A Linear Perturbation Model with Physical Optics Approximation to Recover Inhomogeneous Surface Impedance

2018 ◽  
Author(s):  
S. Loillier ◽  
S. Méric ◽  
R. Loison ◽  
G. Maze-Merceur
Keyword(s):  
Surface Impedance ◽  
Physical Optics ◽  
Linear Perturbation ◽  
Inhomogeneous Surface ◽  
Perturbation Model ◽  
Physical Optics Approximation ◽  
Linear Perturbation Model

Monthly runoff generation using the linear perturbation model

1993 ◽  
Vol 144 (1-4) ◽  
pp. 371-379 ◽  
Author(s):  
U.C. Kothyari ◽  
V. Aravamuthan ◽  
V.P. Singh
Keyword(s):  
Linear Perturbation ◽  
Runoff Generation ◽  
Perturbation Model ◽  
Monthly Runoff ◽  
Linear Perturbation Model

scholarly journals Assessing the performance of eight real-time updating models and procedures for the Brosna River

2005 ◽  
Vol 9 (4) ◽  
pp. 394-411 ◽  
Author(s):  
M. Goswami ◽  
K. M. O'Connor ◽  
K. P. Bhattarai ◽  
A. Y. Shamseldin
Keyword(s):  
Neural Network ◽  
Linear Model ◽  
Lead Time ◽  
Ar Model ◽  
Linear Perturbation ◽  
Discharge Data ◽  
Perturbation Model ◽  
Simple Linear Model ◽  
Auto Regressive ◽  
Linear Perturbation Model

Abstract. The flow forecasting performance of eight updating models, incorporated in the Galway River Flow Modelling and Forecasting System (GFMFS), was assessed using daily data (rainfall, evaporation and discharge) of the Irish Brosna catchment (1207 km2), considering their one to six days lead-time discharge forecasts. The Perfect Forecast of Input over the Forecast Lead-time scenario was adopted, where required, in place of actual rainfall forecasts. The eight updating models were: (i) the standard linear Auto-Regressive (AR) model, applied to the forecast errors (residuals) of a simulation (non-updating) rainfall-runoff model; (ii) the Neural Network Updating (NNU) model, also using such residuals as input; (iii) the Linear Transfer Function (LTF) model, applied to the simulated and the recently observed discharges; (iv) the Non-linear Auto-Regressive eXogenous-Input Model (NARXM), also a neural network-type structure, but having wide options of using recently observed values of one or more of the three data series, together with non-updated simulated outflows, as inputs; (v) the Parametric Simple Linear Model (PSLM), of LTF-type, using recent rainfall and observed discharge data; (vi) the Parametric Linear perturbation Model (PLPM), also of LTF-type, using recent rainfall and observed discharge data, (vii) n-AR, an AR model applied to the observed discharge series only, as a naïve updating model; and (viii) n-NARXM, a naive form of the NARXM, using only the observed discharge data, excluding exogenous inputs. The five GFMFS simulation (non-updating) models used were the non-parametric and parametric forms of the Simple Linear Model and of the Linear Perturbation Model, the Linearly-Varying Gain Factor Model, the Artificial Neural Network Model, and the conceptual Soil Moisture Accounting and Routing (SMAR) model. As the SMAR model performance was found to be the best among these models, in terms of the Nash-Sutcliffe R2 value, both in calibration and in verification, the simulated outflows of this model only were selected for the subsequent exercise of producing updated discharge forecasts. All the eight forms of updating models for producing lead-time discharge forecasts were found to be capable of producing relatively good lead-1 (1-day ahead) forecasts, with R2 values almost 90% or above. However, for higher lead time forecasts, only three updating models, viz., NARXM, LTF, and NNU, were found to be suitable, with lead-6 values of R2 about 90% or higher. Graphical comparisons were made of the lead-time forecasts for the two largest floods, one in the calibration period and the other in the verification period.

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