Bayesian selection of slope hydraulic model and identification of model parameters using monitoring data and subset simulation

2021 ◽  
Vol 139 ◽  
pp. 104428
Author(s):  
Xin Liu ◽  
Yu Wang
Keyword(s):  
Hydraulic Model ◽  
Monitoring Data ◽  
Model Parameters ◽  
Subset Simulation ◽  
Selection Of

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 Initial value selection of the model parameters in the curve fitting phase of the dynamic SPECT imaging

2018 ◽  
Vol 51 (27) ◽  
pp. 241-246
Author(s):  
Zsófia Barna ◽  
Ákos Szlávecz ◽  
Gábor Hesz ◽  
Péter Somogyi ◽  
Katalin Kovács ◽  
...  
Keyword(s):  
Curve Fitting ◽  
Spect Imaging ◽  
Model Parameters ◽  
Dynamic Spect ◽  
Initial Value ◽  
Selection Of

Selection of indicator species for State of the Environment reporting: A case study from New Zealand

2010 ◽  
Vol 16 (2) ◽  
pp. 76 ◽  
Author(s):  
Joanne M. Hoare ◽  
Colin F. J. O’Donnell ◽  
Elaine F. Wright
Keyword(s):  
New Zealand ◽  
Indicator Species ◽  
Species Concepts ◽  
Monitoring Data ◽  
Environmental Reporting ◽  
Environmental Attributes ◽  
State Of The Environment ◽  
Selection Of

Indicator species approaches are widely used in conservation as a shortcut to measuring attributes of species and ecosystems. A variety of indicator species concepts are in use and are applicable to a range of situations. Indicator species are increasingly being used in environmental reporting to evaluate trends in environmental attributes quantitatively. We use the most recent State of the Environment report from New Zealand as a case study to evaluate: (1) how indicator species concepts are being applied to environmental reporting and (2) the selection of individual species as indicators. At present indicator species used in environmental reporting in New Zealand reflect biases in national monitoring data towards forest-dwelling, terrestrial vertebrates that are vulnerable to predation by introduced mammals. Scientific literature generally supports links between selected taxa and the aspect of ecosystem health they are purported to indicate, but their roles as long-term indicators of environmental health have yet to be evaluated. A primary goal of State of the Environment reporting is to set a benchmark against which environmental outcomes can be monitored over time; thus it is recognized that taxa reported should represent a broader range of environmental attributes. However, selection of taxa for environmental reporting is severely constrained by limited national species monitoring data. A strategic approach to national measurement, storage and analysis of long-term monitoring data is required to support selection of representative species for environmental reporting. We support current initiatives to select taxa for future measurement and reporting in an objective, transparent manner and recommend that they encompass representation of: (1) taxonomic diversity, (2) ecosystem types, (3) key environmental pressures and (4) threat status.

scholarly journals Centralized Maintenance Time Prediction Algorithm for Freight Train Wheels Based on Remaining Useful Life Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Hongmei Shi ◽  
Jinsong Yang ◽  
Jin Si
Keyword(s):  
Wiener Process ◽  
Process Model ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Monitoring Data ◽  
Prediction Algorithm ◽  
Model Parameters ◽  
Freight Train ◽  
Fixed Group ◽  
Useful Life

Many freight trains for special lines have in common the characteristics of a fixed group. Centralized Condition-Based Maintenance (CCBM) of key components, on the same freight train, can reduce maintenance costs and enhance transportation efficiency. To this end, an optimization algorithm based on the nonlinear Wiener process is proposed, for the prediction of the train wheels Remaining Useful Life (RUL) and the centralized maintenance timing. First, Hodrick–Prescott (HP) filtering algorithm is employed to process the raw monitoring data of wheel tread wear, extracting its trend components. Then, a nonlinear Wiener process model is constructed. Model parameters are calculated with a maximum likelihood estimation and the general deterioration parameters of wheel tread wear are obtained. Then, the updating algorithm for the drift coefficient is deduced using Bayesian formula. The online updating of the model is realized, based on individual wheel monitoring data, while a probability density function of individual wheel RUL is obtained. A prediction method of RUL for centralized maintenance is proposed, based on two set thresholds: “maintenance limit” and “the ratio of limit-arriving.” Meanwhile, a CCBM timing prediction algorithm is proposed, based on the expectation distribution of individual wheel RUL. Finally, the model is validated using a 500-day online monitoring data on a fixed group, consisting of 54 freight train cars. The validation result shows that the model can predict the wheels RUL of the train for CCBM. The proposed method can be used to predict the maintenance timing when there is a large number of components under the same working conditions and following the same path of degradation.

A Mathematical Model of Evaporation and Dissolution From Oil Spills on Ice, Land, Water and Under Ice

1975 ◽  
Vol 10 (1) ◽  
pp. 132-141 ◽  
Author(s):  
P.J. Leinonen ◽  
D. Mackay
Keyword(s):  
Mass Transfer ◽  
Crude Oil ◽  
Transfer Coefficient ◽  
Mass Transfer Coefficient ◽  
Oil Spills ◽  
Aquatic Toxicity ◽  
Eddy Diffusivity ◽  
Model Parameters ◽  
Evaporation Flux ◽  
Selection Of

Abstract Mathematical models are presented which quantify the processes of evaporation and dissolution of components of crude oil in three situations: a spill on water, a spill on ice, and a spill under ice cover in which the oil lies between the water and ice phases. Constant spill area is assumed. The evaporation flux is calculated using a mass transfer coefficient based on windspeed and spill dimensions. The dissolution flux can be calculated from two models, a mass transfer coefficient approach and an eddy diffusivity approach involving the integration of a set of partial differential equations in depth and time. The selection of model parameters is discussed. For the three physical situations, using a synthetic crude oil, results are presented giving the relative rates of evaporation and dissolution and the aqueous phase concentration of selected hydrocarbons. The implications of the results for clean-up technology and aquatic toxicity are discussed, particularly with regard to spills under ice.

Automatic selection of arterial input function in DSC-MRI measurements for calculation of brain perfusion parameters using parametric modelling

2018 ◽  
Vol 13 (6) ◽  
pp. 58
Author(s):  
Seweryn Lipiński ◽  
Renata Kalicka
Keyword(s):  
Arterial Input Function ◽  
Regression Function ◽  
Input Function ◽  
Model Parameters ◽  
Parametric Modelling ◽  
Automatic Selection ◽  
Dsc Mri ◽  
Mri Measurements ◽  
Selection Of

A novel method and algorithm of automatic selection of arterial input function (AIF) is presented and its efficiency is proved using exemplary DSC-MRI measurements. The method chooses AIF devoted to a particular purpose, which is calculation of perfusion parameters with the use of parametric modelling of DSC-MRI data. The quality of medical diagnosis made on the basis of perfusion parameters depends on the quality of these parameters, which in turn is determined by the quality of the AIF signal. The proposed algorithm combines physiological requirements for AIF with mathematical criteria. The choice of parametric approach, instead of black-box modelling, allows better understanding of the investigated system functioning, as model parameters may be credited with physical interpretation. Furthermore, using multi-compartmental model of the DSC-MRI data with AIF regression function in an exponential form, gives direct, analytic results concerning the basic descriptors of AIF. The method chooses candidates for AIF on the basis of the descriptors quality. This step allows rejecting measurements which do not fulfil fundamental requirements concerning AIF from the physiological point of view. As these requirements are met, the next criterion can be adopted, that is the quality of fitting the regression function to measurements. The final step is choosing the AIF for calculating perfusion parameters with the best accuracy, which is attainable thanks to implementing the AIF devoted particularly to parametric modelling.

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