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UID:pretalx-foss4g-europe-2025-3Z8WAQ@talks.staging.osgeo.org
DTSTART;TZID=CET:20250717T160000
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DESCRIPTION:Pipe leaks are a significant concern for water companies respon
 sible for managing water infrastructures. In this context\, anticipating t
 hese events is crucial not only for conserving water but also for ensuring
  that the infrastructure remains in optimal condition.\n\nThe FLUENT proje
 ct presents a unique opportunity for water companies to study the probabil
 ity of pipe leaks using artificial intelligence (AI)\, Linear Extended Yul
 e Process (LEYP) \, and logistic regression (LR) enabling them to predict 
 and proactively address potential issues. The main goal of the project is 
 to develop a predictive system for pipe leaks using advanced AI algorithms
 . To achieve this\, four water companies\, serving between 20\,000 and 100
 \,000 consumers\, contributed their data to train the AI model. These comp
 anies also collaborated to establish common definitions of key concepts an
 d to share valuable knowledge on how to tackle the challenges associated w
 ith leak detection and prevention. \n\nThe collected data was stored in a 
 PostgreSQL database\, and was processed using PL/pgSQL and PostGIS functio
 ns\, allowing for efficient data manipulation and preparation before being
  used by the AI algorithms outside the database. This collaborative approa
 ch not only aims to improve the accuracy of leak predictions but also seek
 s to provide practical solutions to enhance infrastructure management and 
 promote more sustainable water usage practices. By leveraging AI in this w
 ay\, the project strives to advance the capabilities of water companies in
  addressing one of the most pressing challenges in water distribution netw
 orks today.
DTSTAMP:20260527T222700Z
LOCATION:SA02
SUMMARY:FLUENT: prediction of pipe leaks using IA\, LEYP and log regression
 . Data pipeline using PostgreSQL - Maria Guzmán
URL:https://talks.staging.osgeo.org/foss4g-europe-2025/talk/3Z8WAQ/
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