Optimizing sensor location for the parsimonious design of flood early warning systems

Flood early warning systems (FEWS) are effective means for saving human lives from the devastating impacts of extreme hydrological events. FEWS relies on hydrologic monitoring networks that are typically expensive and challenging to design. This issue is particularly relevant when identifying the most cost-efficient number, type, and positioning of the sensors for FEWS that may be used to take decisions and alert the population at flood risk. In this study, we focus on a widely recognized FEWS solution to analyze hydrological monitoring and forecasting performances expressed as discharge in various cross-sections of a drainage network. We propose and test a novel framework that aims to maximize FEWS performances while minimizing the number of sections that need instrumentation and suggesting optimal sensor placement to enhance forecasting accuracy. In the selected case study, we demonstrate through feature importance measure that only four sub-basins can achieve the same forecasting performance as the potential twenty-six cross-sections of the local hydrologic monitoring network. The operational dashboard resulting from our proposed framework can assist decision-makers in maximizing the performance and wider adoption of flood early warning systems across geographic and socio-economic scales.

Subjects

Continuous hydrological modelling | Data driven flood forecasting | Feature importance measures | Flood early warning systems

DDC Class

600: Technology

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Optimizing sensor location for the parsimonious design of flood early warning systems