A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
Smart water resource management using Artificial Intelligence—A review
Water management is one of the crucial topics discussed in most of the international forums.
Water harvesting and recycling are the major requirements to meet the global upcoming …
Water harvesting and recycling are the major requirements to meet the global upcoming …
[HTML][HTML] Exploding the myths: An introduction to artificial neural networks for prediction and forecasting
Abstract Artificial Neural Networks (ANNs), sometimes also called models for deep learning,
are used extensively for the prediction of a range of environmental variables. While the …
are used extensively for the prediction of a range of environmental variables. While the …
Application of machine learning in water resources management: A systematic literature review
In accordance with the rapid proliferation of machine learning (ML) and data management,
ML applications have evolved to encompass all engineering disciplines. Owing to the …
ML applications have evolved to encompass all engineering disciplines. Owing to the …
Real-time water quality prediction in water distribution networks using graph neural networks with sparse monitoring data
Ensuring the safety and reliability of drinking water supply requires accurate prediction of
water quality in water distribution networks (WDNs). However, existing hydraulic model …
water quality in water distribution networks (WDNs). However, existing hydraulic model …
Micro hydro power generation in water distribution networks through the optimal pumps-as-turbines sizing and control
Sustainable use of water and energy sources is a crucial challenge for smart and resilient
urban water infrastructure. At this aim, this study presents a methodology for implementing …
urban water infrastructure. At this aim, this study presents a methodology for implementing …
Shall we always use hydraulic models? A graph neural network metamodel for water system calibration and uncertainty assessment
Representing reality in a numerical model is complex. Conventionally, hydraulic models of
water distribution networks are a tool for replicating water supply system behaviour through …
water distribution networks are a tool for replicating water supply system behaviour through …
Predicting the urban stormwater drainage system state using the Graph-WaveNet
Abstract Graph Neural Networks (GNNs) have been applied to network data such as traffic
flow and water distribution systems, yet their use in predicting the state of urban stormwater …
flow and water distribution systems, yet their use in predicting the state of urban stormwater …
A short-term water demand forecasting model using multivariate long short-term memory with meteorological data
Sustainable management of water resources is a key challenge nowadays and in the future.
Water distribution systems have to ensure fresh water for all users in an increasing demand …
Water distribution systems have to ensure fresh water for all users in an increasing demand …
Graph neural networks for pressure estimation in water distribution systems
Pressure and flow estimation in water distribution networks (WDNs) allows water
management companies to optimize their control operations. For many years, mathematical …
management companies to optimize their control operations. For many years, mathematical …