Thesis on avalanche prediction using One Class SVM I'm doing my thesis on avalanche prediction using machine learning.

For my input features I'm using avalanche accidents with features like slope, altitude, facing direction of the slope, combined with according weather data from the day the avalanche occurred. 

I want to predict an avalanche when certain variables combine and create a deadly avalanche situation. So 1: the avalanche occurs. 0: The avalanche does not occur.

The only data in my database are occurred avalanches, I got around 200 samples. So I don't have any data of a non deadly avalanche situation, which is mostly the case.

My question is if a One Class SVM is a good approach to take on this classification?
Your problem seems to belong to novelty detection in the general area of OCC problems.

So, the short answer is: yes. You can apply SVDD (Support Vector Data Description) method to get the smallest hypersphere containing samples in the dataset and then assess whether a new observation is an outlier or not.

Of course, the less representative your dataset is, the less accurate your classifier will be.