Track 1: AI and Data-Driven Decision Making

Hill, P., Biggs, J., Ponce‐López, V., & Bull, D. (2021). Time‐Series Prediction Approaches to Forecasting Deformation in Sentinel‐1 InSAR Data. Journal of Geophysical Research: Solid Earth, 126(3). https://doi.org/10.1029/2020JB020176 LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539 Liu, X., Li, Z., Sun, L., Khailah, E. Y., Wang, J., & Lu, W. (2023). A critical review of statistical model of dam monitoring data. Journal of Building Engineering, 80, 108106. https://doi.org/10.1016/j.jobe.2023.108106 Lumbroso, D., McElroy, C., Goff, C., Collell, M. R., Petkovsek, G., & Wetton, M. (2019). The potential to reduce the risks posed by tailings dams using satellite-based information. International Journal of Disaster Risk Reduction, 38, 101209. https://doi.org/10.1016/j.ijdrr.2019.101209 Massonnet, D., & Feigl, K. L. (1998). Radar interferometry and its application to changes in the Earth’s surface. Reviews of Geophysics, 36(4), 441–500. https://doi.org/10.1029/97RG03139 Nava, L., Carraro, E., Reyes-Carmona, C., Puliero, S., Bhuyan, K., Rosi, A., Monserrat, O., Floris, M., Meena, S. R., Galve, J. P., & Catani, F. (2023). Landslide displacement forecasting using deep learning and monitoring data across selected sites. Landslides, 20(10), 2111–2129. https://doi.org/10.1007/s10346-023-02104-9 Rana, N. M., Delaney, K. B., Evans, S. G., Deane, E., Small, A., Adria, D. A. M., McDougall, S., Ghahramani, N., & Take, W. A. (2024). Application of Sentinel-1 InSAR to monitor tailings dams and predict geotechnical instability: practical considerations based on case study insights. Bulletin of Engineering Geology and the Environment, 83(5), 204. https://doi.org/10.1007/s10064-024-03680-3 Rico, M., Benito, G., Salgueiro, A. R., Díez-Herrero, A., & Pereira, H. G. (2008). Reported tailings dam failures. Journal of Hazardous Materials, 152(2), 846–852. https://doi.org/10.1016/j.jhazmat.2007.07.050 Rosen, P. A., Hensley, S., Joughin, I. R., Li, F. K., Madsen, S. N., Rodriguez, E., & Goldstein, R. M. (2000). Synthetic aperture radar interferometry. Proceedings of the IEEE, 88(3), 333–382. https://doi.org/10.1109/5.838084 Sánchez, V., Cabrera-Torres, F., Arciniegas, S., Okada, N., Ohtomo, Y., Suorineni, F., & Kawamura, Y. (2026). Monitoring tailings storage facilities with multi-temporal DInSAR: A systematic review. Science of The Total Environment, 1011, 181161. https://doi.org/10.1016/j.scitotenv.2025.181161 Silva Rotta, L. H., Alcântara, E., Park, E., Negri, R. G., Lin, Y. N., Bernardo, N., Mendes, T. S. G., & Souza Filho, C. R. (2020). The 2019 Brumadinho tailings dam collapse: Possible cause and impacts of the worst human and environmental disaster in Brazil. International Journal of Applied Earth Observation and Geoinformation, 90, 102119. https://doi.org/10.1016/j.jag.2020.102119 Yang, D., Gu, C., Zhu, Y., Dai, B., Zhang, K., Zhang, Z., & Li, B. (2020). A Concrete Dam Deformation Prediction Method Based on LSTM With Attention Mechanism. IEEE Access, 8, 185177–185186. https://doi.org/10.1109/ACCESS.2020.3029562 Yunjun, Z., Fattahi, H., & Amelung, F. (2019). Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction. Computers & Geosciences, 133, 104331. https://doi.org/10.1016/j.cageo.2019.104331

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