Application of Visual MODFLOW to the Analysis of Boundary Conditions for a Phreatic Porous Aquifer Using Limited Available Information: A Case Study

2021 ◽  
Vol 44 ◽  
Author(s):  
Milena Stefany Lage Almeida ◽  
JOSÉ AUGUSTO COSTA GONÇALVES
Keyword(s):  
Boundary Conditions ◽  
Groundwater Flow ◽  
Root Mean Square ◽  
Flow Behavior ◽  
Groundwater Resources ◽  
Absolute Error ◽  
Mean Square ◽  
Visual Modflow ◽  
Porous Aquifer ◽  
The Mean

The increasing water demand, especially in developing regions, continuously puts pressure on groundwater resources both quantitatively and qualitatively. Hydrogeological modeling is a tool used in planning and management of groundwater resources. The factors that interfere in groundwater flow dynamics can be determined by developing a conceptual model and they can be validated via a numerical model. The objective of the manuscript is the hydrogeological groundwater flow modeling of the phreatic porous aquifer of the Ribeirão Candidópolis catchment in the Itabira municipality, State of Minas Gerais (Brazil). The software used in this study is GMS: MODFLOW, which enabled a steady state flow regime modeling by means of the Finite Difference Method (FDM) and the parameters calibration from a semi-transient approach. To assess the performance of the model, the Mean Error (ME), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE) were calculated. The results proved to be compatible with the values observed in the field. After several adjustments of the boundary conditions, a Normalized Root Mean Square (NRMS) of 9.648% and a correlation coefficient of 0.993 were obtained. Despite the economic importance of the study area, studies made available on groundwater flow behavior are rare. The results obtained via modeling are in accordance with the data observed in the field and consequently our model can be used in the study of water level changes.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

scholarly journals Direct Calculation of Thermodynamic Wet-Bulb Temperature as a Function of Pressure and Elevation

2013 ◽  
Vol 30 (8) ◽  
pp. 1757-1765 ◽  
Author(s):  
Sayed-Hossein Sadeghi ◽  
Troy R. Peters ◽  
Douglas R. Cobos ◽  
Henry W. Loescher ◽  
Colin S. Campbell
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Direct Calculation ◽  
Ambient Air ◽  
Absolute Error ◽  
Mean Square ◽  
Air Temperatures ◽  
Order Polynomial ◽  
The Mean ◽  
Very High

Abstract A simple analytical method was developed for directly calculating the thermodynamic wet-bulb temperature from air temperature and the vapor pressure (or relative humidity) at elevations up to 4500 m above MSL was developed. This methodology was based on the fact that the wet-bulb temperature can be closely approximated by a second-order polynomial in both the positive and negative ranges in ambient air temperature. The method in this study builds upon this understanding and provides results for the negative range of air temperatures (−17° to 0°C), so that the maximum observed error in this area is equal to or smaller than −0.17°C. For temperatures ≥0°C, wet-bulb temperature accuracy was ±0.65°C, and larger errors corresponded to very high temperatures (Ta ≥ 39°C) and/or very high or low relative humidities (5% < RH < 10% or RH > 98%). The mean absolute error and the root-mean-square error were 0.15° and 0.2°C, respectively.

Accuracy Assessment of TanDEM-X 90 and CartoDEM Using ICESat-2 Datasets for Plain Regions of Ratlam City and Surroundings

2021 ◽  
Vol 10 (1) ◽  
pp. 59
Author(s):  
Unnati Yadav ◽  
Ashutosh Bhardwaj
Keyword(s):  
Mean Absolute Error ◽  
Accuracy Assessment ◽  
Absolute Error ◽  
Terrain Analysis ◽  
Mean Square ◽  
High Quality ◽  
Ice Cloud ◽  
The Mean ◽  
Cut And Fill ◽  
Statistical Results

The spaceborne LiDAR dataset from the Ice, Cloud, and Land Elevation Satellite (ICESat-2) provides highly accurate measurements of heights for the Earth’s surface, which helps in terrain analysis, visualization, and decision making for many applications. TanDEM-X 90 (90 m) and CartoDEM V3R1 (30 m) elevation are among the high-quality openly accessible DEM datasets for the plain regions in India. These two DEMs are validated against the ICESat-2 elevation datasets for the relatively plain areas of Ratlam City and its surroundings. The mean error (ME), mean absolute error (MAE), and root mean square error (RMSE) of TanDEM-X 90 DEM are 1.35 m, 1.48 m, and 2.19 m, respectively. The computed ME, MAE, and RMSE for CartoDEM V3R1 are 3.05 m, 3.18 m, and 3.82 m, respectively. The statistical results reveal that TanDEM-X 90 performs better in plain areas than CartoDEMV3R1. The study further indicates that these DEMs and spaceborne LiDAR datasets can be useful for planning various works requiring height as an important parameter, such as the layout of pipelines or cut and fill calculations for various construction activities. The TanDEM-X 90 can assist planners in quick assessments of the terrain for infrastructural developments, which otherwise need time-consuming traditional surveys using theodolite or a total station.

Direct numerical simulation of turbulence in injection-driven three-dimensional cylindrical flows

2011 ◽  
Vol 670 ◽  
pp. 176-203 ◽  
Author(s):  
JU ZHANG ◽  
THOMAS L. JACKSON
Keyword(s):  
Reynolds Number ◽  
Root Mean Square ◽  
Streamwise Velocity ◽  
Wall Turbulence ◽  
Wall Friction ◽  
Mean Square ◽  
The Mean ◽  
Wall Shear ◽  
Strong Injection ◽  
Near Wall

Incompressible turbulent flow in a periodic circular pipe with strong injection is studied as a simplified model for the core flow in a solid-propellant rocket motor and other injection-driven internal flows. The model is based on a multi-scale asymptotic approach. The intended application of the current study is erosive burning of solid propellants. Relevant analysis for easily accessible parameters for this application, such as the magnitudes, main frequencies and wavelengths associated with the near-wall shear, and the assessment of near-wall turbulence viscosity is focused on. It is found that, unlike flows with weak or no injection, the near-wall shear is dominated by the root mean square of the streamwise velocity which is a function of the Reynolds number, while the mean streamwise velocity is only weakly dependent on the Reynolds number. As a result, a new wall-friction velocity $\(u_\tau{\,=\,}\sqrt{\tau_w/\rho}\)$, based on the shear stress derived from the sum of the mean and the root mean square, i.e. $\(\tau_{w,inj} {\,=\,} \mu |{\partial (\bar{u}+u_{rms})}/{\partial r}|_w\)$, is proposed for the scaling of turbulent viscosity for turbulent flows with strong injection. We also show that the mean streamwise velocity profile has an inflection point near the injecting surface.

Metabolic Rate in Adolescent Athletes: The Development and Validation of New Equations, and Comparison to Previous Models

2020 ◽  
Vol 30 (4) ◽  
pp. 249-257
Author(s):  
Reid J. Reale ◽  
Timothy J. Roberts ◽  
Khalil A. Lee ◽  
Justina L. Bonsignore ◽  
Melissa L. Anderson
Keyword(s):  
Root Mean Square Error ◽  
Metabolic Rate ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Compartment Model ◽  
Absolute Error ◽  
Mean Square ◽  
Adolescent Athletes ◽  
Two Compartment Model

We sought to assess the accuracy of current or developing new prediction equations for resting metabolic rate (RMR) in adolescent athletes. RMR was assessed via indirect calorimetry, alongside known predictors (body composition via dual-energy X-ray absorptiometry, height, age, and sex) and hypothesized predictors (race and maturation status assessed via years to peak height velocity), in a diverse cohort of adolescent athletes (n = 126, 77% male, body mass = 72.8 ± 16.6 kg, height = 176.2 ± 10.5 cm, age = 16.5 ± 1.4 years). Predictive equations were produced and cross-validated using repeated k-fold cross-validation by stepwise multiple linear regression (10 folds, 100 repeats). Performance of the developed equations was compared with several published equations. Seven of the eight published equations examined performed poorly, underestimating RMR in >75% to >90% of cases. Root mean square error of the six equations ranged from 176 to 373, mean absolute error ranged from 115 to 373 kcal, and mean absolute error SD ranged from 103 to 185 kcal. Only the Schofield equation performed reasonably well, underestimating RMR in 51% of cases. A one- and two-compartment model were developed, both r2 of .83, root mean square error of 147, and mean absolute error of 114 ± 26 and 117 ± 25 kcal for the one- and two-compartment model, respectively. Based on the models’ performance, as well as visual inspection of residual plots, the following model predicts RMR in adolescent athletes with better precision than previous models; RMR = 11.1 × body mass (kg) + 8.4 × height (cm) − (340 male or 537 female).

scholarly journals GROUNDWATER CONCEPTUAL MODEL FOR PADDY IRRIGATION

2015 ◽  
Vol 78 (1-2) ◽  
Author(s):  
Ahmad Fikri Abdullah ◽  
Wan Amirul Wan Mustapa
Keyword(s):  
Groundwater Flow ◽  
Conceptual Model ◽  
Root Zone ◽  
Groundwater Resources ◽  
Visual Modflow ◽  
Alternative Source ◽  
Aquifer Storage ◽  
Paddy Irrigation ◽  
Rice Irrigation ◽  
Current Water

Hydrological modelling is representative of current, past or future hydrologic balance. It has been used widely in water-related problem such as drought, flood, water contamination and irrigation. Crops irrigation requires a lot of water to irrigate the root zone layer especially for paddy crops. With the current issues of water such as drought and pollution, an alternative source is needed to overcome the problem of water scarcity.  Generally Malaysia depends on the surface water to irrigate the crops with no aided of groundwater. This study focuses on the availability of groundwater resources to irrigate the paddy crops. Hence, a conceptual model of groundwater flow was developed to shows the current situation of the groundwater flow at the study area. Several models were developed to see if groundwater can be extracted using wells and be used as an alternative source for irrigation. The study area is located at Sawah Sempadan, which is one of Malaysia’s greeneries areas under Tanjung Karang Rice Irrigation Scheme (TAKRIS). The conceptual model is built by using Visual MODFLOW 4.2. The conceptual model shows the current water balance, water table elevation and equipotential head in the study area. Simulations with pump wells have been done to shows the availability of groundwater sources for paddy irrigation. The result shows that groundwater flows from area of higher elevation towards the lower elevated area. It is also shows that groundwater extraction could not be too excessive as it may dry up the aquifer storage.

scholarly journals Global Mean Sea Surface Height Estimated from Spaceborne Cyclone-GNSS Reflectometry

2020 ◽  
Vol 12 (3) ◽  
pp. 356 ◽  
Author(s):  
Hui Qiu ◽  
Shuanggen Jin
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
Absolute Error ◽  
Satellite System ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Mean Square ◽  
Mean Sea Surface

Mean sea surface height (MSSH) is an important parameter, which plays an important role in the analysis of the geoid gap and the prediction of ocean dynamics. Traditional measurement methods, such as the buoy and ship survey, have a small cover area, sparse data, and high cost. Recently, the Global Navigation Satellite System-Reflectometry (GNSS-R) and the spaceborne Cyclone Global Navigation Satellite System (CYGNSS) mission, which were launched on 15 December 2016, have provided a new opportunity to estimate MSSH with all-weather, global coverage, high spatial-temporal resolution, rich signal sources, and strong concealability. In this paper, the global MSSH was estimated by using the relationship between the waveform characteristics of the delay waveform (DM) obtained by the delay Doppler map (DDM) of CYGNSS data, which was validated by satellite altimetry. Compared with the altimetry CNES_CLS2015 product provided by AVISO, the mean absolute error was 1.33 m, the root mean square error was 2.26 m, and the correlation coefficient was 0.97. Compared with the sea surface height model DTU10, the mean absolute error was 1.20 m, the root mean square error was 2.15 m, and the correlation coefficient was 0.97. Furthermore, the sea surface height obtained from CYGNSS was consistent with Jason-2′s results by the average absolute error of 2.63 m, a root mean square error ( RMSE ) of 3.56 m and, a correlation coefficient ( R ) of 0.95.

scholarly journals Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature

2014 ◽  
Vol 7 (3) ◽  
pp. 1247-1250 ◽  
Author(s):  
T. Chai ◽  
R. R. Draxler
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Model Evaluation ◽  
Mean Absolute Error ◽  
Evaluation Studies ◽  
Model Performance ◽  
Absolute Error ◽  
Average Error ◽  
Mean Square

Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.

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