Machine learning for predicting earthquake magnitudes in the Central Himalaya
Human intervention cannot halt natural disasters like earthquakes, but machine learning application expertise can be utilized to detect patterns in data and increase understanding and predictive power. Recent development of machine learning models has increasingly developed interest in forecasting...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Department of Physics, Mahendra Morang Adarsh Multiple Campus, Tribhuvan University
2025-01-01
|
| Series: | Bibechana |
| Subjects: | |
| Online Access: | https://nepjol.info/index.php/BIBECHANA/article/view/70637 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832582638312882176 |
|---|---|
| author | Ram Krishna Tiwari Rudra Prasad Poudel Harihar Paudyal |
| author_facet | Ram Krishna Tiwari Rudra Prasad Poudel Harihar Paudyal |
| author_sort | Ram Krishna Tiwari |
| collection | DOAJ |
| description |
Human intervention cannot halt natural disasters like earthquakes, but machine learning application expertise can be utilized to detect patterns in data and increase understanding and predictive power. Recent development of machine learning models has increasingly developed interest in forecasting and predicting the magnitude of earthquakes. In this work, Random Forest Regressor (RFR), Multi-Layer Perceptron Regressor(MLPR), and Support Vector Regression (SVR) models were employed to predict the magnitude of greater than 6 mb earthquakes that occurred in the year 2015 in the central Himalaya. We noticed RFR method had been able to predict the magnitude of the Gorkha earthquake (6.9mb), and the Kodariearthquake (6.7 mb) in comparison with the other two models. We also checked the performance of these models by three parameters Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) and noticed the better performance of RFR model. The findings illustrate that RFR is achieving better performance than the other two algorithms, as the predicted magnitudes are close to the actual magnitudes.
|
| format | Article |
| id | doaj-art-47cfa8b128604794b118a49487afc092 |
| institution | Kabale University |
| issn | 2091-0762 2382-5340 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Department of Physics, Mahendra Morang Adarsh Multiple Campus, Tribhuvan University |
| record_format | Article |
| series | Bibechana |
| spelling | doaj-art-47cfa8b128604794b118a49487afc0922025-01-29T13:15:25ZengDepartment of Physics, Mahendra Morang Adarsh Multiple Campus, Tribhuvan UniversityBibechana2091-07622382-53402025-01-01221Machine learning for predicting earthquake magnitudes in the Central Himalaya Ram Krishna Tiwari0Rudra Prasad Poudel1Harihar Paudyal 2Birendra Multiple Campus, Tribhuvan University, Bharatpur, ChitwanCentral Department of Physics, Tribhuvan University, Kathmandu 44600, NepalBirendra Multiple Campus, Tribhuvan University, Bharatpur, Chitwan Human intervention cannot halt natural disasters like earthquakes, but machine learning application expertise can be utilized to detect patterns in data and increase understanding and predictive power. Recent development of machine learning models has increasingly developed interest in forecasting and predicting the magnitude of earthquakes. In this work, Random Forest Regressor (RFR), Multi-Layer Perceptron Regressor(MLPR), and Support Vector Regression (SVR) models were employed to predict the magnitude of greater than 6 mb earthquakes that occurred in the year 2015 in the central Himalaya. We noticed RFR method had been able to predict the magnitude of the Gorkha earthquake (6.9mb), and the Kodariearthquake (6.7 mb) in comparison with the other two models. We also checked the performance of these models by three parameters Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) and noticed the better performance of RFR model. The findings illustrate that RFR is achieving better performance than the other two algorithms, as the predicted magnitudes are close to the actual magnitudes. https://nepjol.info/index.php/BIBECHANA/article/view/70637Machine LearningEarthquakeRegressorPrediction |
| spellingShingle | Ram Krishna Tiwari Rudra Prasad Poudel Harihar Paudyal Machine learning for predicting earthquake magnitudes in the Central Himalaya Bibechana Machine Learning Earthquake Regressor Prediction |
| title | Machine learning for predicting earthquake magnitudes in the Central Himalaya |
| title_full | Machine learning for predicting earthquake magnitudes in the Central Himalaya |
| title_fullStr | Machine learning for predicting earthquake magnitudes in the Central Himalaya |
| title_full_unstemmed | Machine learning for predicting earthquake magnitudes in the Central Himalaya |
| title_short | Machine learning for predicting earthquake magnitudes in the Central Himalaya |
| title_sort | machine learning for predicting earthquake magnitudes in the central himalaya |
| topic | Machine Learning Earthquake Regressor Prediction |
| url | https://nepjol.info/index.php/BIBECHANA/article/view/70637 |
| work_keys_str_mv | AT ramkrishnatiwari machinelearningforpredictingearthquakemagnitudesinthecentralhimalaya AT rudraprasadpoudel machinelearningforpredictingearthquakemagnitudesinthecentralhimalaya AT hariharpaudyal machinelearningforpredictingearthquakemagnitudesinthecentralhimalaya |