Earthquake detection and early warning prediction using folium and Geopandas

Abstract

The challenge of swift and reliable earthquake location prediction within earthquake early warning (EEW) systems underscore the need for innovative solutions. Existing methods provide predictions, yet there is a clear demand for enhanced accuracy and expediency in the process of resolving. The article intends in prediction of both magnitude of earthquakes and the affected areas in a specific region, focusing on California, United States, by leveraging historical data using neural networks (NNs). Neural networks outperform other algorithms such as XGBoost, linear regression, random forest, gradient boosting and support vector machine (SVM), in terms of high R² score and low score of MAE and also mean squared error (MSE). The derived results of R² score, MSE and MAE are 0.1607, 0.1615 and 0.2951, respectively. It seamlessly integrates geospatial visualization through Folium and GeoPandas. These tools enhance the predictive model’s capabilities by generating dynamic maps enriched with markers, each representing the anticipated impact of earthquakes. The user interface facilitates interactive input, enabling users to input earthquake parameters for real-time predictions. The resultant map not only showcases the predicted impact zone but also provides valuable insights into the severity of seismic events. It stands as a testament to the synergy between machine learning and geospatial visualization, offering a holistic solution for earthquake prediction and geographical representation.

Keywords:

• Earthquake detection
• folium
• Geopandas
• machine learning

Reviewing editor:

• Swadesh Kumar Singh, Gokaraju Rangaraju Institute of Engineering and Technology, India; Victoria University, Australia

Subjects:

• Artificial Intelligence
• Neural Networks
• Machine Learning - Design
• Computer Engineering

Authors’ contributions

Conceptualization – Dr. Y. Krishna Bhargavi; Methodology – GangulaVarshitha Reddy, YellineniPranathi, R. Sneha and S. Gayathri; Investigation – GangulaVarshitha Reddy, YellineniPranathi, R. Sneha and S. Gayathri; resources – Yellineni Pranathi; data curation – Upendra Dabral; writing and original draft preparation – Dr. Y. Krishna Bhargavi; writing: review and editing – Solovev S. A.; visualization – Nikolai IvanovichVatin; supervision – Dr. Y. Krishna Bhargavi; project administration – Dr. Y. Krishna Bhargavi.

Disclosure statement

The authors declare no conflict of interest.

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.

Additional information

Funding

No funding was received for this article.