Title: Neural Network Model for Automated Prediction of Avalanche Danger Level

Scientific Significance:

• Research work is contributing for better understanding of Snow avalanches prediction through neural network.
• Sufficient size of data set is used.

Scientific Quality:

• Valid methods used for avalanche prediction.
• Proposed Neural Network model NNM-1 is useful for other researchers to apply for other data set.
• Appropriate references have been used.

Presentation Quality:

• In paper a clear presentation is given for data used, results and conclusions.
• Well written and structured paper.
• Quality of figures/tables is good.
• Appropriate words are used technical description.

Summary of the review of the manuscript

The following main points highlight the strength of the paper:

• Authors have proposed a neural networkmodel for predicting avalanches, a new approach on the available good quality of data.
• A mathematical model based on past data record is trained with a quality-controlled sub-dataset of Swiss Alps to predict the avalanches danger level.
• The model Training accuracy of 79.75% and validation accuracy of 76.54% have been achieved, which is quite significant.

Introduce the problem clearly and well written in structured manner:

• Accurate prediction of snow avalanches can help ensure people's safety in snow-covered regions.
• The major hurdle in developing machine learning models is the lack of sufficient and reliable data. This issue has been resolvedto a great extent by the WSL Institute of Snow and Avalanche Research, Switzerland, by collecting 20 years of data inavalanche forecasting.
• The data set has been further refined with data selection techniques.
• The dataset combines differentfeature 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.

Analyses of the data have been done for prediction purpose:

The dataset used for the training ofneural networks is described well. Authors explained the neural network model, tuning of hyper parameters and evaluation metrics. Random Forest machine learning method details applied to the same dataset are described.

Relevant papers are cited. Following two more references may be added in the paper which are using different techniques for snow avalanche. 

• Amreek Singh, AshwagoshaGanju, 2008. Artificial Neural Networks for Snow Avalanche Forecasting inIndian Himalaya. “The 12th International Conference ofInternational Association for Computer Methods and Advances in Geomechanics (IACMAG)1-6 October, 2008Goa, India.
• Singh, A., Srinivasan, K. and Ganju, A. 2005. Avalanche Forecast Using Numerical Weather Prediction in Indian Himalaya,Cold Regions Science and Technology, Vol. 43, 83-92.

On the basis of mentioned facts and importance of the topic I recommend this work for publication.

The authors have presented a work entitled:Neural Network Model for Automated Prediction of Avalanche Danger Level. It is an interesting and is very much relevent to scenarios whre avalanches are a common phenomena.

The paper is well written and well explained and shows an interesting application of AI. The methodology is clearly explaned and the numrical results justify the potential of the present work.

Some minor suggestions to the authors are to add a few more recent references relevant to the present work. It will also be good f the authors can write a word or two about the complexity of data used in the present work. They can mention if the data acquired needed some pre-processing and if the answer is es than what type of pre-rocessing was used.