Towards a method to predict possible obstructions in a sewage system: A case study applied in the Aburrá Valley, Colombia

This article presents a predictive model to determine possible obstructions in the sewerage system of the Valle de Aburrá metropolitan area in Colombia. This city has special characteristics for being located in a valley surrounded by high mountains, susceptible to rainfall almost all year round and with a population of over 4 million inhabitants. To achieve this, we apply the CRISP-DM methodology. Initially, the way how the system determines the obstructions through preventive and corrective maintenance was identified. Subsequently, the data provided by the main assets that make up the system, especially the spillways, were understood; then, the dynamic and exogenous variables that could influence the occurrence of obstructions were selected. A case study with a dataset of 3980 records with information between the years 2018 - 2021, facilitated the application of feature selection techniques to analyze the most influential variables, and the predictive model was developed to identify possible obstructions in the system. We use algorithms such as neural networks, decision trees, random forests, logistic regression, and support vector machines. The results of the neural networks yielded an accuracy of 84% that was calculated with the cross-validation method and an F1 Score of 70%. In the evaluation, the model showed that it was determined by the variables number of previous obstructions, density of afferent clients, previous maintenance, and the average rainfall of the last days. These results expand the decision-making capacity to define preventive maintenance and reduce corrective maintenance in the spillways that make up the sewerage system. Finally, it is concluded that the model can correctly predict the possible obstructions, however, it is necessary to deploy it in the complete system so that the prediction increases its level of accuracy.