Improving the reliability of sub‐seasonal forecasts of high and low flows by using a flow‐dependent non‐parametric model

2021 ◽  
Author(s):  
David McInerney ◽  
Mark Thyer ◽  
Dmitri Kavetski ◽  
Richard Laugesen ◽  
Fitsum Woldemeskel ◽  
...  
Keyword(s):  
Parametric Model ◽  
Seasonal Forecasts ◽  
Low Flows ◽  
Non Parametric

ESTIMATION OF PRESSURE DROP FOR NON-NEWTONIAN LIQUID FLOW THROUGH BENDS USING ADAPTIVE NON-PARAMETRIC MODEL

2020 ◽  
Vol 47 (1) ◽  
pp. 59-69
Author(s):  
Suman Debnath ◽  
Anirban Banik ◽  
Tarun Kanti Bandyopadhyay ◽  
Mrinmoy Majumder ◽  
Apu Kumar Saha
Keyword(s):  
Pressure Drop ◽  
Liquid Flow ◽  
Parametric Model ◽  
Newtonian Liquid ◽  
Flow Through ◽  
Non Parametric

scholarly journals Estimating lengths-of-stay of hospitalized COVID-19 patients using a non-parametric model: a case study in Galicia (Spain)

2021 ◽  
pp. 1-14
Author(s):  
Ana López-Cheda ◽  
María-Amalia Jácome ◽  
Ricardo Cao ◽  
Pablo M. De Salazar
Keyword(s):  
Parametric Model ◽  
Lengths Of Stay ◽  
Non Parametric

A Non Parametric Model for Magneto Rheological Dampers

1999 ◽  
Author(s):  
Mehdi Ahmadian ◽  
Xubin Song
Keyword(s):  
Test Data ◽  
Mr Damper ◽  
Parametric Model ◽  
Parametric Models ◽  
Magnetic Saturation ◽  
Mr Dampers ◽  
Electro Magnetic ◽  
Magneto Rheological ◽  
Force Characteristics ◽  
Non Parametric

Abstract A non-parametric model for magneto-rheological (MR) dampers is presented. After discussing the merits of parametric and non-parametric models for MR dampers, the test data for a MR damper is used to develop a non-parametric model. The results of the model are compared with the test data to illustrate the accuracy of the model. The comparison shows that the non-parametric model is able to accurately predict the damper force characteristics, including the damper non-linearity and electro-magnetic saturation. It is further shown that the parametric model can be numerically solved more efficiently than the parametric models.

scholarly journals Comparison of Canopy Closure Estimation of Plantations Using Parametric, Semi-Parametric, and Non-Parametric Models Based on GF-1 Remote Sensing Images

Forests ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 597 ◽  
Author(s):  
Jiarui Li ◽  
Xuegang Mao
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Parametric Model ◽  
Canopy Closure ◽  
Mean Square ◽  
Remote Sensing Images ◽  
Estimation Models ◽  
Forest Farm ◽  
Non Parametric

Canopy closure (CC) is an important parameter in forest ecosystems and has diverse applications in a wide variety of fields. Canopy closure estimation models, using a combination of measured data and remote sensing data, can largely replace traditional survey methods for CC. However, it is difficult to estimate the forest CC based on high spatial resolution remote sensing images. This study used China Gaofen-1 satellite (GF-1) images, and selected China’s north temperate Wangyedian Forest Farm (WYD) and subtropical Gaofeng Forest Farm (GF) as experimental areas. A parametric model (multiple linear regression (MLR)), non-parametric model (random forest (RF)), and semi-parametric model (generalized additive model (GAM)) were developed. The ability of the three models to estimate the CC of plantations based on high spatial resolution remote sensing GF-1 images and their performance in the two experimental areas was analyzed and compared. The results showed that the decision coefficient (R2), root mean square error (RMSE), and relative root mean square error (rRMSE) values of the parametric model (MLR), semi-parametric model (GAM), and non-parametric model (RF) for the WYD forest ranged from 0.45 to 0.69, 0.0632 to 0.0953, and 9.98% to 15.05%, respectively, and in the GF forest the R2, RMSE, and rRMSE values ranged from 0.40 to 0.59, 0.0967 to 0.1152, and 16.73% to 19.93%, respectively. The best model in the two study areas was the GAM and the worst was the RF. The accuracy of the three models established in the WYD was higher than that in the GF area. The RMSE and rRMSE values for the MLR, GAM, and RF established using high spatial resolution GF-1 remote sensing images in the two test areas were within the scope of existing studies, indicating the three CC estimation models achieved satisfactory results.

Efficiency and ownership in electricity distribution: A non-parametric model of the Turkish experience

1996 ◽  
Vol 18 (1-2) ◽  
pp. 1-23 ◽  
Author(s):  
Necmiddin Bagdadioglu ◽  
Catherine M. Waddams Price ◽  
Thomas G. Weyman-Jones
Keyword(s):  
Parametric Model ◽  
Electricity Distribution ◽  
Non Parametric

scholarly journals Extreme Quantiles Dynamic Line Rating Forecasts and Application on Network Operation

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3090
Author(s):  
Romain Dupin ◽  
Laura Cavalcante ◽  
Ricardo J. Bessa ◽  
Georges Kariniotakis ◽  
Andrea Michiorri
Keyword(s):  
Extreme Events ◽  
Parametric Model ◽  
Safe Operation ◽  
Extreme Quantiles ◽  
Application Method ◽  
Probabilistic Forecasts ◽  
Network Operation ◽  
New Distribution ◽  
Non Parametric

This paper presents a study on dynamic line rating (DLR) forecasting procedure aimed at developing a new methodology able to forecast future ampacity values for rare and extreme events. This is motivated by the belief that to apply DLR network operators must be able to forecast their values and this must be based on conservative approaches able to guarantee the safe operation of the network. The proposed methodology can be summarised as follows: firstly, probabilistic forecasts of conductors’ ampacity are calculated with a non-parametric model, secondly, the lower part of the distribution is replaced with a new distribution calculated with a parametric model. The paper presents also an evaluation of the proposed methodology in network operation, suggesting an application method and highlighting the advantages. The proposed forecasting methodology delivers a high improvement of the lowest quantiles’ reliability, allowing perfect reliability for the 1% quantile and a reduction of roughly 75% in overconfidence for the 0.1% quantile.

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