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Snow avalanches cause danger to human lives and property worldwide in high-altitude mountainous regions. Mathematical models based on past data records can predict the danger level. In this paper, we are proposing a neural network model for predicting avalanches. The model is trained with a quality-controlled sub-dataset of Swiss Alps. Training accuracy of 79.75% and validation accuracy of 76.54% have been achieved. Comparative analysis of neural network and random forest models concerning metrics like precision, recall, and F1 has also been carried out. 1. Introduction Accurate prediction of snow avalanches can help ensure people's safety in snow-covered regions. Many countries still depend on human experts to analyse meteorological data to forecast avalanche warnings. The major hurdle in developing machine learning models is the lack of sufficient and reliable data. This issue has been resolved to a great extent by the WSL Institute of Snow and Avalanche Research, Switzerland, by collecting 20 years of data in avalanche forecasting. This data set has been further refined with quality control by experts. The dataset combines different feature sets with meteorological variables. This unique dataset has enabled experimentation with machine learning models like neural networks and compared its performance with the random forest machine learning technique. This paper is organized as follows. Related literature is briefly overviewed in Section II. The dataset used for the training of neural networks is described in Section III. After that, in Section IV, we explain the neural network model, tuning of hyperparameters, and evaluation metrics. Random Forest machine learning method details applied to the same dataset are described in Section V. Results from both methods are compared and analysed in Section VI. The paper is concluded in Section VII. 2. Related Work Many countries face snow avalanche hazards with snow-clad mountains. It affects people, facilities, and properties. The impact of snow avalanches on living, work, and recreation in Canada is well documented (Sethem et. al., 2003). Every country