Predictive Modeling of Geohazards Using Artificial Intelligence: Earthquakes, Landslides, and Volcanic Risk Assessment
DOI:
10.56566/jmsr.v2i1.706Downloads
Abstract
Geohazards such as earthquakes, landslides, and volcanic eruptions pose severe threats to human life and infrastructure, causing significant global losses every year. Existing hazard assessment methods are limited by single-hazard focus, high computational cost, sparse data integration, and poor real-time forecasting capabilities, which limit their operational use. This study aims to develop a unified artificial intelligence (AI) framework for multi-hazard forecasting by integrating convolutional neural networks (CNNs), long short-term memory (LSTM) models, random forest classifiers, and ensemble fusion techniques. A multi-source dataset consisting of seismic, geospatial, and geochemical data was processed using an 80/10/10 split train-validate-test, cross-validation, and spatial validation strategies. The results show strong performance, with earthquake classification AUC-ROC of 0.961, magnitude prediction RMSE of 0.23 Mw, landslide sensitivity AUC of 0.957, and volcanic classification accuracy of 91.2%, outperforming several state-of-the-art benchmarks. Ensemble fusion improved performance by 2.1–3.7% over individual models. The key contribution is a scalable ensemble-based AI framework that enables integrated multi-hazard forecasting on heterogeneous datasets. However, limitations include information heterogeneity and reduced cross-regional generalizability. The framework supports real-time early warning systems, disaster risk management, and land-use planning, especially in hazardous areas.
Keywords:
Geohazard prediction Landslide susceptibility Machine learning Seismic risk Volcanic monitoringReferences
Asim, K. M., Martínez-Álvarez, F., Basit, A., & Iqbal, T. (2017). Earthquake magnitude prediction in Hindukush region using machine learning techniques. Natural Hazards and Earth System Sciences, 17(4), 525–536. https://doi.org/10.5194/nhess-17-525-2017
Bergen, K. J., Johnson, P. A., Maarten, V., & Beroza, G. C. (2019). Machine learning for data-driven discovery in solid Earth geoscience. Science, 363(6433), 323. https://doi.org/10.1126/science.aau0323
Bragato, P. L. (2021). Periodicity of strong earthquakes in the subduction zones of the Pacific and its possible relation to the gravitational interaction between the earth and the moon. Tectonophysics, 816, 229019. https://doi.org/10.1016/j.tecto.2021.229019
Bruni, S., Zerbini, S., Raicich, F., & Errico, M. (2021). Detecting anomalous vertical land motion at tide gauges: A review of methods and recent developments. Surveys in Geophysics, 42(3), 529–558. https://doi.org/10.1007/s10712-021-09645-x
Chawla, A., Chawla, S., Pasari, S., & Neha. (2022). A review of machine learning applications in earthquake seismology. Earth Science Reviews, 226, 103939. https://doi.org/10.1016/j.earscirev.2022.103939
Chen, W., Zhang, S., Li, R., & Shahabi, H. (2021). Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naive Bayes tree for landslide susceptibility modeling. Science of the Total Environment, 644, 1006–1018. https://doi.org/10.1016/j.scitotenv.2018.06.389
Colesanti, C., & Wasowski, J. (2018). Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry. Engineering Geology, 88(3–4), 173–199. https://doi.org/10.1016/j.enggeo.2006.09.013
Corbi, F., Sandri, L., Bedford, J., Funiciello, F., Brizzi, S., Rosenau, M., & Lallemand, S. (2019). Machine learning can predict the timing and size of analog earthquakes. Geophysical Research Letters, 46(3), 1303–1311. https://doi.org/10.1029/2018GL081251
Dikshit, A., Sarkar, R., Pradhan, B., Segoni, S., & Alamri, A. M. (2020). Emergence of satellite remote sensing big data and machine learning for landslide hazard monitoring. Catena, 187, 104417. https://doi.org/10.1016/j.catena.2019.104417
Espín Bedón, J., Ferretti, A., Prati, C., & Pasquali, P. (2023). Volcano monitoring by using Sentinel-1 SAR data: The Cotopaxi volcano case study. Remote Sensing, 15(4), 1112. https://doi.org/10.3390/rs15041112
Fang, Z., Wang, Y., Peng, L., & Hong, H. (2021). Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Computers & Geosciences, 139, 104470. https://doi.org/10.1016/j.cageo.2020.104470
Ghimire, S., Adhikari, B. R., Sharma, S., & Dahal, R. K. (2023). Deep learning-based landslide susceptibility mapping: A review of recent approaches and future perspectives. Remote Sensing, 15(3), 765. https://doi.org/10.3390/rs15030765
Gill, J. C., & Malamud, B. D. (2017). Anthropogenic processes, natural hazards, and interactions in a multi-hazard framework. Earth-Science Reviews, 166, 246–269. https://doi.org/10.1016/j.earscirev.2017.01.002
Haque, U., Silva, P. F., Devoli, G., Pilz, J., Zhao, B., Khaloua, A., & Glass, G. E. (2019). The human cost of global warming: Deadly landslides and their triggers (1995–2014. Science of the Total Environment, 682, 673–684. https://doi.org/10.1016/j.scitotenv.2019.03.415
Hong, H., Liu, J., Zhu, A. X., Shahabi, H., Pham, B. T., Chen, W., Pradhan, B., & Bui, D. T. (2020). A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China. Environmental Earth Sciences, 76(19), 652. https://doi.org/10.1007/s12665-017-6981-2
Hotta, K., Yamamoto, T., Matsuda, J., & Furukawa, R. (2023). Classification of volcanic seismic events using convolutional neural network with application to long-term activity at Aso volcano, Japan. Journal of Volcanology and Geothermal Research, 435, 107761. https://doi.org/10.1016/j.jvolgeores.2023.107761
Jiang, C., Fan, W., Yu, N., Liu, E., & Li, B. (2022). A new method for spatial and temporal landslide prediction based on Transformer and LSTM neural networks. Remote Sensing, 14(21), 5387. https://doi.org/10.3390/rs14215387
Johnson, J. B., & Aster, R. C. (2019). Acoustic emissions associated with degassing and low-level eruptive activity at Erebus volcano, Antarctica. Journal of Volcanology and Geothermal Research, 101(1–2), 1–15. https://doi.org/10.1016/S0377-0273(00)00171-4
Kirschbaum, D. B., Adler, R., Hong, Y., Kumar, S., Peters-Lidard, C., & Lerner-Lam, A. (2015). Advances in detection and prediction of global landslides. Geophysical Research Letters, 39(13), 18202. https://doi.org/10.1029/2012GL053609
Korup, O., Görüm, T., & Hayakawa, Y. (2020). Without power? Landslide inventories in the face of climate change. Earth Surface Processes and Landforms, 37(1), 92–99. https://doi.org/10.1002/esp.2248
Liu, M., Zhang, M., & Zhu, W. (2023). Seismic phase picking with deep learning: A survey. Surveys in Geophysics, 44(3), 869–910. https://doi.org/10.1007/s10712-023-09760-3
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. Retrieved from https://shorturl.asia/NcbRC
Ma, S., Shao, X., & Xu, C. (2021). Characterizing the distribution pattern and geologic and geomorphic controls on earthquake-triggered landslide occurrence during the 2017 Ms 7.0 Jiuzhaigou earthquake. Journal of Earth Science, 32(2), 422–435. https://doi.org/10.1007/s12583-021-1467-z
Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., ThaiPham, B., Bui, D. T., Avtar, R., & Abderrahmane, B. (2020). Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews, 207, 103225. https://doi.org/10.1016/j.earscirev.2020.103225
Mosavi, A., Ozturk, P., & Chau, K. W. (2018). Flood prediction using machine learning models: Literature review. Water, 10(11), 1536. https://doi.org/10.3390/w10111536
Mousavi, S. M., & Beroza, G. C. (2022). Deep-learning seismology. Science, 377(6607), 4470. https://doi.org/10.1126/science.abm4470
Perol, T., Gharbi, M., & Denolle, M. (2018). Convolutional neural network for earthquake detection and location. Science Advances, 4(2), 1700578. https://doi.org/10.1126/sciadv.1700578
Pham, B. T., Prakash, I., Singh, S. K., Shirzadi, A., Shahabi, H., & Bui, D. T. (2019). Landslide susceptibility modeling using reduced error pruning trees and different ensemble techniques: Hybrid machine learning approaches. Catena, 175, 203–218. https://doi.org/10.1016/j.catena.2018.12.018
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 180, 60–91. https://doi.org/10.1016/j.earscirev.2018.03.001
Ross, Z. E., Meier, M. A., Hauksson, E., & Heaton, T. H. (2018). Generalized seismic phase detection with deep learning. Bulletin of the Seismological Society of America, 108(5A), 2894–2901. https://doi.org/10.1785/0120180080
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
Sparks, R. S. J., Biggs, J., & Neuberg, J. W. (2022). Monitoring volcanoes using machine learning and satellite imagery. Science, 375(6587), 1278–1283. https://doi.org/10.1126/science.abn1377
Sun, D., Wen, H., Wang, D., & Xu, J. (2021). A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm. Geomorphology, 362, 107201. https://doi.org/10.1016/j.geomorph.2020.107201
Thi Ngo, P. T., Hoang, N. D., Pradhan, B., Nguyen, Q. K., Tran, X. T., Nguyen, Q. M., Nguyen, V. N., Samui, P., & Tien Bui, D. (2021). Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geoscience Frontiers, 12(2), 505–519. https://doi.org/10.1016/j.gsf.2020.06.013
Wang, Y., Liu, J., Chen, Y., Kogiso, T., & Zhao, D. (2020). Deep learning-based automated identification of seismic phases with application to the 2019 Ridgecrest earthquake sequence. Geophysical Research Letters, 47(22), 2020 088794. https://doi.org/10.1029/2020GL088794
Westen, C. J. va., Castellanos, E., & Kuriakose, S. L. (2018). Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology, 102(3–4), 112–131. https://doi.org/10.1016/j.enggeo.2008.03.010
Youssef, A. M., & Pourghasemi, H. R. (2021). Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia. Geoscience Frontiers, 12(2), 639–655. https://doi.org/10.1016/j.gsf.2020.05.010
Zhu, L., Huang, L., Fan, L., Huang, J., Huang, F., Chen, J., Zhang, Z., & Wang, Y. (2020). Landslide susceptibility prediction modeling based on remote sensing and a novel deep learning algorithm of a cascade-parallel recurrent neural network. Sensors, 20(6), 1576. https://doi.org/10.3390/s20061576
Zhu, W., Beroza, G. C., & Ross, Z. E. (2019). PhaseNet: A deep-neural-network-based seismic arrival-time picking method. Geophysical Journal International, 216(1), 261–273. https://doi.org/10.1093/gji/ggy423
Zou, Q., Jiang, H., Dai, K., Yao, Y., Chen, L., & Yao, D. (2019). A new approach to assess landslide susceptibility based on slope failure mechanisms in susceptibility model comparison in southeastern China. Catena, 182, 104090. https://doi.org/10.1016/j.catena.2019.104090
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