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Roisin, S,G,, Yang, M., Charpiat, G., Kegl, Balazs and Monteleoni, C. (2018). Deep learning for
Hurricane track forecasting from aligned spatio-temporal climate datasets. In: Workshop
on modeling and decision-making in the spatiotemporal domain, the 32nd conference on
neural information processing systems (NIPS 2018)
Romano, M., Liong, S.Y., Vu, M.T., Zemskyy, P., Doan, C.D., Dao, M.D., and Tkalich, R. (2009).
Artificial neural network for tsunami forecasting. Journal of Asian Earth Sciences, 36,
29-37.
Sandmael, T.N. Smith, B.S., Reinhart, A,.E., Schick, I.M., Ake, M.C., Madden, J.G., Steeves,
R.B., Williams, S.S., Elmore, K.L. and Meyer, T.C. (2023). The tornado probability
algorithm: A probabilistic machine learning tornadic circulation detection algorithm.
Weather and Forecasting, 38(3),455-466.
Soori, M., Arezoo, B. and Dastres, R. (2023). Artificial intelligence, machine learning and deep
learning in advanced robotics, a review. Cognitive Robotics, 3, 54-70.
Sunil, G., Chaithra, S. and Kudari, J.M. (2022). Prediction of volcanoes using machine learning
algorithms: A survey paper. Journal of Emerging Technologies and Innovative Research,
9(6), 363-365
Valade, S., Ley, A., Massimetti, F., Hondt, O.D., Laiolo, M., Coppola, D., Loibl, D., Hellwich,
O. and Walter, T.R. (2019). Towards global volcano monitoring using multisensory
Sentinel mission and Artificial Intelligence: The MOUNTS monitoring system. Remote
Sensing, 11, 1528
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