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Adaptive space–time sampling with wireless sensor nodes for flood forecasting

Adaptive space–time sampling with wireless sensor nodes for flood forecasting
Adaptive space–time sampling with wireless sensor nodes for flood forecasting
This paper investigates a method for the real-time design and execution of a space–time sampling strategy in the context of flood forecasting. Measurements of water level taken by a network of wireless sensors were assimilated into a one-dimensional hydrodynamic model using an ensemble Kalman filter, to create a forecasting model. This research focused on methods for targeting measurements in real-time to be assimilated by the forecasting model, such that the power-limited but flexible sensor network could be used optimally. Two targeting methods were developed. The first targeted measurements systematically over space and time until the forecasting model predicted that the probability of the water level exceeding a pre-defined threshold was less than 5%. The second method targeted measurements based on the expected decrease in forecasted water level error variance at a validation time and location, quickly calculated for various sets of measurements by an ensemble transform Kalman filter. Targeting measurements based on the decrease in forecast error variance was shown to be more efficient than a systematic sampling method.
enkf etkf, flood forecasting, wireless sensors
0022-1694
136-147
Neal, Jeffrey C.
848216c6-dd4b-4400-a86b-0c5883543ffa
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Hutton, Craig W.
9102617b-caf7-4538-9414-c29e72f5fe2e
Neal, Jeffrey C.
848216c6-dd4b-4400-a86b-0c5883543ffa
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Hutton, Craig W.
9102617b-caf7-4538-9414-c29e72f5fe2e

Neal, Jeffrey C., Atkinson, Peter M. and Hutton, Craig W. (2012) Adaptive space–time sampling with wireless sensor nodes for flood forecasting. Journal of Hydrology, 414-415, 136-147. (doi:10.1016/j.jhydrol.2011.10.021).

Record type: Article

Abstract

This paper investigates a method for the real-time design and execution of a space–time sampling strategy in the context of flood forecasting. Measurements of water level taken by a network of wireless sensors were assimilated into a one-dimensional hydrodynamic model using an ensemble Kalman filter, to create a forecasting model. This research focused on methods for targeting measurements in real-time to be assimilated by the forecasting model, such that the power-limited but flexible sensor network could be used optimally. Two targeting methods were developed. The first targeted measurements systematically over space and time until the forecasting model predicted that the probability of the water level exceeding a pre-defined threshold was less than 5%. The second method targeted measurements based on the expected decrease in forecasted water level error variance at a validation time and location, quickly calculated for various sets of measurements by an ensemble transform Kalman filter. Targeting measurements based on the decrease in forecast error variance was shown to be more efficient than a systematic sampling method.

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More information

e-pub ahead of print date: 25 October 2011
Published date: 11 January 2012
Keywords: enkf etkf, flood forecasting, wireless sensors
Organisations: Global Env Change & Earth Observation

Identifiers

Local EPrints ID: 339909
URI: https://eprints.soton.ac.uk/id/eprint/339909
ISSN: 0022-1694
PURE UUID: 46684af9-5c7a-408d-a64a-acbdfc51999d
ORCID for Craig W. Hutton: ORCID iD orcid.org/0000-0002-5896-756X

Catalogue record

Date deposited: 01 Jun 2012 11:42
Last modified: 28 Jun 2018 00:34

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Contributors

Author: Jeffrey C. Neal
Author: Craig W. Hutton ORCID iD

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