#9824. Earthquake vulnerability assessment for the Indian subcontinent using the Long Short-Term Memory model (LSTM)

September 2026publication date
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Journal’s subject area:
Social Sciences (all);
Earth and Planetary Sciences (all);
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Abstract:
Earthquakes are one of the most destructive and unpredictable natural hazards with a long-term physical, psychological, and economic impact to the society. In the past century, more than 1100 destructive earthquakes occurred, and caused around 1.5 million deaths worldwide. Some recent studies have suggested that a future earthquake in the Himalayan region of magnitude range MW 7.5–8 can cause more than 0.2 million human lives and around 150 billion dollar financial loss. Deep learning methods in recent studies proved very useful in natural hazards forecasting and prediction modelling. Long Short-Term Memory (LSTM) model has been particularly popular in several natural hazard forecasting. In this research, for the first time, LSTM model is implemented with suitable Geospatial Information Systems (GIS) techniques to assess the earthquake vulnerability for whole of India.
Keywords:
Deep learning; Earthquake vulnerability; GIS; Indian subcontinent; LSTM model

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