scholarly journals Impact of different centroid means on the accuracy of orthometric height modelling by geometric geoid method

2020 ◽  
Vol 6 (4) ◽  
pp. 124 ◽  
Author(s):  
Oluyori P. Dare ◽  
Eteje S. Okiemute
Keyword(s):  
Root Mean Square ◽  
Arithmetic Mean ◽  
Orthometric Height ◽  
Harmonic Mean ◽  
Geometric Method ◽  
Mean Square ◽  
Geoid Model ◽  
Microsoft Excel ◽  
Orthometric Heights ◽  
The Impact

<p class="abstract"><strong>Background:</strong> Orthometric height, as well as geoid modelling using the geometric method, requires centroid computation. And this can be obtained using various models, as well as methods. These methods of centroid mean computation have impacts on the accuracy of the geoid model since the basis of the development of the theory of each centroid mean type is different. This paper presents the impact of different centroid means on the accuracy of orthometric height modelling by geometric geoid method.</p><p class="abstract"><strong>Methods:</strong> DGPS observation was carried out to obtain the coordinates and ellipsoidal heights of selected points. The centroid means were computed with the coordinates using three different centroid means models (arithmetic mean, root mean square and harmonic mean). The computed centroid means were entered accordingly into a Microsoft Excel program developed using the Multiquadratic surface to obtain the model orthometric heights at various centroid means. The root means square error (RMSE) index was applied to obtain the accuracy of the model using the known and the model orthometric heights obtained at various centroid means.  </p><p class="abstract"><strong>Results:</strong> The computed accuracy shows that the arithmetic mean method is the best among the three centroid means types.</p><p class="abstract"><strong>Conclusions:</strong> It is concluded that the arithmetic mean method should be adopted for centroid computation, as well as orthometric height modelling using the geometric method.</p>

Examining the Impact of and Sensitivity of Fit Indices to Omitting Covariates Interaction Effect in Multilevel Multiple-Indicator Multiple-Cause Models

2021 ◽  
pp. 001316442199240
Author(s):  
Chunhua Cao ◽  
Eun Sook Kim ◽  
Yi-Hsin Chen ◽  
John Ferron
Keyword(s):  
Root Mean Square ◽  
Interaction Effect ◽  
Intraclass Correlation ◽  
Information Criteria ◽  
Parameter Estimates ◽  
Mean Square ◽  
Fit Indices ◽  
Cluster Number ◽  
Multiple Indicator ◽  
The Impact

This study examined the impact of omitting covariates interaction effect on parameter estimates in multilevel multiple-indicator multiple-cause models as well as the sensitivity of fit indices to model misspecification when the between-level, within-level, or cross-level interaction effect was left out in the models. The parameter estimates produced in the correct and the misspecified models were compared under varying conditions of cluster number, cluster size, intraclass correlation, and the magnitude of the interaction effect in the population model. Results showed that the two main effects were overestimated by approximately half of the size of the interaction effect, and the between-level factor mean was underestimated. None of comparative fit index, Tucker–Lewis index, root mean square error of approximation, and standardized root mean square residual was sensitive to the omission of the interaction effect. The sensitivity of information criteria varied depending majorly on the magnitude of the omitted interaction, as well as the location of the interaction (i.e., at the between level, within level, or cross level). Implications and recommendations based on the findings were discussed.

Measuring Rights-Based Practice: Introducing the Human Rights Methods in Social Work Scales

2019 ◽  
Vol 50 (1) ◽  
pp. 222-242 ◽  
Author(s):  
Jane McPherson ◽  
Neil Abell
Keyword(s):  
Human Rights ◽  
Factor Analysis ◽  
Social Work ◽  
Social Workers ◽  
Root Mean Square ◽  
Interdisciplinary Collaboration ◽  
Social Work Practice ◽  
Mean Square ◽  
Convenience Sample ◽  
The Impact

Abstract This article introduces a measurable framework for rights-based social work practice and an accompanying set of instruments, the Human Rights Methods in Social Work (HRMSW) scales: (i) ‘participation’, (ii) ‘non-discrimination’, (iii) ‘strengths perspective’, (iv) ‘micro/macro integration’, (v) ‘capacity-building’, (vi) ‘community and interdisciplinary collaboration’, (vii) ‘activism’ and (viii) ‘accountability’. These scales, designed for use by researchers, educators and practitioners, are the first to measure social workers’ use of rights-based methods. An electronic survey was used to collect data from a convenience sample of 1,014 licensed US social workers, and a confirmatory factor analysis was used to validate the scales’ psychometric properties. A respecified model using eight error covariances fit the data (χ2/df ratio = 2.9; comparative fit index (CFI) = 0.91; tucker lewis index (TLI) = 0.90; root mean square error of approximation (RMSEA) = 0.04; standardized root mean square residual (SRMR) = 0.07). Thus, factor analysis yielded a set of eight related scales—collectively called the HRMSW—each measuring a different human rights practice method that social workers can use to promote human dignity and the rights-based principles of participation, accountability and non-discrimination. Scholars argue that ‘human rights’ are a more appropriate yardstick for measuring the impact of social work intervention rather than our traditional aim of social justice; the HRMSW scales can help us begin to test this proposition.

scholarly journals Assessment of the Accuracy of Recent Empirical and Assimilated Tidal Models for the Great Barrier Reef, Australia, Using Satellite and Coastal Data

2019 ◽  
Vol 11 (10) ◽  
pp. 1211 ◽  
Author(s):  
Fardin Seifi ◽  
Xiaoli Deng ◽  
Ole Baltazar Andersen
Keyword(s):  
Great Barrier Reef ◽  
Sea Level ◽  
Root Mean Square ◽  
Tide Gauge ◽  
Bottom Topography ◽  
Deep Ocean ◽  
Mean Square ◽  
Barrier Reef ◽  
In Situ Data ◽  
The Impact

The latest satellite and in situ data are a fundamental source for tidal model evaluations. In this work, the satellite missions TOPEX/Poseidon, Jason-1, Jason-2 and Sentinel-3A, together with tide gauge data, were used to investigate the performance of recent regional and global tidal models over the Great Barrier Reef, Australia. Ten models, namely, TPXO8, TPXO9, EOT11a, HAMTIDE, FES2012, FES2014, OSUNA, OSU12, GOT 4.10 and DTU10, were considered. The accuracy of eight major tidal constituents (i.e., K1, O1, P1, Q1, M2, S2, N2 and K2) and one shallow water constituent (M4) were assessed based on the analysis of sea-level observations from coastal tide gauges and altimetry data (TOPEX series). The outcome was compared for four different subregions, namely, the coastline, coastal, shelf and deep ocean zones. Sea-level anomaly data from the Sentinel-3A mission were corrected using the tidal heights predicted by each model. The root mean square values of the sea level anomalies were then compared. According to the results, FES2012 compares more favorably to other models with root mean square (RMS) values of 10.9 cm and 7.7 cm over the coastal and shelf zones, respectively. In the deeper sections, the FES2014 model compares favorably at 7.5 cm. In addition, the impact of sudden fluctuations in bottom topography on model performances suggest that a combination of bathymetric variations and proximity to the coast or islands contributes to tidal height prediction accuracies of the models.

scholarly journals Establishment of local geometric geoid model for Busoga, Uganda

2020 ◽  
Vol 8 (3) ◽  
pp. 139-148
Author(s):  
Bruno Kyamulesire ◽  
Sylvester Okiemute Eteje ◽  
Paul Dare Oluyori
Keyword(s):  
Least Squares ◽  
Geometric Method ◽  
Dual Frequency ◽  
Comparison Result ◽  
Geoid Model ◽  
Microsoft Excel ◽  
Least Squares Adjustment ◽  
Test Points ◽  
Local Geoid ◽  
Gnss Observations

The importance of the local geoid model for the computation of accurate geoid heights, as well as orthometric heights used for engineering constructions, necessitated its establishment in areas, regions or countries. Consequently, this study establishes the local geometric geoid model of Busoga, Uganda, using the geometric method. A total of 26 points were used in the study, 20 points for the development of the model and 6 test points. GNSS observations were acquired with Trimble GNSS dual-frequency receivers and processed with Bernese (V5.2) and Spectra Precision Survey Office (v4.1) software to obtain the coordinates and ellipsoidal heights of the points. Differences between the existing orthometric and ellipsoidal heights were computed to obtain the geoid heights. The Least squares adjustment technique was applied to determine the fit, as well as the Bicubic and Multiquadratic models’ parameters. The Root Mean Squares Error (RMSE) index was used to compute the accuracy of the models. The geoid models were compared with their RMSE, as well as accuracy to determine which of them is more suitable for application in the study area. The comparison result shows that the Multiquadratic geoid model is more suitable for implementation in the study area. A Microsoft Excel program was developed for the application of the model in the study area.

scholarly journals The Poor Fit of Model Fit for Selecting Number of Factors in Exploratory Factor Analysis for Scale Evaluation

2020 ◽  
pp. 001316442094289
Author(s):  
Amanda K. Montoya ◽  
Michael C. Edwards
Keyword(s):  
Factor Analysis ◽  
Root Mean Square ◽  
Exploratory Factor Analysis ◽  
Model Fit ◽  
Mean Square ◽  
Fit Indices ◽  
Scale Evaluation ◽  
Number Of Factors ◽  
Model Fit Indices ◽  
The Impact

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the ubiquity of correlated residuals and imperfect model specification. Our research focuses on a scale evaluation context and the performance of four standard model fit indices: root mean square error of approximate (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker–Lewis index (TLI), and two equivalence test-based model fit indices: RMSEAt and CFIt. We use Monte Carlo simulation to generate and analyze data based on a substantive example using the positive and negative affective schedule ( N = 1,000). We systematically vary the number and magnitude of correlated residuals as well as nonspecific misspecification, to evaluate the impact on model fit indices in fitting a two-factor exploratory factor analysis. Our results show that all fit indices, except SRMR, are overly sensitive to correlated residuals and nonspecific error, resulting in solutions that are overfactored. SRMR performed well, consistently selecting the correct number of factors; however, previous research suggests it does not perform well with categorical data. In general, we do not recommend using model fit indices to select number of factors in a scale evaluation framework.

Deep Learning Stream Temperature Model: Recommendations for Modeling Gauged and Ungauged Basins

2021 ◽  
Author(s):  
Farshid Rahmani ◽  
Kathryn Lawson ◽  
Samantha Oliver ◽  
Alison Appling ◽  
Chaopeng Shen
Keyword(s):  
Deep Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
The United States ◽  
Data Availability ◽  
Efficiency Coefficient ◽  
Mean Square ◽  
Training Set ◽  
The Impact

&lt;p&gt;Stream water temperature (T&lt;sub&gt;s&lt;/sub&gt;) is a variable that plays a pivotal role in managing water resources. We used the long short-term memory (LSTM) deep learning architecture to develop a basin centric single T&lt;sub&gt;s&lt;/sub&gt; model based on general meteorological data and basin meteo-geological attributes. We created a strong tool for long-term Ts projection and subsequently, improved the Ts model using novel approaches. We investigated the impact of both observed and simulated streamflow data on improving the model accuracy. At a national scale, we obtained a median root-mean-square error (RMSE) of 0.69 &lt;sup&gt;o&lt;/sup&gt;C, and Nash-Sutcliffe model efficiency coefficient (NSE) of 0.985, which are marked improvements over previous values reported in previous studies. In order to test the performance of the model on basins ranging from basins with extensive data to unmonitored basins, we used more than 400 basins with different data-availability groups (DAG) across the continent of the United States to explore how to assemble the training dataset for both monitored and unmonitored basins. Best root-mean-square error (RMSE) for sites with extensive (99%), intermediate (60%), scarce (10%) and absent (0%) data for training were 0.75, 0.837, 0.889, and 1.595 &lt;sup&gt;o&lt;/sup&gt;C, respectively. We observed the negative effect of the presence of reservoirs in T&lt;sub&gt;s&lt;/sub&gt; modeling. Our results illustrated that the most suitable training set should be different in modeling basins with different availability of observed data. for predicting T&lt;sub&gt;s&lt;/sub&gt; in a monitored basin, including basins that have at least equal DAG with that particular basin will result in most accurate predictions, however, for T&lt;sub&gt;s&lt;/sub&gt; prediction in ungauged basin, including all basins in training section will generate the best model, showing a more diverse training set. Furthermore, to decrease overfitting produced by attributes for PUB application, we could improve the accuracy of the model using input-selection ensemble method. We got median correlation higher than 0.90 for PUB after seasonality was removed which is still high. While many T&lt;sub&gt;s&lt;/sub&gt; prediction models showed better performance in summer, our model was on the opposite side. We found a strong relationship between general available daily meteorological variables and catchment attributes with the presented T&lt;sub&gt;s&lt;/sub&gt; model. However, our results indicate that combining physics-based criteria to the model can improve the prediction of temperature in river networks.&lt;/p&gt;&lt;p&gt;.&lt;/p&gt;

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