Landslide Hazard in the Nainital township, Kumaun Himalaya, India: the case of September 2014 Balia Nala landslide

Gupta, Vikram; Bhasin, Rajinder; Kaynia, Amir M.; Tandon, Ruchika Sharma; Venkateshwarlu, B.

doi:10.1007/s11069-015-2002-5

Cited by 33 publications

(1 citation statement)

References 7 publications

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“…These are covered with a thin veneer of debris, mainly consisting of pebble-and cobble-sized limestone, sandstone, and slate embedded in a sand-siltclay matrix. Additional context includes an annual rainfall of 4190 mm in the area, as reported by Gupta et al (2016). Spanning a duration of 2 years, from June 2019 to June 2021, this dataset holds valuable insights into the behavior of soil in response to various environmental factors.…”

Section: Introductionmentioning

confidence: 99%

Addressing class imbalance in soil movement predictions

Kumar,

Priyanka,

Uday

et al. 2024

Nat. Hazards Earth Syst. Sci.

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Abstract. Landslides threaten human life and infrastructure, resulting in fatalities and economic losses. Monitoring stations provide valuable data for predicting soil movement, which is crucial in mitigating this threat. Accurately predicting soil movement from monitoring data is challenging due to its complexity and inherent class imbalance. This study proposes developing machine learning (ML) models with oversampling techniques to address the class imbalance issue and develop a robust soil movement prediction system. The dataset, comprising 2 years (2019–2021) of monitoring data from a landslide in Uttarakhand, has a 70:30 ratio of training and testing data. To tackle the class imbalance problem, various oversampling techniques, including the synthetic minority oversampling technique (SMOTE), K-means SMOTE, borderline-SMOTE, and adaptive SMOTE (ADASYN), were applied to the training dataset. Several ML models, namely random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), adaptive boosting (AdaBoost), category boosting (CatBoost), long short-term memory (LSTM), multilayer perceptron (MLP), and a dynamic ensemble, were trained and compared for soil movement prediction. A 5-fold cross-validation method was applied to optimize the ML models on the training data, and the models were tested on the testing set. Among these ML models, the dynamic ensemble model with K-means SMOTE performed the best in testing, with an accuracy, precision, and recall rate of 0.995, 0.995, and 0.995, respectively, and an F1 score of 0.995. Additionally, models without oversampling exhibited poor performance in training and testing, highlighting the importance of incorporating oversampling techniques to enhance predictive capabilities.

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Section: Introductionmentioning

confidence: 99%