Estimation of hydrodynamic parameters for underwater systems using a simple off-line regression method: a case study

2018 ◽  
Vol 24 (3) ◽  
pp. 968-983 ◽  
Author(s):  
Thiyagarajan Ranganathan ◽  
Vijendra Singh ◽  
Asokan Thondiyath
Keyword(s):  
Regression Method ◽  
Hydrodynamic Parameters

scholarly journals PEMODELAN REGRESI RIDGE ROBUST-MM DALAM PENANGANAN MULTIKOLINIERITAS DAN PENCILAN (Studi Kasus : Faktor-Faktor yang Mempengaruhi AKB di Jawa Tengah Tahun 2017)

2019 ◽  
Vol 8 (1) ◽  
pp. 24-34
Author(s):  
Eka Destiyani ◽  
Rita Rahmawati ◽  
Suparti Suparti
Keyword(s):  
Least Squares ◽  
Robust Regression ◽  
Ordinary Least Squares ◽  
Regression Method ◽  
Predictor Variables ◽  
Healthy Life ◽  
Significance Level ◽  
Central Java ◽  
Regression Parameters

The Ordinary Least Squares (OLS) is one of the most commonly used method to estimate linear regression parameters. If multicollinearity is exist within predictor variables especially coupled with the outliers, then regression analysis with OLS is no longer used. One method that can be used to solve a multicollinearity and outliers problems is Ridge Robust-MM Regression. Ridge Robust-MM  Regression is a modification of the Ridge Regression method based on the MM-estimator of Robust Regression. The case study in this research is AKB in Central Java 2017 influenced by population dencity, the precentage of households behaving in a clean and healthy life, the number of low-weighted baby born, the number of babies who are given exclusive breastfeeding, the number of babies that receiving a neonatal visit once, and the number of babies who get health services. The result of estimation using OLS show that there is violation of multicollinearity and also the presence of outliers. Applied ridge robust-MM regression to case study proves ridge robust regression can improve parameter estimation. Based on t test at 5% significance level most of predictor variables have significant effect to variable AKB. The influence value of predictor variables to AKB is 47.68% and MSE value is 0.01538.Keywords:  Ordinary  Least  Squares  (OLS),  Multicollinearity,  Outliers,  RidgeRegression, Robust Regression, AKB.

Hydrodynamic parameters assessment using specialized techniques, in the context of low flows. Case study Epurasu Branch - Danube River

2019 ◽  
Author(s):  
D. György ◽  
G. Tudor ◽  
A. F. Nicolae ◽  
B. Uriţescu ◽  
C. Cîrstinoiu ◽  
...  
Keyword(s):  
Danube River ◽  
Hydrodynamic Parameters ◽  
Low Flows

Partial Linear Method for Background Harmonic Voltage Estimation

2014 ◽  
Vol 960-961 ◽  
pp. 710-715
Author(s):  
Xiu Fang Jia ◽  
Shao Guang Zhang ◽  
Hai Qing An
Keyword(s):  
Linear Regression ◽  
Taylor Series ◽  
Linear Method ◽  
Test System ◽  
Regression Method ◽  
Linear Regression Method ◽  
Bandwidth Control ◽  
Harmonic Voltage ◽  
Partial Linear

The linear regression method which will be influenced by fluctuations could only calculate constant background harmonic voltage. To make up the limitation, this paper studies partial linear method. The method expands fluctuant background harmonic voltage at a time in accordance with Taylor series. On the basis of least sum of square error, the objective function selected by the method considers the influence of weight and uses bandwidth control each size of weight. This method can calculate fluctuant background harmonic voltage accurately. A case study based on the IEEE 14-bus test system is conducted and the results indicate that fluctuant background harmonic voltage can be obtained effectively and accurately by the proposed method.

The comparison of Holt–Winters method and Multiple regression method: A case study

Energy ◽  
2016 ◽  
Vol 109 ◽  
pp. 266-276 ◽  
Author(s):  
Liljana Ferbar Tratar ◽  
Ervin Strmčnik
Keyword(s):  
Multiple Regression ◽  
Regression Method ◽  
Multiple Regression Method

scholarly journals Numerical simulation of groundwater in an unconfined aquifer with a novel hybrid model (case study: Birjand Aquifer, Iran)

2021 ◽  
Author(s):  
Ali Mohtashami ◽  
Seyed Arman Hashemi Monfared ◽  
Gholamreza Azizyan ◽  
Abolfazl Akbarpour
Keyword(s):  
Numerical Modeling ◽  
Data Assimilation ◽  
Hybrid Model ◽  
Numerical Models ◽  
Unconfined Aquifer ◽  
Model Case ◽  
Hydrodynamic Parameters ◽  
East Of Iran ◽  
Assimilation Model

Abstract In recent decades, due to the population growth and low precipitation, the overexploitation of ground water resources has become an important issue. To ensure a sustainable scheme for these resources, understanding the behavior of the aquifers is a key step. This study takes a numerical modeling approach to investigate the behavior of an unconfined aquifer in an arid area located in the east of Iran. A novel hybrid model is proposed that couples the numerical modeling to a data assimilation model to remove the uncertainty in the hydrodynamic parameters of the aquifer including the hydraulic conductivity coefficients and specific yields. The uncertainty that exists in these parameters results in unreliability of the head values acquired from the models. Meshless local Petrov-Galerkin (MLPG) is used as the numerical model, and particle filter (PF) is our data assimilation model. These models are implemented in the MATLAB software. We have calibrated and validated our PF-MLPG model by the observation head data from the piezometers. The RMSE in head values for our model and other commonly used numerical models in the literature including the finite difference method and MPLG are calculated as 0.166, 1.197 and 0.757 m, respectively. This fact shows the necessity of using this method in each aquifer.

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