The application of statistical and Machine Learning models plays a critical role in planning and decision support processes for efficient and reliable Water Distribution Network (WDN) management. Failure models can provide valuable information for prioritizing system rehabilitation even in data scarcity scenarios, such as developing countries. Few studies have analyzed the performance of more than two models, and examples of case studies in developing countries are insufficient. This study compares various statistical and Machine Learning models to provide useful information to practitioners for the selection of a suitable pipe failure model according to information availability and network characteristics. Three statistical models (i.e., Linear, Poisson, and Evolutionary Polynomial Regressions) were used for failure prediction in groups of pipes. Machine Learning approaches, particularly Gradient-Boosted Tree (GBT), Bayes, Support Vector Machines and Artificial Neuronal Networks (ANNs), were compared in predicting individual pipe failure rates. The proposed approach was applied to a WDN in Bogotá (Colombia). The statistical models showed an acceptable performance (R² between 0.695 and 0.927), but the Poisson Regression was the most suitable for predicting failures in pipes with lower failure rates. Regarding Machine Learning models, Bayes and ANNs exhibited low performance in the prediction of pipe failure condition. The GBT approach had the best performing classifier.

In the past years, alternative drainage approaches have emerged, such as Sustainable Urban Drainage Systems (SUDS), to prevent run-off and flooding impacts on the most vulnerable zones of the cities. These systems not only provide the benefit of water regulation but also promote other types of ecosystem services. Several studies have developed optimization tools to assist SUDS selection, location, and design. However, they do not consider a comprehensive set of ecosystem services (e.g., provision, regulation, cultural, and support services). This research proposes a flexible and adaptable methodology to incorporate SUDS in different stages of urban projects using a multi-objective optimization technique to minimize run-off, maximize ecosystem services and minimize cost. The methodology comprises four phases: (1) the preliminary analysis of ecosystem services potentially generated by each SUDS type, (2) the priority and opportunity index quantification, (3) the physical feasibility analysis, and (4) the multi-objective optimization tool implementation. The methodology was successfully applied to three different urban areas of Bogotá city (Colombia). Results evidence that the interaction of the budget constraints and the available area restrict the potential benefits of SUDS implementation. These results are helpful to support different urban planning stages.

Pipe failures in Water Distribution Networks (WDN) may cause economic, environmental and social costs. The application of statistical and Machine Learning (ML) models play a critical role in planning and decision support processes for WDN management. Failure models can provide valuable information for prioritizing the system rehabilitation even in data scarcity scenarios (such as developing countries). This study compares several statistical and ML pipe failure models thus providing useful information to practitioners to select a suitable model according to their needs. Three statistical models (i.e. Linear, Poisson and Evolutionary Polynomial Regressions) were used for pipe failures prediction based on diameter, age of pipes and length as explanatory variables. The K-means clustering approach was applied to improve the performance of the statistical models. The performance indicators used were the coefficient of determination (R2) and the root mean square error (RMSE). ML approaches - namely Gradient Boosted Tree (GBT), Bayes, Support Vector Machine and Artificial Neuronal Networks (ANNs) - were compared in predicting individual pipe failure rates. The pipe’s attributes, environmental and operational variables were included as input variables. Their performance was evaluated using confusion matrices and receiver operating characteristic curves. The proposed approach was applied to a WDN in Bogotá (Colombia). The results showed that the cluster-based prediction model reduces the prediction error of pipe failures. All the models demonstrated acceptable results in terms of their performance (R2 between 0.695-0.927 and RMSE between 45-22 for the test sample). Regarding ML models, all methods but the ANNs show acceptable performance. The GBT approach has the best performing classifier (79.41% correct predictions in the test sample). This model was used to calculate the failure rate of individual pipes for rehabilitation planning. Furthermore, a sensitivity analysis of the GBT model to the input variables was performed to provide information on its generalization capability.