Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Adaptive space–time sampling with wireless sensor nodes for flood forecasting
AU - Neal, Jeffrey C.
AU - Atkinson, Peter M.
AU - Hutton, Craig W.
PY - 2012/1/11
Y1 - 2012/1/11
N2 - 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.
AB - 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.
KW - EnKF
KW - ETKF
KW - Flood forecasting
KW - Wireless sensors
U2 - 10.1016/j.jhydrol.2011.10.021
DO - 10.1016/j.jhydrol.2011.10.021
M3 - Journal article
VL - 414-415
SP - 136
EP - 147
JO - Journal of Hydrology
JF - Journal of Hydrology
SN - 0022-1694
ER -