Sediment Level Prediction of a Combined Sewer System Using Spatial Features

The prediction of sediment levels in combined sewer system (CSS) would result in enormous savings in resources for their maintenance as a reduced number of inspections would be needed. In this paper, we benchmark different machine learning (ML) methodologies to improve the maintenance schedules of the sewerage and reduce the number of cleanings using historical sediment level and inspection data of the combined sewer system in the city of Barcelona. Two ML methodologies involve the use of spatial features for sediment prediction at critical sections of the sewer, where the cost of maintenance is high because of the dangerous access; one uses a regression model to predict the sediment level of a section, and the other one a binary classification model to identify whether or not a section needs cleaning. The last ML methodology is a short-term forecast of the possible sediment level in future days to improve the ability of operators to react and solve an imminent sediment level increase. Our study concludes with three different models. The spatial and short-term regression methodologies accomplished the best results with Artificial Neural Networks (ANN) with 0.76 and 0.61 R2 scores, respectively. The classification methodology resulted in a Gradient Boosting (GB) model with an accuracy score of 0.88 and an area under the curve (AUC) of 0.909.The work described in this paper has been conducted within the project SCOREwater. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no 820751. R. Béjar also acknowledges funding from project PID2019-111544GB-C22 from the Spanish Government. C. Mateu also acknowledges funding from project RTI2018-093849-B-C31 from the Spanish Government. Lluís Corominas acknowledges the Ministry of Economy and competitiveness for the Ramon and Cajal grant and the corresponding I3 consolidation (RYC-2013-465 14595). ICRA researchers thank funding from the CERCA program and from the Generalitat de Catalunya through the Consolidated Research Group 2017 SGR 1318. Marc Ribalta also acknowledges funding from AGAUR DI-2019-066

PANGEA – Platform for Automatic coNstruction of orGanizations of intElligent Agents

This article presents PANGEA, an agent platform to develop open multiagent systems, specifically those including organizational aspects such as virtual agent organizations. The platform allows the integral management of organizations and offers tools to the end user. Additionally, it includes a communication protocol based on the IRC standard, which facilitates implementation and remains robust even with a large number of connections. The introduction of a CommunicationAgent and a Sniffer make it possible to offer web services for the distributed control of interaction

Proximity Detection Prototype Adapted to a Work Environment

This article presents a proximity detection prototype that uses ZigBee technology. The prototype is primarily oriented to proximity detection within an office environment and some of the particular characteristics specific to such an environment, including the integration of people with disabilities into the workplace. This allows the system to define and manage the different profiles of people with disabilities, facilitating their job assimilation by automatically switching on or off the computer upon detecting the user’s presence, or initiating a procedure that automatically adapts the computer to the personal needs of the user

Detección de los movimientos de la cara de un usuario delante de la pantalla de un ordenador

Estudio de diferentes alternativas para detectar el movimiento de la cara de un usuario delante de la pantalla de un ordenador. Realización de una aplicación capaz de desplazar el mouse de forma relativa y realizar clicks de una forma sencilla mediante la cabeza del usuario con una webcam de bajo coste