Event-based decision support algorithm for real-time flood forecasting in urban drainage systems using machine learning modelling

Urban flooding is a major problem for cities around the world, with significant socio-economic consequences. Conventional real-time flood forecasting models rely on continuous time-series data and often have limited accuracy, especially for longer lead times than 2 hrs. This study proposes a novel event-based decision support algorithm for...

Detection of water quality failure events at treatment works using a hybrid two-stage method with CUSUM and random forest algorithms

Near-real-time event detection is crucial for water utilities to be able to detect failure events in their water treatment works (WTW) quickly and efficiently. This paper presents a new method for an automated, near-real-time recognition of failure events at WTWs by the application of combined statistical process control and machine-learning...

Interactive decision support methodology for near real-time response to failure events in a water distribution network

The present study proposes a new interactive methodology and an interactive tool for the response to water network failure events facilitating near real-time decision-making. The proposed methodology considers: (i) a structured yet flexible approach supporting and guiding the operator throughout the entire response process to water network...