- UHRA Home
- Browsing by Author
Browsing by Author "C. Campos, Luiza"
Now showing items 1-9 of 9
-
Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review
Kavianpour, Babak; Piadeh, Farzad; Gheibi, Mohammad; Ardakanian, Atiyeh; Behzadian, Kourosh; C. Campos, Luiza (2024-11-30)Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-based epidemiology, and advancing environmental toxicology research. In ... -
Comprehensive Flood Early Warning Systems: From Modelling to Policy Making Perspectives
Behzadian, Kourosh; Piadeh, Farzad; Razavi, Saman; C. Campos, Luiza; Gheibi, Mohamad; S. Chen, Albert (2024-04-19)Todays, early warning systems are widely applied in real-time flood forecasting operations as valuable non-structural tools for mitigating the impacts of floods [1]. Although many research works have perfectly could review ... -
A critical review of digital technology innovations for early warning of water-related disease outbreaks associated with climatic hazards
Girotto, Cristiane; Piadeh, Farzad; Bakhtiari, Vahid; Behzadian, Kourosh; S. Chen, Albert; C. Campos, Luiza; Zolgharni, Massoud (2024-01-01)Water-related climatic disasters pose a significant threat to human health due to the potential of disease outbreaks, which are exacerbated by climate change. Therefore, it is crucial to predict their occurrence with ... -
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 ... -
Machine learning models for stream-level predictions using readings from satellite and ground gauging stations
Girotto, Cristiane; Piadeh, Farzad; Behzadian, Kourosh; Zolgharni, Massoud; C. Campos, Luiza; S. Chen, Albert (2024-04-19)While the accuracy of flood predictions is likely to improve with increasing gauging station networks and robust radar coverage, challenges arise when such sources are spatially limited [1]. For instance, severe rainfall ... -
Optimising oceanic rainfall estimates for increased lead time of stream level forecasting: A case study of GPM IMERG estimates application in the UK
Girotto, Cristiane; Piadeh, Farzad; Behzadian, Kourosh; Zolgharni, Massoud; C. Campos, Luiza; S. Chen, Albert (2024-04-19)Among the three main rainfall data sources (rain gauge stations, rainfall radar stations and weather satellites), satellites are often the most appropriate for longer lead times in real-time flood forecasting [1]. This is ... -
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 ... -
Real-time operation of municipal anaerobic digestion using an ensemble data mining framework
Piadeh, Farzad; Offie, Ikechukwu; Behzadian, Kourosh; Bywater, Angela; C. Campos, Luiza (2023-11-13)This study presents a novel approach for real-time operation of anaerobic digestion using an ensemble decision-making framework composed of weak learner data mining models. The framework utilises simple but practical ... -
Using Ensemble Data Mining Modelling for Nonbinary Overflow Detection in Urban Flooding
Behzadian, Kourosh; Piadeh, Farzad; S. Chen, Albert; C. Campos, Luiza; Kapelan, Zoran (2023-04-28)Application of data-driven modelling especially using data mining techniques in flood warning systems has received significant attention recently due mainly to its well-explored sustainable solution for alleviating disruptive ...