Articles | Volume 24, issue 6
https://doi.org/10.5194/nhess-24-1913-2024
https://doi.org/10.5194/nhess-24-1913-2024
Research article
 | 
06 Jun 2024
Research article |  | 06 Jun 2024

Addressing class imbalance in soil movement predictions

Praveen Kumar, Priyanka Priyanka, Kala Venkata Uday, and Varun Dutt

Related authors

An Ensemble Random Forest Model for Seismic Energy Forecast
Sukh Sagar Shukla, Jaya Dhanya, Praveen Kumar, Priyanka, and Varun Dutt
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-129,https://doi.org/10.5194/nhess-2024-129, 2024
Preprint under review for NHESS
Short summary

Related subject area

Landslides and Debris Flows Hazards
Landslide activation during deglaciation in a fjord-dominated landscape: observations from southern Alaska (1984–2022)
Jane Walden, Mylène Jacquemart, Bretwood Higman, Romain Hugonnet, Andrea Manconi, and Daniel Farinotti
Nat. Hazards Earth Syst. Sci., 25, 2045–2073, https://doi.org/10.5194/nhess-25-2045-2025,https://doi.org/10.5194/nhess-25-2045-2025, 2025
Short summary
Brief communication: Weak correlation between building damage and loss of life from landslides
Maximillian Van Wyk de Vries, Alexandre Dunant, Amy L. Johnson, Erin L. Harvey, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Simon J. Dadson, Alexander L. Densmore, Tek Bahadur Dong, Mark E. Kincey, Katie Oven, Anuradha Puri, and Nick J. Rosser
Nat. Hazards Earth Syst. Sci., 25, 1937–1942, https://doi.org/10.5194/nhess-25-1937-2025,https://doi.org/10.5194/nhess-25-1937-2025, 2025
Short summary
Comparative analysis of μ(I) and Voellmy-type grain flow rheologies in geophysical mass flows: insights from theoretical and real case studies
Yu Zhuang, Brian W. McArdell, and Perry Bartelt
Nat. Hazards Earth Syst. Sci., 25, 1901–1912, https://doi.org/10.5194/nhess-25-1901-2025,https://doi.org/10.5194/nhess-25-1901-2025, 2025
Short summary
Exploring implications of input parameter uncertainties in glacial lake outburst flood (GLOF) modelling results using the modelling code r.avaflow
Sonam Rinzin, Stuart Dunning, Rachel Joanne Carr, Ashim Sattar, and Martin Mergili
Nat. Hazards Earth Syst. Sci., 25, 1841–1864, https://doi.org/10.5194/nhess-25-1841-2025,https://doi.org/10.5194/nhess-25-1841-2025, 2025
Short summary
From rockfall source area identification to susceptibility zonation: a proposed workflow tested on El Hierro (Canary Islands, Spain)
Roberto Sarro, Mauro Rossi, Paola Reichenbach, and Rosa María Mateos
Nat. Hazards Earth Syst. Sci., 25, 1459–1479, https://doi.org/10.5194/nhess-25-1459-2025,https://doi.org/10.5194/nhess-25-1459-2025, 2025
Short summary

Cited articles

Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. 
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P.: SMOTE: synthetic minority over-sampling technique, J. Artif. Intell. Res., 16, 321–357, 2002. 
Chen, T. and Guestrin, C.: Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, 13–17 August 2016, 785–794, https://doi.org/10.1145/2939672.2939785, 2016. 
Crosta, G.: Regionalization of rainfall thresholds: an aid to landslide hazard evaluation, Environ. Geol., 35, 131–145, 1998. 
Douzas, G., Bacao, F., and Last, F.: Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE, Inform. Sciences, 465, 1–20, 2018. 
Download
Short summary
Our study focuses on predicting soil movement to mitigate landslide risks. We develop machine learning models with oversampling techniques to address the class imbalance in monitoring data. The dynamic ensemble model with K-means SMOTE (synthetic minority oversampling technique) achieves high precision, high recall, and a high F1 score. Our findings highlight the potential of these models with oversampling techniques to improve soil movement predictions in landslide-prone areas.
Share
Altmetrics
Final-revised paper
Preprint