Articles | Volume 24, issue 6
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

Learning in an interactive simulation tool against landslide risks: the role of strength and availability of experiential feedback
Pratik Chaturvedi, Akshit Arora, and Varun Dutt
Nat. Hazards Earth Syst. Sci., 18, 1599–1616,,, 2018
Short summary

Related subject area

Landslides and Debris Flows Hazards
Assessing the impact of climate change on landslides near Vejle, Denmark, using public data
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci., 24, 1897–1911,,, 2024
Short summary
Analysis of three-dimensional slope stability combined with rainfall and earthquake
Jiao Wang, Zhangxing Wang, Guanhua Sun, and Hongming Luo
Nat. Hazards Earth Syst. Sci., 24, 1741–1756,,, 2024
Short summary
Assessing landslide damming susceptibility in Central Asia
Carlo Tacconi Stefanelli, William Frodella, Francesco Caleca, Zhanar Raimbekova, Ruslan Umaraliev, and Veronica Tofani
Nat. Hazards Earth Syst. Sci., 24, 1697–1720,,, 2024
Short summary
Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico
Rex L. Baum, Dianne L. Brien, Mark E. Reid, William H. Schulz, and Matthew J. Tello
Nat. Hazards Earth Syst. Sci., 24, 1579–1605,,, 2024
Short summary
Evaluation of debris-flow building damage forecasts
Katherine R. Barnhart, Christopher R. Miller, Francis K. Rengers, and Jason W. Kean
Nat. Hazards Earth Syst. Sci., 24, 1459–1483,,, 2024
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,, 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. 
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.
Final-revised paper