Volume 20 No 22 (2022)
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Comparative Analysis of AI Regression and Classification Models for Predicting Earthquake-Induced House Damages in Nepal
Aditya Saxena , RishabhChauhan , Devansh Chauhan , Dr Shilpi Sharma , and Dr. Dolly Sharma
This paper proposes a machine learning model for earthquake prediction. Earthquakes are complex and unpredictable natural phenomena, making it challenging to predict them accurately. However, recent advances in machine learning techniques have shown promise in predicting earthquakes by analyzing various factors such as seismic activity, geospatial data, and weather patterns. In this study, we collected earthquake data from various sources and used it to train a machine learning model. We evaluated the model's performance using metrics such as accuracy, precision, and recall. Our results demonstrate that our machine learning model can accurately predict earthquakes, with a high degree of precision and recall. The model has the potential to provide early warnings of earthquakes, which can help reduce the damage caused by these disasters. Overall, this study highlights the potential of machine learning in earthquake prediction and provides a roadmap for future research in this field.
Earthquake Prediction, Machine Learning, Regression.
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