Proprietary software capable of performing 1D/2D hydraulic modeling often presents a significant financial barrier for individuals, researchers, and public institutions, limiting accessibility and hindering hydrological studies. To overcome this challenge, we have developed Digital RAIN (DRAIN), an open-source QGIS plugin designed to facilitate 1D/2D hydraulic modeling in urban environments.

DRAIN leverages the combined strengths of EPA SWMM and Iber, two widely used hydraulic modeling tools, to simulate rainfall events and their impact on drainage systems. The tool provides detailed simulation results, including calculated hydrodynamic parameters for each element of the hydraulic infrastructure (processed by SWMM) and high-resolution raster outputs that depict accumulated rainfall and surface flow over time (generated by Iber).

At the core of DRAIN, a GeoPackage-based data model ensures seamless integration between Iber and SWMM, incorporating triggers and constraints to maintain data consistency and accuracy. The plugin also features an intuitive graphical interface with interactive GIS tools, allowing users to efficiently manage spatial data, define modeling parameters, and execute simulations directly within the QGIS environment.

By offering a free, open-source alternative to proprietary hydraulic modeling software, DRAIN promotes accessibility, reproducibility, and innovation in urban hydrology studies. This tool has the potential to support decision-making processes related to flood risk management, stormwater infrastructure design, and climate resilience planning in urban areas.

Giswater is an open-source software platform available as a plugin in QGIS that bridges the GIS with relational databases for the management of water-related structures. It is specifically designed to manage water supply networks, and urban drainage systems and integrate GIS with hydraulic modelling tools like EPANET for water supply networks and SWMM for stormwater and sewer systems, enabling simulations directly from QGIS. It also incorporates inventory management through PostgreSQL Database systems.

The design and operation of urban sanitation and drainage networks have always been largely overlooked in the integral water cycle. In this sense, Geographic Information Systems (GIS) and mathematical models for networks play a key role in both the design and exploitation phases. This project demonstrates the viability of carrying out a long-term strategy for asset management of urban sanitation and drainage networks with the use of open-source technologies. In the design phase, a complete analysis of the terrain can be carried out along with sizing and selection of materials, and design of auxiliary structures of network elements. In the exploitation phase, this set of technologies working in solidarity will allow us to efficiently work on the activities and processes necessary to maintain and operate the systems efficiently and effectively. This includes preventive and corrective maintenance, monitoring and control, emergency management, or resource optimization ensuring seamless operations. This project demonstrates the possibility of carrying out all the work professionally stated with open-source technologies, which opens the door to the universalization of urban sanitation management, regardless of the degree of maturity or available capital since access to technology has become possible in this regard.

Pipe leaks are a significant concern for water companies responsible for managing water infrastructures. In this context, anticipating these events is crucial not only for conserving water but also for ensuring that the infrastructure remains in optimal condition.

The FLUENT project presents a unique opportunity for water companies to study the probability of pipe leaks using artificial intelligence (AI), Linear Extended Yule 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 companies also collaborated to establish common definitions of key concepts and to share valuable knowledge on how to tackle the challenges associated with leak detection and prevention.

The collected data was stored in a PostgreSQL database, and was processed using PL/pgSQL and PostGIS functions, allowing for efficient data manipulation and preparation before being used by the AI algorithms outside the database. This collaborative approach not only aims to improve the accuracy of leak predictions but also seeks to provide practical solutions to enhance infrastructure management and promote more sustainable water usage practices. By leveraging AI in this way, the project strives to advance the capabilities of water companies in addressing one of the most pressing challenges in water distribution networks today.