction.

Table 1. Comparison of metrics among machine learning model.

Model name	R^2	Mean absolute error (MAE)	Mean squared error (MSE)
Linear regression	0.023513	0.322742	0.187941
SVM	−2.627321	0.571287	0.698141
Random forest	0.136024	0.298496	0.166287
Neural network	0.160769	0.295104	0.161524
XGBoost	0.137525	0.300482	0.165998
Gradient boosting	0.140008	0.296574	0.165520

Figure 2. Comparison of mean absolute error among machine learning models.

Figure 3. Comparison of mean absolute error among machine learning models.

Figure 4. Comparison of mean absolute error among machine learning models.

Figure 5. Importance of features.

Figure 6. Actual versus predicted magnitude.

Figure 7. Result of machine learning model.

Figure 8. Visual representation of predictions.

Data availability statement

The data that support the findings of this study are openly available in NCEDC (2014), Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory Dataset at http://doi.org/10.7932/NCEDC.