Snow depth measurements and predictions : Reducing environmental impact for artificial grass pitches at snowfall

Abstract: Rubber granulates, used at artificial grass pitches, pose a threat to the environment when leaking into the nature. As the granulates leak to the environment through rain water and snow clearances, they can be transported by rivers and later on end up in the marine life. Therefore, reducing the snow clearances to its minimum is of importance. If the snow clearance problem is minimized or even eliminated, this will have a positive impact on the surrounding nature. The object of this project is to propose a method for deciding when to remove snow and automate the information dispersing upon clearing or closing a pitch. This includes finding low powered sensors to measure snow depth, find a machine learning model to predict upcoming snow levels and create an application with a clear and easy-to-use interface to present weather information and disperse information to the responsible persons. Controlled experiments is used to find the models and sensors that are suitable to solve this problem. The sensors are tested on a single snow quality, where ultrasonic and infrared sensors are found suitable. However, fabricated tests for newly fallen snow questioned the possibility of measuring snow depth using the ultrasonic sensor in the general case. Random Forest is presented as the machine learning model that predicts future snow levels with the highest accuracy. From a survey, indications is found that the web application fulfills the intended functionalities, with some improvements suggested.