Purpose: This study describes the trends and applications of machine learning systems in the management of water supply networks. Machine learning is a field in constant development, and it has a great potential and capability to attain improvements in real industries. The recent tendency of data storage by companies that manage the water supply networks have created a range of possibilities to apply machine learning. One particular case is the prediction of pipe failures based on historical data, which can help to optimally plan the renovation and maintenance tasks. The objective of this work is to define the stages and main characteristics of machine learning systems, focusing on supervised learning methods. Additionally, singularities that are usually found in data from water supply networks are highlighted.Design/methodology/approach: For this purpose, eight studies which contain real cases from around the world are discussed. From the data processing to the model validation, a tour of the methods used in each study is carried out. Moreover, the trendiest models are briefly defined together with the mechanisms that best suit their performance.Findings: As a result of the study, it was found that the imbalanced class problem is typical of data from water supply networks where only a small percentage of pipes fail. Consequently, it is recommended to use sampling methods to train classifiers, however, it is not necessary if we are training a regression system. Additionally, scaling and transformation of variables has generally a positive impact on the model’s performance. Currently, cross-validation is almost a requirement to obtain reliable and representative results. This technique is employed in all revised studies to train and validate their models.Originality/value: The use of machine learning systems to predict pipe failures in water supply networks is still a developing field. This study tries to define the advantages and disadvantages of different methods to process data from water supply networks, as well as to train and validate the models.

In Spain, the water supply service is a municipal responsibility and in general is a sector without competitors. For this reason, an efficiency analysis attains greater significance. This study uses Data Envelopment Analysis (DEA) to estimate the efficiency of different urban water utilities. An extensive data search, where several variables such as the capital expenditure, the cost of material, or the labor have been recorded, has allowed evaluating the relative efficiency of the most important Spanish water distribution networks in using their resources. Furthermore, their sustainable efficiency has also been evaluated by including a variable representing the percentage of water losses. Results reveal the weaknesses of inefficient utilities and help to detect potential aspects that these companies should improve. For instance, there is an evident incorrect management of the costs of material by many urban water utilities, which does not happen with the labor. Additionally, the most efficient water utilities regarding the sustainable efficiency help to discover target percentages of water losses for the inefficient ones.

En España el abastecimiento y saneamiento del agua es competencia municipal, realizándose como gestión directa o indirecta a través de empresas públicas, privadas o mixtas. El presente trabajo recoge el análisis de las empresas que prestan este servicio en las capitales de provincia de España. De las 52 empresas analizadas, se han incluido 46 en el estudio. De estas empresas se ha estudiado el tipo (pública, privada o mixta), así como su rentabilidad económica y financiera a través de cinco ratios. También se ha analizado la tarifa del agua para consumo doméstico de dichas empresas. Para ello se ha analizado el consumo medio de un hogar en España. Se ha analizado si existe correlación entre los mejores resultados económicos y el precio del agua. Además, se ha establecido un ranking global en función de los valores obtenidos para las ratios y la tarifa. Los resultados obtenidos no inducen a pensar que la gestión pública proporciona mejores precios del agua que el sector privado, ni tampoco que obtengan mejores o peores resultados de forma generalizada en cuanto a las empresas analizadas.