Improvements in Performance of the Hillslope Link Model in Iowa using a Non-linear Representation of Natural and Artificially Drained Subsurface Flows

2021 ◽  
Author(s):  
Nicolas Velasquez ◽  
Ricardo Mantilla ◽  
Witold F Krajewski ◽  
Morgan Fonley
Keyword(s):  
Linear Representation ◽  
Subsurface Flows ◽  
Non Linear ◽  
Link Model

scholarly journals Improving Hillslope Link Model Performance from Non-Linear Representation of Natural and Artificially Drained Subsurface Flows

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 187
Author(s):  
Nicolás Velásquez ◽  
Ricardo Mantilla ◽  
Witold Krajewski ◽  
Morgan Fonley ◽  
Felipe Quintero
Keyword(s):  
Model Performance ◽  
Linear Representation ◽  
Tile Drainage ◽  
Diagnostic Approach ◽  
Scale Parameters ◽  
Performance Improvements ◽  
Total Runoff ◽  
Recession Curves ◽  
Non Linear ◽  
Link Model

This study evaluates the potential for a newly proposed non-linear subsurface flux equation to improve the performance of the hydrological Hillslope Link Model (HLM). The equation contains parameters that are functionally related to the hillslope steepness and the presence of tile drainage. As a result, the equation provides better representation of hydrograph recession curves, hydrograph timing, and total runoff volume. The authors explore the new parameterization’s potential by comparing a set of diagnostic and prognostic setups in HLM. In the diagnostic approach, they configure 12 different scenarios with spatially uniform parameters over the state of Iowa. In the prognostic case, they use information from topographical maps and known locations of tile drainage to distribute parameter values. To assess performance improvements, they compare simulation results to streamflow observations during a 17-year period (2002–2018) at 140 U.S. Geological Survey (USGS) gauging stations. The operational setup of the HLM model used at the Iowa Flood Center (IFC) serves as a benchmark to quantify the overall improvement of the model. In particular, the new equation provides better representation of recession curves and the total streamflow volumes. However, when comparing the diagnostic and prognostic setups, the authors found discrepancies in the spatial distribution of hillslope scale parameters. The results suggest that more work is required when using maps of physical attributes to parameterize hydrological models. The findings also demonstrate that the diagnostic approach is a useful strategy to evaluate models and assess changes in their formulations.

scholarly journals Psychophysical evidence for a non-linear representation of facial identity

2009 ◽  
Vol 49 (18) ◽  
pp. 2285-2296 ◽  
Author(s):  
Steven C. Dakin ◽  
Diana Omigie
Keyword(s):  
Linear Representation ◽  
Facial Identity ◽  
Non Linear

scholarly journals Towards non-linear representation

2011 ◽  
Vol 15 (2) ◽  
pp. 93-95
Author(s):  
Paul Richens
Keyword(s):  
Linear Representation ◽  
Non Linear

Reconstructing Sea Surface Dynamics Using a Linear Koopman Kalman Filter

2021 ◽  
Author(s):  
Said Ouala ◽  
Ronan Fablet ◽  
Ananda Pascual Pascual ◽  
Bertrand Chapron ◽  
Fabrice Collard ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Linear Representation ◽  
Sea Surface ◽  
Dynamical Model ◽  
Optimal Interpolation ◽  
Model Based ◽  
Trade Offs ◽  
Non Linear ◽  
Spatio Temporal

<p>Spatio-temporal interpolation applications are important in the context of ocean surface modeling. Current state-of-the-art techniques typically rely either on optimal interpolation or on model-based approaches which explicitly exploit a dynamical model. While the optimal interpolation suffers from smoothing issues making it unreliable in retrieving fine-scale variability, the selection and parametrization of a dynamical model, when considering model-based data assimilation strategies, remains a complex issue since several trade-offs between the model's complexity and its applicability in sea surface data assimilation need to be carefully addressed. For these reasons, deriving new data assimilation architectures that can perfectly exploit the observations and the current advances in signal processing, modeling and artificial intelligence is crucial.</p><p>In this work, we explore new advances in data-driven data assimilation to exploit the classical Kalman filter in the interpolation of spatio-temporal fields. The proposed algorithm is written in an end-to-end differentiable setting in order to allow for the learning of the linear dynamical model from a data assimilation cost. Furthermore, the linear model is formulated on a space of observables, rather than the space of observations, which allows for perfect replication of non-linear dynamics when considering periodic and quasi-periodic limit sets and providing a decent (short-term) forecast of chaotic ones. One of the main advantages of the proposed architecture is its simplicity since it utilises a linear representation coupled with a Kalman filter. Interestingly, our experiments show that exploiting such a linear representation leads to better data assimilation when compared to non-linear filtering techniques, on numerous applications, including the sea level anomaly reconstruction from satellite remote sensing observations.</p>

Graphical Non-Linear Representation of Multi-Dimensional Laboratory Measurements in their Clinical Context

1991 ◽  
Vol 30 (02) ◽  
pp. 138-144 ◽  
Author(s):  
J. O. O. Hoeke ◽  
E. S. Gelsema ◽  
R. W. WuIkan ◽  
B. Leijnse
Keyword(s):  
Information Transfer ◽  
Graphical Representation ◽  
Clinical Chemistry ◽  
Linear Representation ◽  
Clinical Decision ◽  
Chemistry Laboratory ◽  
Test Results ◽  
Laboratory Test Results ◽  
Non Linear ◽  
Clinical Chemistry Laboratory

AbstractA polygon-based graphical representation of laboratory test results using non-linear scaling is described. It is argued that the non-linearity of the scale and the use of colors in the representation facilitates interpretation of the test result in its relationship to the standard reference range and critical clinical decision levels. Preliminary results suggest that this representation may be fruitfully used to enhance the efficiency of the information transfer from the clinical chemistry laboratory to clinicians. Other applications, inside as well as outside the medical field, may easily be imagined.

scholarly journals Deep Embedded Non-Redundant Clustering

2020 ◽  
Vol 34 (04) ◽  
pp. 5174-5181
Author(s):  
Lukas Miklautz ◽  
Dominik Mautz ◽  
Muzaffer Can Altinigneli ◽  
Christian Böhm ◽  
Claudia Plant
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Image Data ◽  
Linear Representation ◽  
Representation Learning ◽  
Complex Data ◽  
Data Types ◽  
Data Set ◽  
Non Linear ◽  
Representational Power

Complex data types like images can be clustered in multiple valid ways. Non-redundant clustering aims at extracting those meaningful groupings by discouraging redundancy between clusterings. Unfortunately, clustering images in pixel space directly has been shown to work unsatisfactory. This has increased interest in combining the high representational power of deep learning with clustering, termed deep clustering. Algorithms of this type combine the non-linear embedding of an autoencoder with a clustering objective and optimize both simultaneously. None of these algorithms try to find multiple non-redundant clusterings. In this paper, we propose the novel Embedded Non-Redundant Clustering algorithm (ENRC). It is the first algorithm that combines neural-network-based representation learning with non-redundant clustering. ENRC can find multiple highly non-redundant clusterings of different dimensionalities within a data set. This is achieved by (softly) assigning each dimension of the embedded space to the different clusterings. For instance, in image data sets it can group the objects by color, material and shape, without the need for explicit feature engineering. We show the viability of ENRC in extensive experiments and empirically demonstrate the advantage of combining non-linear representation learning with non-redundant clustering.

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