Predicting the risk of accidents for downhill skiers

Student:  Marco Dallagiacoma
Date and Time:  Tuesday August 15th, 15:00
Examiner:  Šarūnas Girdzijauskas
Supervisor:  Amira Soliman El Hosary

Title: Predicting the risk of accidents for downhill skiers

Opponents: Marc Höffl - mhoffl@kth.se    Di Zhu - dzhu@kth.se

Abstract

In recent years, the need for insurance coverage for downhill skiers is becoming increasingly important. The goal of this thesis work is to enable the development of innovative insurance services for skiers. Specifically, this project addresses the problem of estimating the probability for a skier to suffer injuries while skiing.

This problem is addressed by developing and evaluating a number of machine- learning models. The models are trained on data that is commonly available to ski- resorts, namely the history of accesses to ski-lifts, reports of accidents collected by ski-patrols, and weather-related information retrieved from publicly accessible weather stations. Both personal information about skiers and environmental variables are considered to estimate the risk. Additionally, an auxiliary model is developed to estimate the condition of the snow in a ski-resort from past weather data. A number of techniques to deal with the problems related to this task, such as the class imbalance and the calibration of probabilities, are evaluated and compared.

The main contribution of this project is the implementation of machine learning models to predict the probability of accidents for downhill skiers. The obtained models achieve a satisfactory performance at estimating the risk of accidents for skiers, provided that the needed historical data for the target ski-resorts is available. The biggest limitation encountered by this study is related to the relatively low volume and quality of available data, which suggests that there are opportunities for further enhancements if additional (and especially better) data is collected.