Information Theory and Artificial Intelligence to Manage Uncertainty in Hydrodynamic and Hydrological Models
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A01=Abebe Andualem Jemberie
advanced hydrodynamic model integration
Ann Model
Author_Abebe Andualem Jemberie
Average Travel Time
Average Wave Speed
Category=UYQ
complementary
Complementary Model
conceptual
Conceptual Rainfall Runoff Model
data assimilation methods
Data Driven Modelling Technique
Dutch Coast
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Error Forecasting
Error Time Series
Flow Distribution
Forecast Horizon
function
fuzzy
fuzzy logic systems
Fuzzy Sets
Gate Operation
HIRLAM Model
hydrological uncertainty quantification
K13 Platform
Lag Time
Manage Model Uncertainty
membership
Membership Function
Meuse River
modelling
network
neural
neural network hydrology
Predictive Parameters
rainfall
residual error modelling
river flow forecasting
Scandinavian Coast
set
Stage Discharge Relationship
Surge Prediction
Water Level
Wind Components
Product details
- ISBN 9781138405578
- Weight: 530g
- Dimensions: 174 x 246mm
- Publication Date: 03 Jul 2017
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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The complementary nature of physically-based and data-driven models in their demand for physical insight and historical data, leads to the notion that the predictions of a physically-based model can be improved and the associated uncertainty can be systematically reduced through the conjunctive use of a data-driven model of the residuals. The objective of this thesis is to minimise the inevitable mismatch between physically-based models and the actual processes as described by the mismatch between predictions and observations. Principles based on information theory are used to detect the presence and nature of residual information in model errors that might help to develop a data-driven model of the residuals by treating the gap between the process and its (physically-based) model as a separate process. The complementary modelling approach is applied to various hydrodynamic and hydrological models to forecast the expected errors and accuracy, using neural
