scholarly journals Selection of catchment descriptors for the physical similarity approach. Part I: Theory.

2013 ◽  
Vol 8 (No. 3) ◽  
pp. 133-140 ◽  
Author(s):  
M. Heřmanovský ◽  
P. Pech
Keyword(s):  
Parameter Estimation ◽  
Model Parameters ◽  
Trial And Error ◽  
Physical Similarity ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Trial And Error Method ◽  
Homogeneous Regions ◽  
Error Method ◽  
Selection Of

This paper focuses on a description of the method used for the identification of optimal catchment descriptors for the physical similarity approach consisting of a scheme for the identification of optimal catchment descriptors and the procedure for finding hydrologically homogeneous regions using inverse clustering. Andrews’ curves are used as the basis for homogeneity checking. The identification of an optimum catchment descriptor is based on the assumption that the addition of an optimal catchment descriptor to a predefined set of catchment descriptors improves the accuracy of model parameter estimation within a set of tested catchments. Two criteria are proposed for the selection of optimal catchment descriptors – a criterion evaluating estimates of model parameters on the basis of different potentially optimal groups of catchment descriptors, MIN, and a criterion evaluating the improvement in model parameter estimation after the addition of a potentially optimal catchment descriptor into the group of preliminarily identified optimal catchment descriptors, MAX. The proposed method provides an alternative to the trial-and-error method for the identification of optimal catchment descriptors.

scholarly journals Selection of catchment descriptors for the physical similarity approach. Part II: Application

2013 ◽  
Vol 8 (No. 4) ◽  
pp. 186-194
Author(s):  
M. Heřmanovský ◽  
P. Pech
Keyword(s):  
Land Cover ◽  
A Priori ◽  
Model Parameters ◽  
Physical Similarity ◽  
Model Parameter ◽  
Soil Moisture Accounting ◽  
Estimated Parameters ◽  
Selection Of

This paper demonstrates an application of the previously published method for selection of optimal catchment descriptors, according to which similar catchments can be identified for the purpose of estimation of the Sacramento – Soil Moisture Accounting (SAC-SMA) model parameters for a set of tested catchments, based on the physical similarity approach. For the purpose of the analysis, the following data from the Model Parameter Estimation Experiment (MOPEX) project were taken: a priori model parameter sets used as reference values for comparison with the newly estimated parameters, and catchment descriptors of four categories (climatic descriptors, soil properties, land cover and catchment morphology). The inverse clustering method, with Andrews’ curves for a homogeneity check, was used for the catchment grouping process. The optimal catchment descriptors were selected on the basis of two criteria, one comparing different subsets of catchment descriptors of the same size (MIN), the other one evaluating the improvement after addition of another catchment descriptor (MAX). The results suggest that the proposed method and the two criteria used may lead to the selection of a subset of conditionally optimal catchment descriptors from a broader set of them. As expected, the quality of the resulting subset of optimal catchment descriptors is mainly dependent on the number and type of the descriptors in the broader set. In the presented case study, six to seven catchment descriptors (two climatic, two soil and at least two land-cover descriptors) were identified as optimal for regionalisation of the SAC-SMA model parameters for a set of MOPEX catchments.

scholarly journals Further Insights on the Role of Accurate State Estimation in Coupled Model Parameter Estimation by a Simple Climate Model Study

2016 ◽  
Author(s):  
Xiaolin Yu ◽  
Shaoqing Zhang ◽  
Xiaopei Lin ◽  
Mingkui Li
Keyword(s):  
Parameter Estimation ◽  
Climate Model ◽  
Coupled Model ◽  
State Parameter ◽  
Multiple Time Scales ◽  
Model Parameters ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Model Study ◽  
The Impact

Abstract. The uncertainties in values of coupled model parameters are an important source of model bias that causes model climate drift. The values can be calibrated by a parameter estimation procedure that projects observational information onto parameters. The signal-to-noise ratio of error covariance between model states and initially perturbed parameters determinates directly the success of parameter estimation or not. With a conceptual climate model that couples the stochastic atmosphere and slow varying ocean, this study examines the sensitivity of the state-parameter covariance on the accuracy of estimated model states in different model components of a coupled system. Due to the interaction of multiple time scales, the fast varying "atmosphere" with the chaotic nature is the major source of state-parameter covariance uncertainties, and thus enhancing the estimation accuracy of atmospheric states is very important for the success of coupled model parameter estimation, especially for the parameters in the air-sea interaction processes. The impact of chaotic-to-periodic ratio in state variability on parameter estimation is also discussed in this study. This simple model study provides a guideline when real observations are used to optimize model parameter in a coupled general circulation model for improving climate analysis and predictions.

scholarly journals Insights on the role of accurate state estimation in coupled model parameter estimation by a conceptual climate model study

2017 ◽  
Vol 24 (2) ◽  
pp. 125-139 ◽  
Author(s):  
Xiaolin Yu ◽  
Shaoqing Zhang ◽  
Xiaopei Lin ◽  
Mingkui Li
Keyword(s):  
Parameter Estimation ◽  
General Circulation ◽  
Climate Model ◽  
Coupled Model ◽  
State Parameter ◽  
Model Parameters ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Model Study ◽  
The Impact

Abstract. The uncertainties in values of coupled model parameters are an important source of model bias that causes model climate drift. The values can be calibrated by a parameter estimation procedure that projects observational information onto model parameters. The signal-to-noise ratio of error covariance between the model state and the parameter being estimated directly determines whether the parameter estimation succeeds or not. With a conceptual climate model that couples the stochastic atmosphere and slow-varying ocean, this study examines the sensitivity of state–parameter covariance on the accuracy of estimated model states in different model components of a coupled system. Due to the interaction of multiple timescales, the fast-varying atmosphere with a chaotic nature is the major source of the inaccuracy of estimated state–parameter covariance. Thus, enhancing the estimation accuracy of atmospheric states is very important for the success of coupled model parameter estimation, especially for the parameters in the air–sea interaction processes. The impact of chaotic-to-periodic ratio in state variability on parameter estimation is also discussed. This simple model study provides a guideline when real observations are used to optimize model parameters in a coupled general circulation model for improving climate analysis and predictions.

Cole-Cole Model Parameter Estimation from Multi-frequency Complex Resistivity Spectrum Based on the Artificial Neural Network

2021 ◽  
Vol 26 (1) ◽  
pp. 71-77
Author(s):  
Weiqiang Liu ◽  
Rujun Chen ◽  
Liangyong Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Parameter Estimation ◽  
Model Parameters ◽  
Near Surface ◽  
Complex Resistivity ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Artificial Neural ◽  
Cole Model

In near surface electrical exploration, it is often necessary to estimate the Cole-Cole model parameters according to the measured multi-frequency complex resistivity spectrum of ore and rock samples in advance. Parameter estimation is a nonlinear optimization problem, and the common method is least square fitting. The disadvantage of this method is that it relies on initial value and the result is unstable when data is confronted with noise interference. To further improve the accuracy of parameter estimation, this paper applied artificial neural network (ANN) method to the Cole-Cole model estimation. Firstly, a large number of forward models are generated as samples to train the neural network and when the data fitting error is lower than the error threshold, the training ends. The trained neural network is directly used to efficiently estimate the parameters of vast amounts of new data. The efficiency of the artificial neural network is analyzed by using simulated and measured spectral induced polarization data. The results show that artificial neural network method has a faster computing speed and higher accuracy in Cole-Cole model parameter estimation.

scholarly journals Predicted small catchment responses to heavy rainfalls with SEGMO and two sets of model parameters

2014 ◽  
Vol 46 (3) ◽  
pp. 205-220 ◽  
Author(s):  
Adam Krajewski ◽  
Hyosang Lee ◽  
Leszek Hejduk ◽  
Kazimierz Banasik
Keyword(s):  
Parameter Estimation ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Small Catchment ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Design Storm ◽  
Catchment Characteristics ◽  
Catchment Responses ◽  
Heavy Rainfalls

Abstract Predicted small catchment responses to heavy rainfalls with SEGMO and two sets of model parameters. The study tests the ability of hydrological part of SEGMO (SedimentGraph Model), i.e. lumped parametric rainfall-runoff procedure of SEGMO to simulate design storm runoff in a Korean catchment. The aim of the investigation is to predict responses of small catchment of the Jeungpyeong river, located in central part of South Korea, with the area of 133.6 km2, to 100-year rainfall events, applying SEGMO and using two parallel approaches for model parameter estimation. The fi rst approach is based on catchment characteristics and USDA-SCS procedures, which is suitable for ungauged basins, and the other one is based on rainfall-runoff measurements. The way of estimation of model parameters has been demonstrated. Finally, the model outputs are compared. The difference in largest peak discharges obtained from SEGMO with the two sets of model parameters, i.e. when estimated on the base of catchment characteristics and USDA-SCS procedures, and on the base of rainfall-runoff measurements were relatively small, approaching 37%. This investigation can be seen as checking the uncertainties in model parameter estimation and their infl uence on fl ood fl ows.

scholarly journals Оптимизация формы корпуса рыболовных судов

2020 ◽  
Author(s):  
VIKTOR BUGAYEV ◽  
DAM VAN TUNG VAN TUNG ◽  
YULIA BONDARENKO
Keyword(s):  
Numerical Methods ◽  
Experimental Methods ◽  
Shape Design ◽  
Trial And Error ◽  
Fishing Vessel ◽  
Trial And Error Method ◽  
Minimal Resistance ◽  
Best Parameters ◽  
Error Method ◽  
Selection Of

При проектировании судна на выбор формы его корпуса влияют тип и назначение судна, многолетний эксплуатационный и инженерный опыт, результаты научных исследований и т.п. Как показывает практика, численные методы исследования имеют явные преимущества (по стоимости и срокам проведения) по сравнению с экспериментальными, суть которых – отработка, например обводов корпуса судна «методом проб и ошибок». По мнению авторов статьи, близкий к экспериментальному метод численного моделирования – весьма удобен при проектировании формы корпуса. В данной работе представлены результаты оптимизации формы корпуса рыболовного судна с помощью численных методов, которые позволяют определить не только наилучшие параметры формы корпуса с точки зрения минимума сопротивления, но и обеспечить требования, предъявляемые к элементам поверхности корпуса. When designing a vessel, the particular hull shape is selected depending on the type and purpose of the vessel, as well as on the longstanding operational and engineering experience, research results, etc. As evidenced in practice, numerical methods of research are obviously more preferable (in terms of costs and time) compared to experimental methods mainly based on pilot testing – for instance, selection of proper shape of the vessel’s hull using the trial and error method. According to the authors of the article, the method of numerical modeling quite close to the experimental one is very convenient for hull shape design. This paper covers the results of optimizing the hull shape of a fishing vessel using numerical methods which can not only determine the best parameters of the hull shape from the point of minimal resistance, but can also ensure that all requirements for the elements of the surface of the hull are properly met.

CONTEXTUAL SUBTEXT INFORMATION OF A FOREIGN LANGUAGE TEXT: HERMENEUTIC APPROACH

2020 ◽  
Vol 2020 (30) ◽  
pp. 75-87
Author(s):  
Lidiya Derbenyova
Keyword(s):  
Foreign Language ◽  
Optimal Solution ◽  
Decision Making Process ◽  
Trial And Error ◽  
Transmission Of Information ◽  
Hermeneutic Approach ◽  
Trial And Error Method ◽  
Problems Of Translation ◽  
Error Method ◽  
Language Text

The article focuses on the problems of translation in the field of hermeneutics, understood as a methodology in the activity of an interpreter, the doctrine of the interpretation of texts, as a component of the transmission of information in a communicative aspect. The relevance of the study is caused by the special attention of modern linguistics to the under-researched issues of hermeneutics related to the problems of transmission of foreign language text semantics in translation. The process of translation in the aspect of hermeneutics is regarded as the optimum search and decision-making process, which corresponds to a specific set of functional criteria of translation, which can take many divergent forms. The translator carries out a number of specific translation activities: the choice of linguistic means and means of expression in the translation language, replacement and compensation of nonequivalent units. The search for the optimal solution itself is carried out using the “trial and error” method. The translator always acts as an interpreter. Within the boundaries of a individual utterance, it must be mentally reconstructed as conceptual situations, the mentally linguistic actions of the author, which are verbalized in this text.

l2-Penalized temporal logit-mixed models for the estimation of regional obesity prevalence over time

2021 ◽  
pp. 096228022110175
Author(s):  
Jan P Burgard ◽  
Joscha Krause ◽  
Ralf Münnich ◽  
Domingo Morales
Keyword(s):  
Parameter Estimation ◽  
Medical Treatment ◽  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Obesity Prevalence ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Public Health Reporting ◽  
Over Time

Obesity is considered to be one of the primary health risks in modern industrialized societies. Estimating the evolution of its prevalence over time is an essential element of public health reporting. This requires the application of suitable statistical methods on epidemiologic data with substantial local detail. Generalized linear-mixed models with medical treatment records as covariates mark a powerful combination for this purpose. However, the task is methodologically challenging. Disease frequencies are subject to both regional and temporal heterogeneity. Medical treatment records often show strong internal correlation due to diagnosis-related grouping. This frequently causes excessive variance in model parameter estimation due to rank-deficiency problems. Further, generalized linear-mixed models are often estimated via approximate inference methods as their likelihood functions do not have closed forms. These problems combined lead to unacceptable uncertainty in prevalence estimates over time. We propose an l2-penalized temporal logit-mixed model to solve these issues. We derive empirical best predictors and present a parametric bootstrap to estimate their mean-squared errors. A novel penalized maximum approximate likelihood algorithm for model parameter estimation is stated. With this new methodology, the regional obesity prevalence in Germany from 2009 to 2012 is estimated. We find that the national prevalence ranges between 15 and 16%, with significant regional clustering in eastern Germany.

Sign in / Sign up

Export Citation Format

Share Document