Forecasting peak surface gust wind in association with thunderstorm activity during pre-monsoon season at Delhi

In this study, an attempt is made to develop an objective method for forecasting the direction and speed of the gusty winds associated with thunderstorms at Delhi. For this purpose, surface and upper-air data for April, May and June (AMJ) for the years 1985-90 are utilized. Multiple regression equations are developed for forecasting the direction and speed of the gusty winds, using stepwise screening method, for which a total of 181 potential predictors are utilized. The developed dynamical-statistical models are tested with independent data sets of 1994 and 1995 for April, May and June. The dynamical-statistical models give satisfactory results with the developmental as well as the independent data sets. The root mean square error of the direction vary between 58° and 77° and the speed forecast vary between 9 and 12 knots. Possible reasons for large deviations of the forecast, noticed on a very few occasions, have also been examined.

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Individual Tree Volume Equations for Red Pine in Michigan

Northern Journal of Applied Forestry ◽

10.1093/njaf/14.2.53 ◽

1997 ◽

Vol 14 (2) ◽

pp. 53-58 ◽

Cited By ~ 5

Author(s):

Gary W. Fowler

Keyword(s):

Red Pine ◽

Data Sets ◽

Regression Equations ◽

Independent Data ◽

Data Set ◽

Individual Tree ◽

Standing Trees ◽

Pine Stands ◽

Volume Estimates ◽

Volume Equations

Abstract New total, pulpwood, sawtimber, and residual pulpwood cubic foot individual tree volume equations were developed for red pine in Michigan using nonlinear and multiple linear regression. Equations were also developed for Doyle, International 1/4 in., and Scribner bd ft volume, and a procedure for estimating pulpwood and residual pulpwood rough cord volumes from the appropriate cubic foot equations was described. Average ratios of residual pulpwood (i.e., topwood, cubic foot or cords) to mbf were developed for 7.6 and 9.6 in. sawtimber. Data used to develop these equations were collected during May-August 1983-1985 from 3,507 felled and/or standing trees from 27 stands in Michigan. Sixteen and 11 stands were located in the Upper and Lower Peninsulas, respectively. All equations were validated on an independent data set. Rough cord volume estimates based on the new pulpwood equation were compared with contemporary tables for 2 small cruise data sets. The new equations can be used to more accurately estimate total volume and volume per acre when cruising red pine stands. North. J. Appl. For. 14(2):53-58.

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CHEMOMETRIC ASSISTED QUANTITATIVE DETERMINATION OF VITEXIN IN ETHANOLIC EXTRACT OF BINAHONG (ANREDERA CORDIFOLIA (TEN.) STEENIS) LEAVES

International Journal of Applied Pharmaceutics ◽

10.22159/ijap.2020v12i6.39248 ◽

2020 ◽

pp. 69-75

Author(s):

OCTAVIANUS BUDI SANTOSA ◽

MICHAEL RAHARJA GANI ◽

SRI HARTATI YULIANI

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Cross Validation ◽

Multivariate Calibration ◽

Data Sets ◽

Calibration Data ◽

Mean Square ◽

Calibration Models ◽

Pcr Method

Objective: The objective of this study was to develop a UV spectroscopy method in combination with multivariate analysis for determining vitexin in binahong (Anredera cordifolia (Ten.) Steenis) leaves extract. Methods: The partial least square (PLS) regression and the principal component regression (PCR) was performed in this study to evaluate several statistical performances such as coefficient of determination (R2), root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and relative error of prediction (REP). Cross-validation in this study was performed using leave one out technique. Results: The R2 values of calibration data sets resulted from PLS and PCR method were 0.9675 and 0.9648, respectively. The low values of RMSEC and RMSECV both for PLS and PCR method indicated the minimum error of the calibration models. The R2 values of validation data sets resulted from PLS and PCR method were 0.9778 and 0.9820, respectively. The low values of RMSEP both for PLS and PCR method indicated the minimum error of prediction generated from the calibration data sets. Multivariate calibration techniques were applied to determine the content of vitexin in binahong leaves extract. Predicted values from the multivariate calibration models were compared to the actual values determined from a validated HPLC method. It was found that PLS models resulted in the lowest REP values compared to the PCR models. Conclusion: The chemometrics technique can be applied as an alternative method for determining vitexin levels in the ethanol solution of binahong leaves extract.

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Simulating the vernalization response of the "Snow Queen" lily (Lilium longiflorum Thunb.)

Scientia Agricola ◽

10.1590/s0103-90162005000200004 ◽

2005 ◽

Vol 62 (2) ◽

pp. 117-121 ◽

Cited By ~ 2

Author(s):

Nereu Augusto Streck ◽

Mariângela Schuh

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Beta Function ◽

Lilium Longiflorum ◽

Independent Data ◽

Mean Square ◽

Vernalization Response ◽

Data Set ◽

Plant Emergence ◽

Santa Maria

Vernalization is a process required by certain plant species, including lilies (Lilium spp.), to enter the reproductive phase, through an exposure to low, non-freezing temperatures. The objective of this study was to evaluate a nonlinear vernalization response function for the "Snow Queen" lily. An experiment was carried out in Santa Maria, RS, Brazil, to provide an independent data set to evaluate the performance of the model. Lily bulbs were vernalized at -0.5, 4.0, and 10ºC during two, four, six, and eight weeks. The daily vernalization rate (fvn) for each treatment was calculated with a beta function, and the effective vernalization days (VD) were calculated by accumulating fvn. The thermal time from plant emergence to visible buds at different VD treatments was used as the observed response to VD. Lily plants were not vernalized at values less than eight effective vernalization days and were fully vernalized at values greater than 40 days. The generalized nonlinear vernalization function described well the "Snow Queen" lily developmental response to VD, with a root mean square error of 0.178.

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Assessment of Pareto Calibration, Stability, and Wavelength Selection

Applied Spectroscopy ◽

10.1366/000370203321558227 ◽

2003 ◽

Vol 57 (3) ◽

pp. 309-316 ◽

Cited By ~ 10

Author(s):

Kelly J. Anderson ◽

John H. Kalivas

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Principal Component Regression ◽

Principal Component ◽

Data Sets ◽

Mean Square ◽

Wavelength Selection ◽

Pareto Model ◽

Improve Analysis

Recent work has shown that ridge regression (RR) is Pareto to partial least squares (PLS) and principal component regression (PCR) when the variance indicator Euclidian norm of the regression coefficients, ‖p̂‖, is plotted against the bias indicator root mean square error of calibration (RMSEC). Simplex optimization demonstrates that RR is Pareto for several other spectral data sets when ‖p̂‖ is used with RMSEC and the root mean square error of evaluation (RMSEE) as optimization criteria. From this investigation, it was observed that while RR is Pareto optimal, PLS and PCR harmonious models are near equivalent to harmonious RR models. Additionally, it was found that RR is Pareto robust, i.e., models formed at one temperature were then used to predict samples at another temperature. Wavelength selection is commonly performed to improve analysis results such that bias indicators RMSEC, RMSEE, root mean square error of validation, or root mean square error of cross-validation decrease using a subset of wavelengths. Just as critical to an analysis of selected wavelengths is an assessment of variance. Using wavelengths deemed optimal in a previous study, this paper reports on the variance/bias tradeoff. An approach that forms the Pareto model with a Pareto wavelength subset is suggested.

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Quantifying the Robustness of a Broth-Based Model for Predicting Listeria monocytogenes Growth in Meat and Poultry Products

Journal of Food Protection ◽

10.4315/0362-028x-68.11.2310 ◽

2005 ◽

Vol 68 (11) ◽

pp. 2310-2316 ◽

Cited By ~ 3

Author(s):

K. G. MARTINO ◽

B. P. MARKS ◽

D. T. CAMPOS ◽

M. L. TAMPLIN

Keyword(s):

Listeria Monocytogenes ◽

Root Mean Square Error ◽

Mean Square Error ◽

Standard Error ◽

Meat Products ◽

Independent Data ◽

Mean Square ◽

Data Set ◽

Poultry Products ◽

Robustness Index

Given the importance of Listeria monocytogenes as a risk factor in meat and poultry products, there is a need to evaluate the relative robustness of predictive growth models applied to meat products. The U.S. Department of Agriculture–Agricultural Research Service Pathogen Modeling Program is a tool widely used by the food industry to estimate pathogen growth, survival, and inactivation in food. However, the robustness of the Pathogen Modeling Program broth-based L. monocytogenes growth model in meat and poultry application has not, to our knowledge, been specifically evaluated. In the present study, this model was evaluated against independent data in terms of predicted microbial counts and covered a range of conditions inside and outside the original model domain. The robustness index was calculated as the ratio of the standard error of prediction (root mean square error of the model against an independent data set not used to create the model) to the standard error of calibration (root mean square error of the model against the data set used to create the model). Inside the calibration domain of the Pathogen Modeling Program, the best robustness index for application to meat products was 0.37; the worst was 3.96. Outside the domain, the best robustness index was 0.40, and the worst was 1.22. Product type influenced the robustness index values (P < 0.01). In general, the results indicated that broth-based predictive models should be validated against independent data in the domain of interest; otherwise, significant predictive errors can occur.

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A novel prediction algorithm for multivariate data sets

Decision Making Applications in Management and Engineering ◽

10.31181/dmame210402215s ◽

2021 ◽

Vol 4 (2) ◽

pp. 225-240

Author(s):

Pinki Sagar ◽

◽

Prinima Gupta ◽

Rohit Tanwar ◽

◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Multivariate Data ◽

Prediction Algorithm ◽

Series Data ◽

Data Sets ◽

Mean Square ◽

Data Set ◽

Independent Variables

Regression analysis is a statistical technique that is most commonly used for forecasting. Data sets are becoming very large due to continuous transactions in today's high-paced world. The data is difficult to manage and interpret. All the independent variables can’t be considered for the prediction because it costs high for maintenance of the data set. A novel algorithm for prediction has been implemented in this paper. Its emphasis is on extraction of efficient independent variables from various variables of the data set. The selection of variables is based on Mean Square Errors (MSE) as well as on the coefficient of determination r2p, after that the final prediction equation for the algorithm is framed on the basis of deviation of actual mean. This is a statistical based prediction algorithm which is used to evaluate the prediction based on four parameters: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and residuals. This algorithm has been implemented for a multivariate data set with low maintenance costs, preprocessing costs, lower root mean square error and residuals. For one dimensional, two-dimensional, frequent stream data, time series data and continuous data, the proposed prediction algorithm can also be used. The impact of this algorithm is to enhance the accuracy rate of forecasting and minimized average error rate.

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The visual arrays task…visual storage capacity or attention control?

10.31234/osf.io/u92cm ◽

2019 ◽

Cited By ~ 1

Author(s):

Jessie Martin ◽

Jason S. Tsukahara ◽

Christopher Draheim ◽

Zach Shipstead ◽

Cody Mashburn ◽

...

Keyword(s):

Working Memory ◽

Storage Capacity ◽

Working Memory Capacity ◽

Memory Capacity ◽

Attention Control ◽

Data Sets ◽

Independent Data ◽

Visual Storage ◽

Design Selection ◽

The Relationship

**The uploaded manuscript is still in preparation** In this study, we tested the relationship between visual arrays tasks and working memory capacity and attention control. Specifically, we tested whether task design (selection or non-selection demands) impacted the relationship between visual arrays measures and constructs of working memory capacity and attention control. Using analyses from 4 independent data sets we showed that the degree to which visual arrays measures rely on selection influences the degree to which they reflect domain-general attention control.

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PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

Journal of Animal Science ◽

10.1093/jas/skab054.357 ◽

2021 ◽

Vol 99 (Supplement_1) ◽

pp. 218-219

Author(s):

Andres Fernando T Russi ◽

Mike D Tokach ◽

Jason C Woodworth ◽

Joel M DeRouchey ◽

Robert D Goodband ◽

...

Keyword(s):

Body Weight ◽

Regression Analysis ◽

Sample Size ◽

Polynomial Regression ◽

Data Sets ◽

Regression Equations ◽

Prediction Equations ◽

Data Set ◽

Rate Of Increase ◽

The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

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Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

Remote Sensing ◽

10.3390/rs13091630 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1630

Author(s):

Yaohui Zhu ◽

Guijun Yang ◽

Hao Yang ◽

Fa Zhao ◽

Shaoyu Han ◽

...

Keyword(s):

Remote Sensing ◽

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Minimum Temperature ◽

Price Regulation ◽

Flowering Period ◽

Mean Square ◽

Prevention Measures ◽

Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

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Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽

10.3390/f12081020 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1020

Author(s):

Yanqi Dong ◽

Guangpeng Fan ◽

Zhiwu Zhou ◽

Jincheng Liu ◽

Yongguo Wang ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Low Cost ◽

Reference Value ◽

Tree Height ◽

Point Clouds ◽

Tree Structure ◽

Mean Square ◽

Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

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