Browsing by Author "P. Rizzuto, Joseph"

Appliance for Water consumption management in showering by using Arduino kit, Energy NExus and SensorS : AWARENESS kit

Piadeh, Farzad; Girotto, Cristiane; Egyir, Daniel; P. Rizzuto, Joseph; Behzadian, Kourosh (2023-09-07)

Appliance for Water consumption management in showering by using Arduino kit, Energy NExus and SensorS Presentation : AWARENESS kit

Piadeh, Farzad; Girotto, Cristiane; Egyir, Daniel; P. Rizzuto, Joseph; Behzadian, Kourosh (2023-09-07)

A critical review for the impact of anaerobic digestion on the sustainable development goals

Piadeh, Farzad; Offie, Ikechukwu; Behzadian, Kourosh; P. Rizzuto, Joseph; Bywater, Angela; Córdoba-Pachón, José-Rodrigo; Walker, Mark (2024-01-01)

Anaerobic Digestion (AD) technology emerges as a viable solution for managing municipal organic waste, offering pollution reduction and the generation of biogas and fertilisers. This study reviews the research works for ...

Enhancing Urban Flood Forecasting in Drainage Systems Using Dynamic Ensemble-based Data Mining

Piadeh, Farzad; Behzadian, Kourosh; S. Chen, Albert; Kapelan, Zoran; P. Rizzuto, Joseph; C. Campos, Luiza (2023-12-01)

This study presents a novel approach for urban flood forecasting in drainage systems using a dynamic ensemble-based data mining model which has yet to be utilised properly in this context. The proposed method incorporates ...

Event-based Flood Data Imputation for Infilling Missing Data in Real-time Flood Warning Systems

Piadeh, Farzad; Behzadian, Kourosh; P. Rizzuto, Joseph (2023-04-28)

Real-time flood warning systems as part of digital and innovative non-structural solutions have been widely used to prepare decision makers, operators, and affected population to alleviate socio-economic flooding consequences ...

Real-time flood overflow forecasting in Urban Drainage Systems by using time-series multi-stacking of data mining techniques

Piadeh, Farzad; Behzadian, Kourosh; S. Chen, Albert; C. Campos, Luiza; P. Rizzuto, Joseph (2023-04-28)

Overflow forecasting in early warning systems is acknowledged as an essential task for devastating urban flood risk management. Although many machine learning models have been developed recently to forecast water levels ...