IMPROVING TIME TO FAILURE PREDICTIONS FOR WATER DISTRIBUTION SYSTEMS USING GRADIENT BOOSTING ALGORITHM
This paper advances the research on pipe failure modeling by applying a state of the art gradient boosting machine learning algorithm (xgboost) to a large ductile iron pipe failure dataset. The model was designed to predict the time to next failure for individual ductile iron pipes. The overall root mean square error for the xgboost model was 5.81, a 1.2% improvement over the Random Forest model and 25.9 improvement over the Artificial Neural Network model. The results from this analysis suggests that industry should consider adopting the xgboost algorithm when building machine learning models that predict time to pipe failure.