Spatiotemporal Prediction of Cloudburst Vulnerability Zones in Uttarakhand Using ERA5 Reanalysis (1960–2024)
Keywords:
Cloudburst prediction, Uttarakhand, ERA5, rainfall extremes, machine learning, deep learningReferences
H. Hersbach, B. Bell, P. Berrisford, S. Hirahara, A. Hora´nyi, J. Mun˜oz- Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers et al., “The ERA5 global reanalysis,” Quarterly Journal of the Royal Meteorological Society, vol. 146, no. 730, pp. 1999–2049, 2020.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
K. Cho, B. van Merrie¨nboer, C. Gulcehre, D. Bahdanau, F. Bougares,
H. Schwenk, and Y. Bengio, “Learning phrase representations using RNN encoder–decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014.
T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 785–794.
