Optimisation of dynamic heterogeneous rainfall sensor networks in the context of citizen observatories

Research output: ThesisDissertation (TU Delft)

72 Downloads (Pure)


Precipitation drives the dynamics of flows and storages in water systems, making its monitoring essential for water management. Conventionally, precipitation is monitored using in-situ and remote sensors. In-situ sensors are arranged in networks, which are usually sparse, providing continuous observations for long periods at fixed points in space, and due to the high costs of such networks, they are often sub-optimal. To increase the efficiency of the monitoring networks, we explore the use of sensors that can relocate as rainfall events develop (dynamic sensors), as well as increasing the number of sensors involving volunteers (citizens). This research focusses on the development of an approach for merging heterogeneous observations in non-stationary precipitation fields, exploring the interactions between different definitions of optimality for the design of sensor networks, as well as development of algorithms for the optimal scheduling of dynamic sensors. This study was carried out in three different case studies, including Bacchiglione River (Italy), Don River (U.K.) and Brue Catchment (U.K.) The results of this study indicate that optimal use of dynamic sensors may be useful for monitoring precipitation to support water management and flow forecasting.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
  • Solomatine, D.P., Supervisor
  • Alfonso, Leonardo, Supervisor, External person
Award date24 Sep 2019
Print ISBNs978-0-367-41706-2
Publication statusPublished - 2019

Bibliographical note

Dissertation submitted in fulfillment of the requirements of the Board for Doctorates of Delft University of Technology and of the Academic Board of IHE Delft Institute for Water Education.


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