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Evaluating the Applicability of Drainage Routing Schemes for Paddy Fields

JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 2020年第9期146卷

作者： Song, Jung-Hun Her, Younggu Hwang, Soonho Park, Jihoon Yoon, Kwang-Sik Kang, Moon-Seong Univ Florida Dept Agr & Biol Engn Trop Res & Educ Ctr Homestead FL 33031 USA Seoul Natl Univ Res Inst Agr & Life Sci Seoul 08826 South Korea APEC Climate Ctr Predict Res Dept 12 Centum 7 Ro Busan 48058 South Korea Chonnam Natl Univ Dept Rural & Biosyst Engn Gwangju 61186 South Korea Seoul Natl Univ Dept Rural Syst Engn Res Inst Agr & Life Sci Inst Green Bio Sci & Technol Seoul 08826 South Korea

Paddy fields play a critical role as storage for the retention of surface water in hydrological processes, but their water balance is complicated by irrigation and drainage management. Studies have proposed flow routing methods for paddy fields by simplifying the hydrological and hydraulic complexity associated with drainage processes. However, the performance of paddy field routing schemes has not yet been sufficiently evaluated to assist in the selection of routing methods for a water balance analysis. This study evaluated three common drainage routing schemes developed for paddy fields, including the simple water balance (SWB), linear reservoir with threshold (LRT), and broad-crested weir (BCW) models, in terms of complexity and performance. This study also compared the accuracy of irrigation water requirement and sediment load estimates simulated using the three schemes. Results showed that the BCW model produced the best statistical accuracy when predicting daily flow, but the three routing methods performed similarly over a 10-day time scale. Results suggested that the simple structure of the SWB model was not accurate enough to reproduce high and low flow on a daily scale, which produced relatively higher irrigation water requirements and sediment loads compared with those of LRT and BCW. LRT provided performance statistics similar to those of BCW using fewer calibration parameters. The comparison confirmed a trade-off relationship between model complexity and performance, highlighting the importance of model selection for hydrological analysis.

关键词： Paddy fields Water balance Drainage process Surface flow routing model performance model complexity

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QMCM: Minimizing Vapnik's bound on the VC dimension

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NEUROCOMPUTING 2020年 399卷 352-360页

作者： Jayadeva Soman, Sumit Pant, Himanshu Sharma, Mayank Indian Inst Technol Dept Elect Engn Delhi India

The recently proposed Minimal complexity Machine (MCM) learns a hyperplane classifier by minimizing a bound on the Vapnik-Chervonenkis (VC) dimension. Both the linear and kernel versions of the MCM solve a linear programming problem, in order to minimize a bound on the VC dimension. This paper proposes a new quadratic programming formulation, termed as the Quadratic MCM (QMCM), that minimizes a tighter bound on the VC dimension. We present two variants of the QMCM, that differ in the norm of the error vector being minimized. We also explore a scalable variant of the QMCM for large datasets using Stochastic Gradient Descent (SGD), and present the use of the QMCM as a viable features-election method, in view of the the sparse nature of the models it learns. We compare the performance of the QMCM variants with LIBLinear, a linear Support Vector Machine (SVM) library;as well as against Pegasos and the linear MCM for large datasets, along with sequential feature selection methods and ReliefF. Our results validate the superiority of the QMCM in terms of statistically significant improvements on benchmark datasets from the UCI Machine Learning repository. (c) 2020 Published by Elsevier B.V.

关键词： VC dimension Minimal complexity machine model complexity Classification Feature selection

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Cost-conscious comparison of supervised learning algorithms over multiple data sets

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PATTERN RECOGNITION 2012年第4期45卷 1772-1781页

作者： Ulas, Aydin Yildiz, Olcay Taner Alpaydin, Ethem Bogazici Univ Dept Comp Engn TR-34342 Istanbul Turkey Isik Univ Dept Comp Engn TR-34398 Istanbul Turkey

In the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi(2)Test, a generalization of our previous work, for ordering multiple learning algorithms on multiple data sets from "best" to "worst" where our goodness measure is composed of a prior cost term additional to generalization error. Our simulations show that Multi2Test generates orderings using pairwise tests on error and different types of cost using time and space complexity of the learning algorithms. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Machine learning Statistical tests Classifier comparison model selection model complexity

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Uncertainty Quantification and Sensitivity Analysis for Computational FFR Estimation in Stable Coronary Artery Disease

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CARDIOVASCULAR ENGINEERING AND TECHNOLOGY 2018年第4期9卷 597-622页

作者： Fossan, Fredrik E. Sturdy, Jacob Mueller, Lucas O. Strand, Andreas Braten, Anders T. Jorgensen, Arve Wiseth, Rune Hellevik, Leif R. Norwegian Univ Sci & Technol Dept Struct Engn Trondheim Norway St Olavs Hosp Clin Cardiol Trondheim Norway Norwegian Univ Sci & Technol Dept Circulat & Med Imaging Trondheim Norway St Olavs Hosp Dept Radiol & Nucl Med Trondheim Norway

Purpose-The main objectives of this study are to validate a reduced-order model for the estimation of the fractional flow reserve (FFR) index based on blood flow simulations that incorporate clinical imaging and patient-specific characteristics, and to assess the uncertainty of FFR predictions with respect to input data on a per patient basis. Methods-We consider 13 patients with symptoms of stable coronary artery disease for which 24 invasive FFR measurements are available. We perform an extensive sensitivity analysis on the parameters related to the construction of a reduced-order (hybrid 1D-0D) model for FFR predictions. Next we define an optimal setting by comparing reduced-order model predictions with solutions based on the 3D incompressible Navier-Stokes equations. Finally, we characterize prediction uncertainty with respect to input data and identify the most influential inputs by means of sensitivity analysis. Results-Agreement between FFR computed by the reduced-order model and by the full 3D model was satisfactory, with a bias (FFR3D - FFR1D-0D) of - 0.03 (+/- 0.03) at the 24 measured locations. Moreover, the uncertainty related to the factor by which peripheral resistance is reduced from baseline to hyperemic conditions proved to be the most influential parameter for FFR predictions, whereas uncertainty in stenosis geometry had greater effect in cases with low FFR. Conclusion-model errors related to solving a simplified reduced-order model rather than a full 3D problem were small compared with uncertainty related to input data. Improved measurement of coronary blood flow has the potential to reduce uncertainty in computational FFR predictions significantly.

关键词： Computational FFR Uncertainty quantification model complexity Total uncertainty

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Investigating the effect of complexity on groundwater flow modeling uncertainty

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2018年第3期32卷 643-659页

作者： Samani, Saeideh Moghaddam, Asghar Asghari Ye, Ming Univ Tabriz Dept Earth Sci Tabriz Iran Florida State Univ Dept Comp Sci Tallahassee FL 32306 USA

Considering complexity in groundwater modeling can aid in selecting an optimal model, and can avoid over parameterization, model uncertainty, and misleading conclusions. This study was designed to determine the uncertainty arising from model complexity, and to identify how complexity affects model uncertainty. The Ajabshir aquifer, located in East Azerbaijan, Iran, was used for comprehensive hydrogeological studies and modeling. Six unique conceptual models with four different degrees of complexity measured by the number of calibrated model parameters (6, 10, 10, 13, 13 and 15 parameters) were compared and characterized with alternative geological interpretations, recharge estimates and boundary conditions. The models were developed with model Muse and calibrated using UCODE with the same set of observed data of hydraulic head. Different methods were used to calculate model probability and model weight to explore model complexity, including Bayesian model averaging, model selection criteria, and multicriteria decision-making (MCDM). With the model selection criteria of AIC, AICc and BIC, the simplest model received the highest model probability. The model selection criterion, KIC, and the MCDM method, in addition to considering the quality of model fit between observed and simulated data and the number of calibrated parameters, also consider uncertainty in parameter estimates with a Fisher information matrix. KIC and MCDM selected a model with moderate complexity (10 parameters) and the best parameter estimation (model 3) as the best models, over another model with the same degree of complexity (model 2). The results of these comparisons show that in choosing between models, priority should be given to quality of the data and parameter estimation rather than degree of complexity.

关键词： model complexity Bayesian model averaging Multicriteria decision making Fuzzy AHP method Fuzzy TOPSIS method

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Building modeling: Selection of the most appropriate model for predictive control

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ENERGY AND BUILDINGS 2012年第Dec.期55卷 341-350页

作者： Privara, Samuel Vana, Zdenek Zacekova, Eva Cigler, Jiri Czech Tech Univ Fac Elect Engn Dept Control Engn Prague 16627 6 Czech Republic

model predictive control has become a widespread solution in many industrial applications and is gaining ground in the field of energy management and automation systems of buildings. A model with reasonable prediction properties is an ultimate condition for good performance of the predictive controller. This paper presents an approach in which a model of a building is selected by an iterative two stage procedure. In the first stage, a minimum set of disturbance inputs is formed so that the resulting model is the best with respect to a defined quality criterion;then the second stage comprises addition of the states to obtain the final minimum set of states maximizing the model quality. The procedure stops when it makes no sense to select more complex model as it brings no more quality improvements. Statistical tests such as the likelihood ratio test, the tests based on cumulative periodogram, the two-sample Kolmogorov-Smirnov test as well as others (fit factor and coefficient of determination) are used to evaluate the relationship between the addition of inputs/states and the model quality. Three identification approaches, namely model predictive control relevant identification, deterministic semi-physical and probabilistic semi-physical modeling are used for estimation of building parameters. Finally, a case study is provided where all the above mentioned approaches are investigated and tested. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Building modeling and identification model selection Statistical tests model complexity

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Occupant behavior in building energy simulation: Towards a fit-for-purpose modeling strategy

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ENERGY AND BUILDINGS 2016年 121卷 188-204页

作者： Gaetani, Isabella Hoes, Pieter-Jan Hensen, Jan L. M. Eindhoven Univ Technol Bldg Phys & Serv POB 513 NL-5600 MB Eindhoven Netherlands

Occupant behavior is nowadays acknowledged as a main source of discrepancy between predicted and actual building performance;therefore, researchers attempt to model occupants' presence and adaptive actions more realistically. Literature shows a proliferation of increasingly complex, data-based models that well fit the cases analyzed. However, the actual use of these models by practitioners is very limited. Moreover, simpler models might be preferable, depending on the aim of investigation. The present study proposes shifting the focus to fit-for-purpose modeling, in which the most appropriate model for a specific case is characterized by the lowest complexity, while preserving its validity with respect to the aim of the simulation. A number of steps are taken to achieve this shift in focus. The existing models are presented according to complexity. The available inter-comparison studies are critically reviewed. Subsequently, a list of parameters that affect the choice of an appropriate modeling strategy is presented as a first attempt to derive guidelines and generate a framework for investigation. To support such claims the effect of some of the listed parameters is evaluated in a case study. The main conclusion to be drawn is that determining the best complexity for occupant behavior modeling is strongly case specific. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Occupant behavior modeling model complexity Fit-for-purpose Parsimony

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Selection and weighting of samples in multivariate regression model updating

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2005年第2期76卷 205-214页

作者： Capron, X Walczak, B de Noord, OE Massart, DL Free Univ Brussels Inst Pharmaceut ChemoAC B-1090 Brussels Belgium Shell Int Chem BV Shell Res & Technol Ctr NL-1030 BN Amsterdam Netherlands

Multivariate regression models are valid only for prediction of samples that are within the range of calibration data. Prediction of dependent variables for samples carrying new sources of variance requires updating of the model. A simple and convenient way to extend the model is to re-calibrate it using new incoming samples. However, it may be difficult and expensive to collect a high number of new samples and to analyse their property of interest with the reference method. Consequently, it is important to know what is the minimal number of samples necessary to update efficiently the model. A possibility would be to use only very few samples and to give them more weight, e.g. by including several copies of them. In this work, the impact of weighting on the performance of updated models is studied. For data sets studied, the weight applied to samples used for the update of the model has less importance than the number of these samples and their representativity, i.e. representative methods of selection of samples lead to better results than the other ones. (c) 2004 Elsevier B.V. All rights reserved.

关键词： new sources of variance selection of samples model complexity

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modelling sulfate reduction in anaerobic digestion: complexity evaluation and parameter calibration

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WATER RESEARCH 2018年 130卷 255-262页

作者： Ahmed, Wasim Rodriguez, Jorge Khalifa Univ Sci & Technol Masdar Inst Dept Chem Engn POB 54224 Abu Dhabi U Arab Emirates

A comparative analysis of five different structures of sulfate reduction (SR) models for anaerobic digestion (AD) was conducted to evaluate their accuracy to provide model developers and users with better information to decide on the optimum degree of complexity. The models evaluated differ in terms of the number/type of sulfate reducing bacterial activities considered based on the electron donors used. A systematic calibration of the evaluated models against a large set of experimental data was also conducted using a very recent parameter calibration method. Results indicate that a simple model incorporating both acetate utilizing and hydrogen utilizing sulfate reducing bacterial activities (the M-AH model) achieves a good balance between performance and complexity in terms of prediction errors against experimental data. All the models evaluated provided acceptable predictions except the model including only hydrogen utilizing sulfate reducing bacterial activity. More complex model structures are recommended only if required in specific experimental cases. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Sulfate reduction Anaerobic digestion modelling model complexity ADM1

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Dependence of Computational models on Input Dimension: Tractability of Approximation and Optimization Tasks

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IEEE TRANSACTIONS ON INFORMATION THEORY 2012年第2期58卷 1203-1214页

作者： Kainen, Paul C. Kurkova, Vera Sanguineti, Marcello Georgetown Univ Dept Math & Stat Washington DC 20057 USA Acad Sci Czech Republic Inst Comp Sci Prague 18207 8 Czech Republic Univ Genoa Dept Commun Comp & Syst Sci DIST I-16145 Genoa Italy

The role of input dimension d is studied in approximating, in various norms, target sets of d-variable functions using linear combinations of adjustable computational units. Results from the literature, which emphasize the number n of terms in the linear combination, are reformulated, and in some cases improved, with particular attention to dependence on d. For worst-case error, upper bounds are given in the factorized form xi(d)kappa(n), where kappa is nonincreasing (typically kappa(n) similar to n(-1/2)). Target sets of functions are described for which the function xi is a polynomial. Some important cases are highlighted where xi decreases to zero as d -> infinity. For target functions, extent (e.g., the size of domains in where they are defined), scale (e.g., maximum norms of target functions), and smoothness (e.g., the order of square-integrable partial derivatives) may depend on, and the influence of such dimension-dependent parameters on model complexity is considered. Results are applied to approximation and solution of optimization problems by neural networks with perceptron and Gaussian radial computational units.

关键词： Dictionary-based computational models high-dimensional approximation and optimization model complexity polynomial upper bounds

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