30 citations as recorded by crossref.

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10. Application of Hidden Markov Model for avalanche danger simulations for road sectors in North-West Himalaya J. Joshi et al. 10.1007/s11069-018-3343-7
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14. A multi-model decision support system (MM-DSS) for avalanche hazard prediction over North-West Himalaya P. Kaur et al. 10.1007/s11069-021-04958-5
15. Predicting Future Built-Up Land Cover from a Yearly Time Series of Satellite-Derived Binary Urban Maps F. O’Neill et al. 10.3390/land14081630
16. Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios N. Javidan et al. 10.3390/w11112319
17. Selection of Significant Features for Snow Avalanche Forecasting K. Kaushik et al. 10.1007/s10666-025-10061-x
18. Parameter importance assessment improves efficacy of machine learning methods for predicting snow avalanche sites in Leh-Manali Highway, India A. Tiwari et al. 10.1016/j.scitotenv.2021.148738
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20. Spatio-Temporal Forecasting: A Survey of Data-Driven Models Using Exogenous Data S. Berkani et al. 10.1109/ACCESS.2023.3282545
21. Addressing class imbalance in avalanche forecasting M. Kala et al. 10.1016/j.coldregions.2024.104411
22. Outlier Detection for Observational Data of Automatic Meteorological Station Based on Least Square Support Vector Machine Z. Meng et al. 10.4028/www.scientific.net/AMM.295-298.945
23. Fine assessment of tropical cyclone disasters based on GIS and SVM in Zhejiang Province, China W. Lou et al. 10.1007/s11069-012-0254-x
24. Combining random forests and class-balancing to discriminate between three classes of avalanche activity in the French Alps P. Dkengne Sielenou et al. 10.1016/j.coldregions.2021.103276
25. Heterogeneous Space–Time Artificial Neural Networks for Space–Time Series Prediction M. Deng et al. 10.1111/tgis.12302
26. Avalanches en moyenne montagne : des représentations à l’occultation du risque G. Florie et al. 10.4000/vertigo.20525
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28. Machine-Learning Algorithms for Mapping Debris-Covered Glaciers: The Hunza Basin Case Study A. Khan et al. 10.1109/ACCESS.2020.2965768
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