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@article{198603,
        author = {Ripudaman M and Suvan S and Saran Karthick and Subhashini},
        title = {Evaluation of Supervised Machine Learning Model for Earthquake Magnitude Estimation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {10177-10182},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198603},
        abstract = {Seismological calculations of earthquakes' magnitudes from metadata of seismic catalogs seem to be a quite hard task. The problem is really tough for solving through the application of modern geoinformatics and machine learning techniques whatever approach or model is chosen. The vast majority of the existing scientific publications usually examine just one kind of data, and still nothing is known about their validity since there is not enough research done on the problem. This work considers the effectiveness of Linear Regression (LR) and Random Forest (RF) methods by analysing seismic data from USGS Monthly Seismic Catalogue, ISC-GEM Global Earthquakes Catalogue, and own compilation of the most powerful earthquakes in the period from 1995 to 2023. One of the main criteria of estimation is the prediction error distribution depending on magnitudes. The conclusion is obvious, the number of errors increases with the increase of seismic activity within all considered datasets, yet a strange finding has been made in the last dataset's, where LR surpasses RF. It is evident from both findings that metadata is not to be trusted for high magnitude earthquake classification.},
        keywords = {Earthquake magnitude estimation, Random Forest, Linear Regression, USGS, ISC-GEM, seismic metadata, magnitude-stratified analysis, Quantum SVM, ZZFeatureMap, cross-catalogue comparison},
        month = {April},
        }