Performance of a Five-Layer ANN Model for Earthquake Magnitude Prediction and Spatial Risk Mapping in Turkey

Saptadeep Biswas; Dhruv Kumar; Murat Nas; Mustafa Softa; Elif Akgün; Uttam Kumar Bera

doi:10.31181/dma31202553

Authors

Saptadeep Biswas Department of Mathematics, National Institute of Technology Agartala, 799046 Tripura, India https://orcid.org/0000-0002-2092-5487
Dhruv Kumar Department of Physics, National Institute of Technology Agartala, 799046 Tripura, India https://orcid.org/0000-0001-8205-0404
Murat Nas Department of Civil Engineering, Karadeniz Technical University, 61080 Trabzon, Turkey https://orcid.org/0000-0002-5726-4096
Mustafa Softa Department of Geological Engineering, Dokuz Eylül University, 35390 Izmir, Turkey https://orcid.org/0000-0001-5064-9260
Elif Akgün Department of Geological Engineering, Fırat University, 23119 Elazığ, Turkey https://orcid.org/0000-0002-6556-2413
Uttam Kumar Bera Department of Mathematics, National Institute of Technology Agartala, 799046 Tripura, India https://orcid.org/0000-0001-5426-7614

DOI:

https://doi.org/10.31181/dma31202553

Keywords:

Earthquake Spacial Forecasting, Artificial Neural Networks, Seismic Risks, USGS Earthquake Catalogue, Spatial Distribution Analysis, Disaster Preparedness

Abstract

Turkey faces significant seismic risks, necessitating accurate earthquake forecasting for effective disaster preparedness. This study employs advanced Artificial Neural Networks (ANN) to predict earthquake magnitudes and assess risks specific to Turkey. Focusing on a distinct segment of the USGS earthquake catalogue from January 2014 to August 2023, the research tailors ANN algorithms with five layers to Turkey's seismic challenges. Rigorous dataset cleaning and processing ensure accuracy, with the ANN model demonstrating exceptional alignment with earthquake data ( RMSE: 0.078, R2: 0.89). Comparative evaluations highlight the effectiveness of ANN models in forecasting earthquake magnitudes in Turkey. The study explores the spatial distribution of earthquake risk across Turkey through an ANN-based map, emphasizing the critical window for preventive measures in this seismically active region. The analysis ensures further enhancement of model accuracy in seismic-prone areas globally. This study advances earthquake prediction by showcasing the high accuracy of our five-layer ANN model in forecasting magnitudes and spatial risk, significantly improving disaster preparedness and risk management in regions such as Turkey.

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