• Dr.M.Ussenaiah
• Dr.K VenkataNagendra

This study proposes an innovative approach to improve earthquake location prediction in early warning systems by integrating machine learning techniques. Traditional earthquake location prediction methods often rely on seismic data analysis and manual interpretation, which are labor-intensive and prone to errors. The proposed method aims to make location prediction more efficient by utilizing machine learning algorithms such as neural networks and support vector machines. The model is trained using previous earthquake connections detectable using traditional approaches. The integration of machine learning into early warning systems aims to provide more timely and reliable information to support disaster response and mitigation. This study contributes to the expanding application of machine learning in seismology and disaster management and offers a promising avenue for improving earthquake prevention.

Machine learning, Earthquake, Location prediction, Early warning systems, Neural Networks, Support Vector Machines.

"COMBINING MACHINE LEARNING FOR IMPROVED PREDICTION OF EARTHQUAKES IN WARNING SYSTEMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.h778-h788, June-2024, Available :http://www.jetir.org/papers/JETIR2406783.pdf

"COMBINING MACHINE LEARNING FOR IMPROVED PREDICTION OF EARTHQUAKES IN WARNING SYSTEMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pph778-h788, June-2024, Available at : http://www.jetir.org/papers/JETIR2406783.pdf