Estudo de caso para predição de falhas em redes de abastecimento de água utilizando Redes Neurais

Abstract

The water supply is considered an important service for cities because provides an essential asset for the lives of its inhabitants, as for the proper functioning of industries, institutions, and harming the social well-being of people. Faults in water supply systems can considerably affect people’s lives with losses and damages, in addition to causing damage to the economy of entire regions. Proposing solutions that optimize the water supply process, reducing losses and improving the quality of the service offered can aggregate value to people, companies, governments and cities. This work aims to investigate and propose solutions for the prevention of failures in water distribution networks based on Predictive Analysis and Machine Learning techniques, using real data from a water supply and sewage concessionaire.