Araştırma Makalesi
Yıl 2025,
Erken Görünüm, 1 - 1
Öz
Kaynakça
- [1] Wang, Q., Guo, Y., Yu, L., Li, P. “Earthquake prediction based on spatio-temporal data mining: An LSTM network approach”, IEEE Transactions on Emerging Topics in Computing, 8(1): 148-158, (2017).
- [2] Galkina, A., Grafeeva, N. “Machine learning methods for earthquake prediction: A survey”, In Proceedings of the Fourth Conference on Software Engineering and Information Management (SEIM-2019), Saint Petersburg, Russia, (2019).
- [3] Pribadi, K.S., Abduh, M., Wirahadikusumah, R.D., Hanifa, N.R., Irsyam, M., Kusumaningrum, P., Puri, E., “Learning from past earthquake disasters: The need for knowledge management system to enhance infrastructure resilience in Indonesia”, International Journal of Disaster Risk Reduction, 64, (2021).
- [4] Erzin, Y., Cetin, T., “The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces”, Scientia Iranica, 19(2): 188-194, (2012).
- [5] Asim, K. M., Martínez-Álvarez, F., Basit, A., Iqbal, T., “Earthquake magnitude prediction in Hindukush region using machine learning techniques”, Natural Hazards, 85(1): 471-486, (2017).
- [6] Moustra, M., Avraamides, M., Christodoulou, C., “Artificial neural networks for earthquake prediction using time series magnitude data or seismic electric signals”, Expert systems with applications, 38(12): 15032-15039, (2011).
- [7] Florido, E., Martínez-Álvarez, F., Morales-Esteban, A., Reyes, J., Aznarte-Mellado, J.L., “Detecting precursory patterns to enhance earthquake prediction in Chile”, Computers & Geosciences, 76: 112-120, (2015).
- [8] Asencio-Cortés, G., Martínez-Álvarez, F., Troncoso, A., Morales-Esteban, A., “Medium–large earthquake magnitude prediction in Tokyo with artificial neural networks”, Neural Computing and Applications, 28(5): 1043-1055, (2017).
- [9] Kavianpour, P., Kavianpour, M., Jahani, E., and Ramezani, A., “A cnn-bilstm model with attention mechanism for earthquake prediction”, The Journal of Supercomputing, 1-33, (2023).
- [10] Sadhukhan, B., Chakraborty, S., Mukherjee, S., and Samanta, R. K., “Climatic and seismic data-driven deep learning model for earthquake magnitude prediction”, Frontiers in Earth Science, 11, 1082832, (2023).
- [11] Berhich, A., Belouadha, F. Z., and Kabbaj, M. I., “An attention-based LSTM network for large earthquake prediction”, Soil Dynamics and Earthquake Engineering, 165: 107663, (2023).
- [12] Katuwal, R., Suganthan, P. N., Zhang, L., “An ensemble of decision trees with random vector functional link networks for multi-class classification”, Applied Soft Computing, 70: 1146-1153, (2018).
- [13] Akhiat, Y., Manzali, Y., Chahhou, M., Zinedine, A., “A new noisy random forest based method for feature selection”, Cybernetics and Information Technologies, 21(2): 10-28, (2021).
- [14] Zhang, F., Yang, X., “Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection”, Remote Sensing of Environment, 251, (2020).
- [15] Wang, Z., Lou, Y., “Hydrological time series forecast model based on wavelet de-noising and ARIMA-LSTM”, 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China, (2019).
- [16] Khan, F. M., Gupta, R., “ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India”, Journal of Safety Science and Resilience, 1(1): 12-18, (2020).
- [17] Patil, P., “A Comparative Study of Different Time Series Forecasting Methods for Predicting Traffic Flow and Congestion Levels in Urban Networks”, International Journal of Information and Cybersecurity, 6(1): 1-20, (2022).
- [18] Schaffer, A.L., Dobbins, T.A., Pearson, S.A., “Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions”, BMC medical research methodology, 21(1): 1-12, (2021).
- [19] Dhyani, B., Kumar, M., Verma, P., Jain, A., “Stock market forecasting technique using arima model”, International Journal of Recent Technology and Engineering, 8(6): 2694-2697, (2020).
- [20] Ren, H., Xu, B., Wang, Y., Yi, C., Huang, C., Kou, X., Zhang, Q., “Time-series anomaly detection service at Microsoft”, Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, Anchorage, USA, (2019).
- [21] He, Q., Zheng, Y.J., Zhang, C.L., Wang, H.Y., “MTAD-TF: Multivariate time series anomaly detection using the combination of temporal pattern and feature pattern”, Complexity, 2020: 1-9, (2020).
- [22] Shih, S.Y., Sun, F.K., Lee, H.Y., “Temporal pattern attention for multivariate time series forecasting”, Machine Learning, 108: 1421-1441, (2019).
- [23] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y., “Recurrent neural networks for multivariate time series with missing values”, Scientific reports, 8(1): 6085, (2018).
- [24] Dai, G., Ma, C., Xu, X., “Short-term traffic flow prediction method for urban road sections based on space–time analysis and GRU”, IEEE Access, 7: 143025-143035, (2019).
- [25] Weerakody, P.B., Wong, K.W., Wang, G., Ela, W., “A review of irregular time series data handling with gated recurrent neural networks”, Neurocomputing, 441: 161-178, (2021).
- [26] Cook, A.A., Mısırlı, G., Fan, Z., “Anomaly detection for IoT time-series data: A survey”, IEEE Internet of Things Journal, 7(7): 6481-6494, (2019).
Yıl 2025,
Erken Görünüm, 1 - 1
Öz
Earthquakes are one of the most dangerous natural disasters that have constantly threatened humanity in the last decade. Therefore, it is extremely important to take preventive measures against earthquakes. Time estimation in these dangerous events is becoming more specific, especially in order to minimize the damage caused by earthquakes. In this study, a hybrid deep learning model is proposed to predict the time of the next earthquake to potentially occur. The developed CNN+GRU model was compared with RF, ARIMA, CNN and GRU. These models were tested using an earthquake dataset. Experimental results show that the CNN+GRU model performs better than others according to MSE, RMSE, MAE and MAPE metrics. This study highlights the importance of predicting earthquakes, providing a way to help take more effective precautions against earthquakes and potentially minimize loss of life and material damage. This study should be considered an important step in the methods used to predict future earthquakes and supports efforts to reduce earthquake risks.
Anahtar Kelimeler
Kaynakça
- [1] Wang, Q., Guo, Y., Yu, L., Li, P. “Earthquake prediction based on spatio-temporal data mining: An LSTM network approach”, IEEE Transactions on Emerging Topics in Computing, 8(1): 148-158, (2017).
- [2] Galkina, A., Grafeeva, N. “Machine learning methods for earthquake prediction: A survey”, In Proceedings of the Fourth Conference on Software Engineering and Information Management (SEIM-2019), Saint Petersburg, Russia, (2019).
- [3] Pribadi, K.S., Abduh, M., Wirahadikusumah, R.D., Hanifa, N.R., Irsyam, M., Kusumaningrum, P., Puri, E., “Learning from past earthquake disasters: The need for knowledge management system to enhance infrastructure resilience in Indonesia”, International Journal of Disaster Risk Reduction, 64, (2021).
- [4] Erzin, Y., Cetin, T., “The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces”, Scientia Iranica, 19(2): 188-194, (2012).
- [5] Asim, K. M., Martínez-Álvarez, F., Basit, A., Iqbal, T., “Earthquake magnitude prediction in Hindukush region using machine learning techniques”, Natural Hazards, 85(1): 471-486, (2017).
- [6] Moustra, M., Avraamides, M., Christodoulou, C., “Artificial neural networks for earthquake prediction using time series magnitude data or seismic electric signals”, Expert systems with applications, 38(12): 15032-15039, (2011).
- [7] Florido, E., Martínez-Álvarez, F., Morales-Esteban, A., Reyes, J., Aznarte-Mellado, J.L., “Detecting precursory patterns to enhance earthquake prediction in Chile”, Computers & Geosciences, 76: 112-120, (2015).
- [8] Asencio-Cortés, G., Martínez-Álvarez, F., Troncoso, A., Morales-Esteban, A., “Medium–large earthquake magnitude prediction in Tokyo with artificial neural networks”, Neural Computing and Applications, 28(5): 1043-1055, (2017).
- [9] Kavianpour, P., Kavianpour, M., Jahani, E., and Ramezani, A., “A cnn-bilstm model with attention mechanism for earthquake prediction”, The Journal of Supercomputing, 1-33, (2023).
- [10] Sadhukhan, B., Chakraborty, S., Mukherjee, S., and Samanta, R. K., “Climatic and seismic data-driven deep learning model for earthquake magnitude prediction”, Frontiers in Earth Science, 11, 1082832, (2023).
- [11] Berhich, A., Belouadha, F. Z., and Kabbaj, M. I., “An attention-based LSTM network for large earthquake prediction”, Soil Dynamics and Earthquake Engineering, 165: 107663, (2023).
- [12] Katuwal, R., Suganthan, P. N., Zhang, L., “An ensemble of decision trees with random vector functional link networks for multi-class classification”, Applied Soft Computing, 70: 1146-1153, (2018).
- [13] Akhiat, Y., Manzali, Y., Chahhou, M., Zinedine, A., “A new noisy random forest based method for feature selection”, Cybernetics and Information Technologies, 21(2): 10-28, (2021).
- [14] Zhang, F., Yang, X., “Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection”, Remote Sensing of Environment, 251, (2020).
- [15] Wang, Z., Lou, Y., “Hydrological time series forecast model based on wavelet de-noising and ARIMA-LSTM”, 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chengdu, China, (2019).
- [16] Khan, F. M., Gupta, R., “ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India”, Journal of Safety Science and Resilience, 1(1): 12-18, (2020).
- [17] Patil, P., “A Comparative Study of Different Time Series Forecasting Methods for Predicting Traffic Flow and Congestion Levels in Urban Networks”, International Journal of Information and Cybersecurity, 6(1): 1-20, (2022).
- [18] Schaffer, A.L., Dobbins, T.A., Pearson, S.A., “Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions”, BMC medical research methodology, 21(1): 1-12, (2021).
- [19] Dhyani, B., Kumar, M., Verma, P., Jain, A., “Stock market forecasting technique using arima model”, International Journal of Recent Technology and Engineering, 8(6): 2694-2697, (2020).
- [20] Ren, H., Xu, B., Wang, Y., Yi, C., Huang, C., Kou, X., Zhang, Q., “Time-series anomaly detection service at Microsoft”, Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, Anchorage, USA, (2019).
- [21] He, Q., Zheng, Y.J., Zhang, C.L., Wang, H.Y., “MTAD-TF: Multivariate time series anomaly detection using the combination of temporal pattern and feature pattern”, Complexity, 2020: 1-9, (2020).
- [22] Shih, S.Y., Sun, F.K., Lee, H.Y., “Temporal pattern attention for multivariate time series forecasting”, Machine Learning, 108: 1421-1441, (2019).
- [23] Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y., “Recurrent neural networks for multivariate time series with missing values”, Scientific reports, 8(1): 6085, (2018).
- [24] Dai, G., Ma, C., Xu, X., “Short-term traffic flow prediction method for urban road sections based on space–time analysis and GRU”, IEEE Access, 7: 143025-143035, (2019).
- [25] Weerakody, P.B., Wong, K.W., Wang, G., Ela, W., “A review of irregular time series data handling with gated recurrent neural networks”, Neurocomputing, 441: 161-178, (2021).
- [26] Cook, A.A., Mısırlı, G., Fan, Z., “Anomaly detection for IoT time-series data: A survey”, IEEE Internet of Things Journal, 7(7): 6481-6494, (2019).
Toplam 26 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Derin Öğrenme |
| Bölüm | Research Article |
| Yazarlar | |
| Erken Görünüm Tarihi | 2 Nisan 2024 |
| Yayımlanma Tarihi | |
| Yayımlandığı Sayı | Yıl 2025 Erken Görünüm |