Extension of Machine Learning Model for Dynamic Risk Analysis

Abstract: During this study a model for predicting the next week's alarm codes based on the past week's alarm codes was developed. The model used alarm data from the location and its surroundings. The model was tuned using hyper parameter optimization to improve performance, this resulted in a model performing better than previous models used on this data set. The performance when adding weather data was evaluated and it was shown that it improved the performance for some alarm codes and the performance for the majority of other alarm codes was not compromised resulting in an improvement in the overall performance. The weather data consisted of temperature, precipitation, cloud coverage, air pressure and wind direction and speed data. Two labeling methods were trialed for the weather data, the first one used the data of the closest weather station for each type of data. The second labeling method used data of the ten closest weather stations within 100 km. The final model using weather data labeled with method 2 had a precision micro average of 0.90, a recall micro average of 0.86, a precision macro average of 0.80 and a recall macro average of 0.77.