Final published version
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Adjusting for Conditional Bias in Process Model Simulations of Hydrological Extremes
T2 - An Experiment Using the North Wyke Farm Platform
AU - Curceac, Stelian
AU - Atkinson, Peter M.
AU - Milne, Alice
AU - Wu, Lianhai
AU - Harris, Paul
PY - 2020/10/9
Y1 - 2020/10/9
N2 - Peak flow events can lead to flooding which can have negative impacts on human life and ecosystem services. Therefore, accurate forecasting of such peak flows is important. Physically-based process models are commonly used to simulate water flow, but they often under-predict peak events (i.e., are conditionally biased), undermining their suitability for use in flood forecasting. In this research, we explored methods to increase the accuracy of peak flow simulations from a process-based model by combining the model’s output with: a) a semi-parametric conditional extreme model and b) an extreme learning machine model. The proposed 3-model hybrid approach was evaluated using fine temporal resolution water flow data from a sub-catchment of the North Wyke Farm Platform, a grassland research station in south-west England, United Kingdom. The hybrid model was assessed objectively against its simpler constituent models using a jackknife evaluation procedure with several error and agreement indices. The proposed hybrid approach was better able to capture the dynamics of the flow process and, thereby, increase prediction accuracy of the peak flow events.
AB - Peak flow events can lead to flooding which can have negative impacts on human life and ecosystem services. Therefore, accurate forecasting of such peak flows is important. Physically-based process models are commonly used to simulate water flow, but they often under-predict peak events (i.e., are conditionally biased), undermining their suitability for use in flood forecasting. In this research, we explored methods to increase the accuracy of peak flow simulations from a process-based model by combining the model’s output with: a) a semi-parametric conditional extreme model and b) an extreme learning machine model. The proposed 3-model hybrid approach was evaluated using fine temporal resolution water flow data from a sub-catchment of the North Wyke Farm Platform, a grassland research station in south-west England, United Kingdom. The hybrid model was assessed objectively against its simpler constituent models using a jackknife evaluation procedure with several error and agreement indices. The proposed hybrid approach was better able to capture the dynamics of the flow process and, thereby, increase prediction accuracy of the peak flow events.
KW - peak flow
KW - conditional extreme model
KW - extreme learning machine
KW - process-based model
KW - hybrid
KW - grassland agriculture
U2 - 10.3389/frai.2020.565859
DO - 10.3389/frai.2020.565859
M3 - Journal article
VL - 3
JO - Frontiers in Artificial Intelligence
JF - Frontiers in Artificial Intelligence
SN - 2624-8212
M1 - 565859
ER -