Early Avalanche Detection Using Ensemble Learning Techniques

Avalanches are caused by changes in the balance of snowpack and present significant dangers to ecosystems and human communities. This study evaluates the efficiency of machine learning algorithms in predicting avalanches, with a focus on selecting features and evaluating models. Key elements such as snow depth, precipitation, temperature, and avalanche probabilities are integrated into the modeling process and further improved through correlation analysis. Performance indicators that includes accuracy, sensitivity, precision, etc, are utilized to assess the effectiveness of classifiers. Ensemble learners such as Random Forest (RF) and Gradient Boosting (GB), together with more recent methods like eXtreme Gradient Boosting (XGBoost)), can improve the ability of prediction. Feature engineering enhances model development by employing techniques such as class balancing and grid search to optimize performance. Evaluation entails comparing models like random forest, gradient boosting, and neural networks with variables such as slab, wet, and sum. Metrics including accuracy, recall, precision, Fl-score, and ROC curve are utilized for this purpose. The XGBoost technique enhances the performance of the gradient boosting model when compared to other factors. Future research may explore various techniques for different natural hazards, alternative hyperparameter optimization methods, and comparing probabilistic ML models. This study advances avalanche prediction understanding and highlights the potential of Machine Learning algorithms in disaster risk mitigation.