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The 2016 Summer Olympics in Rio de Janeiro brought glaring international attention to the issue of water pollution in Brazil. This was an alarming fact for Brazilian water resources managers and highlighted the lack of a resilience-driven water management system in dealing with the rapidly growing environmental and development challenges across the country.
From a technical point of view, resolving these issues by identifying best management practices through process-based models is one option. However such modelling approaches require a great deal of data and information which are costly and time-consuming to collect and monitor. Therefore, this study focuses on utilising artificial neural network (ANN) to develop a predictive model to evaluate water quality resilience in the case study of São Carlos, São Paulo State in Brazil.
This predictive model enables decision makers and urban planners to identify vulnerable areas with low adaptive capacity and in urgent need for intervention investments to cope with emerging challenges. This model could be used as a surrogate of an actual monitoring station in ungauged areas to predict water resources self-depuration capacities and resilience.
Dr Imani is a Civil Engineer with expertise in water systems engineering. She is currently a Senior Lecturer at Anglia Ruskin University in Essex, where she has worked on different research projects on infrastructure resilience over the past few years (either as PI or Co-I).
Prior to joining Anglia Ruskin University she worked as a research engineer in the Centre for Water Systems (CWS) at the University of Exeter and was involved in different research projects. Maryam’s research interests are focused on water and wastewater infrastructure resilience/interdependencies/optimisation and also application of the Artificial Intelligences in water systems engineering.
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