scholarly journals Integrated Model for Water, Food, Energy and Human Development

10.29007/46pc ◽  
2018 ◽  
Author(s):  
Korinus Nixon Waimbo ◽  
Dragan Savic ◽  
Fayyaz Ali Memon
Keyword(s):  
Human Development ◽  
Root Mean Square ◽  
Food Systems ◽  
Behaviour Pattern ◽  
Test Model ◽  
Mean Square ◽  
Actual System ◽  
Food Energy ◽  
Percent Error ◽  
The Impact

Water, energy and food are basic needs crucial to human survival but also pervade many aspects of human development. Systemically, they are vastly interdependent. A system dynamics model comprises five modules, namely water, food, energy, demographic, and human development, is being constructed. The aim is to evaluate the dynamics behaviour of water, energy and food systems and their linkages to human development at national scale. The model was simulated on annual basis from the year 1990 to 2015. It was then tested against national historical data of Indonesia. Analysis of error using mean-square error, root mean square percent error, and Theil inequality statistics were performed to test model behaviour. Preliminary results show that most of the variables such as total population, income per capita, human development index, and sectoral water demands have root mean square percent error below 10% that indicate the model produces similar behaviour pattern to the actual system. As part of the future work, once the model is fully constructed, it will be applied to assess the impact of a range of policy scenarios and implications on the water, energy and food sectors and on human development in Indonesia.

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.

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>

scholarly journals اقتراح استعمال مبدأ اعظم دالة انتروبي POME على توزيع كاما العام في تقدير احتمالات البقاء للسكان في العراق

2016 ◽  
Vol 22 (93) ◽  
pp. 454
Author(s):  
عمر عبد المحسن علي ◽  
رغدة زياد طارق
Keyword(s):  
Maximum Likelihood ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Square ◽  
Percent Error ◽  
Mean Absolute Percent Error ◽  
Generalized Gamma ◽  
Economic Survey ◽  
Absolute Percent Error

المستخلص: تم في هذا البحث تقدير دالة البقاء على قيد الحياة لبيانات تعاني من اضطراب وتشويش للمسح الاجتماعي والاقتصادي للأسرة في العراق 2012 (Iraq Household Socio-Economic Survey: IHSES II 2012) لبيانات فئات خماسية العمر تتبع توزيع كاما العام (Generalized Gamma: GG). واستعملت طريقتين للأغراض التقدير والموائمة fitting وهي طريقة مبدأ اعظم دالة انتروبي Principle of Maximizing Entropy: POME  وطريقة تمهيد لامعلمية بدالة لبّية Kernel ، للتغلب على المشاكل الرياضية التي تعتري التكاملات التي يتضمنها هذا التوزيع بالذات المتمثلة بتكامل دالة كاما الناقص، هذا الى جانب استعمال الطريقة التقليدية وهي الامكان الاعظم Maximum Likelihood: ML حيث تتم المقارنة على اساس اسلوب الجهاز المركزي للإحصاء في احتساب دالة البقاء من خلال برنامج MORTPAK كقيم حقيقية. وبعد ذلك القيام بالمقارنة باستعمال معيار جذر متوسط مربعات الخطأ Root Mean Square Error: RMSE  ، ومعيار متوسط مطلق نسبة الخطأ Mean Absolute Percent Error: MAPE  . وأظهرت النتائج أفضلية طريقة الانتروبي في تقدير دالة البقاء على الطرائق الاخرى.  

DELAY PREDICTION IN IOT AND TACTILE INTERNET USING MACHINE LEARNING APPROACH

2021 ◽  
Author(s):  
А.Р. АБДЕЛЛАХ ◽  
О.А. МАХМУД ◽  
А.И. ПАРАМОНОВ ◽  
А.Е. КУЧЕРЯВЫЙ
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Single Step ◽  
Mean Square ◽  
Percent Error ◽  
Mean Absolute Percent Error ◽  
Accuracy Measure ◽  
Delay Prediction ◽  
Absolute Percent Error

Предложены методы прогнозирования задержки в сетях интернета вещей и тактильного интернета при прогнозировании вперед на несколько шагов MSP (Multi-step ahead Prediction) и один шаг SSP (Single-step ahead Prediction). Использованы нелинейные авторегресионные рекуррентные нейронные сети с внешними входами NARX(NonlinearAutoregressive with Exogenous inputs) для временных рядов. Проведена оценка точности прогнозирования с помощью трех алгоритмов обучения нейронной сети (Trainlm, Traincgf, Trainrp) при использовании в качестве оценок точности прогнозирования среднеквадратичной ошибки RMSE(Root Mean Square Error) и средней абсолютной ошибки в процентах MAPE(Mean Absolute Percent Error). In this paper, we perform the delay prediction in IoT and tactile Internet communication networks using a multistep ahead prediction (MSP) and single-step ahead prediction (SSP) with Time Series NARX (Nonlinear AutoRegressive with eXogenous inputs) Recurrent Neural Networks. The prediction accuracy has been evaluated using three neural network training algorithms (Trainlm, Traincgf, Trainrp) using the RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percent Error) as predictive accuracy measure.

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 Modeling Advertising Carryover in Fluid Milk: Comparison of Alternative Lag Specifications

1993 ◽  
Vol 22 (1) ◽  
pp. 10-19 ◽  
Author(s):  
Meenakshi Venkateswaran ◽  
Henry W. Kinnucan ◽  
Hui-Shung Chang
Keyword(s):  
New York ◽  
New York City ◽  
Mean Square Error ◽  
Root Mean Square ◽  
York City ◽  
Mean Square ◽  
Market Data ◽  
Percent Error ◽  
Milk Market ◽  
Fluid Milk

The performance of restricted estimators such as Almon and Shiller in modeling advertising carryover is tested and compared to the unrestricted OLS estimator, using 1971–1988 monthly New York City fluid milk market data. Results indicate that in the absence of autocorrelation and multicollinearity among the lagged advertising variables, the unrestricted OLS estimator is still the preferred estimator, based on Mean Square Error and Root Mean Square Percent Error criteria. In this case, the Almon and Shiller estimators perform equally well, although next only to the OLS estimator. In the presence of autocorrelation or multicollinearity however, the restricted estimators may outperform the OLS estimator, in a MSE sense, with the flexible Shiller estimator (which subsumes the Almon) being more desirable.

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 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;

scholarly journals Uncertainties of ground-based microwave radiometer retrievals in zenith and off-zenith methods under snow conditions

2016 ◽  
Author(s):  
Wengang Zhang ◽  
Guirong Xu ◽  
Yuanyuan Liu ◽  
Guopao Yan ◽  
Shengbo Wang
Keyword(s):  
Relative Humidity ◽  
Root Mean Square ◽  
Microwave Radiometer ◽  
Mean Square ◽  
Atmospheric Conditions ◽  
Vapor Density ◽  
Heavy Snowfall ◽  
Snow Conditions ◽  
The Impact ◽  
Mean Square Errors

Abstract. This paper is to investigate the uncertainties of microwave radiometer (MWR) retrievals in snow conditions and also explore the discrepancies of MWR retrievals in zenith and off-zenith methods. The MWR retrievals were averaged in the ±15 min period centered at sounding times of 00:00 and 12:00 UTC and compared with the radiosonde observations (RAOBs). In general, the MWR retrievals have a better correlation with RAOB profiles in off-zenith method than in zenith method, and the biases (MWR observations minus RAOBs) and root mean square errors (RMSEs) between MWR and RAOB are also clearly reduced in off-zenith method. The biases of temperature, relative humidity, and vapor density decrease from 4.6 K, 9 %, and 1.43 g m−3 in zenith method to −0.6 K, −2 %, and 0.10 g m−3 in off-zenith method, respectively. The discrepancies between the MWR retrievals and the RAOB profiles along with the altitude present the same situation. Case studies show that the impact of snow on accuracies of the MWR retrievals is more serious in heavy snowfall than that in light snowfall, but the off-zenith method can mitigate the impact of snowfall. The MWR measurements become less accurate in snowfall is mainly due to the retrieving method which does not consider the effect of snow, and the accumulated snow on the top of radome increases the signal noise of MWR measurement. As the snowfall drops away by gravity in the sides of the radome and the off-zenith observations are more representative of the atmospheric conditions for RAOBs.

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