scholarly journals Prediction of manning's coefficient of roughness for high-gradient streams using M5P

2021 ◽  
Author(s):  
Parveen Sihag ◽  
Balraj Singh ◽  
Md. Azlin Bin Md. Said ◽  
H. Md. Azamathulla
Keyword(s):  
Performance Evaluation ◽  
Model Development ◽  
Absolute Error ◽  
The Novel ◽  
Data Set ◽  
High Gradient ◽  
Taylor Diagram ◽  
Development And Validation ◽  
The Given

Abstract The coefficient of Manning's roughness (n) has been generally implemented in the determination of depth and discharge in open channels and canals. This study unravels the novel idea and potential of Random Forest (RF), M5P, and Random Tree (RT) approaches to evaluate and predict the coefficient of Manning's roughness for hydraulic designing. To achieve this purpose, 42 observations are collected for high-gradient streams in Colorado, USA. All the observations are from boulder-bed, cobble and high gradient (S > 0.002 m/m) streams for within bank flows. In order to ascertain the best model, the above-mentioned approaches are evaluated and compared using performance evaluation indices such as mean absolute error (MAE), coefficient of correlation (CC), and root mean square error (RMSE). Outcomes of performance evaluation indices revealed that the proposed pruned M5P approach outperformed other applied models for predicting the coefficient of Manning's roughness for hydraulic designing with CC = 0.7858, 0.7910, RMSE = 0.0195, 0.0195, and MAE = 0.0157, 0.0165 for model development and validation period, correspondingly. Furthermore, Taylor diagram and Box plot also suggest that M5P based approach works better than RF and RT based approaches for predicting the coefficient of Manning's roughness for high-gradient streams using the given data set.

scholarly journals Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 489
Author(s):  
Fadi Almohammed ◽  
Parveen Sihag ◽  
Saad Sh. Sammen ◽  
Krzysztof Adam Ostrowski ◽  
Karan Singh ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Performance Evaluation ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Curing Time ◽  
Data Set ◽  
Soft Computing Techniques ◽  
Bacterial Concrete

In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree (REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have been evaluated and compared with the multiple linear regression-based model (MLR) to be used for prediction of the compressive strength of bacterial concrete. For this purpose, 128 experimental observations have been collected. The total data set has been divided into two segments such as training (87 observations) and testing (41 observations). The process of data set separation was arbitrary. Cement, Aggregate, Sand, Water to Cement Ratio, Curing time, Percentage of Bacteria, and type of sand were the input variables, whereas the compressive strength of bacterial concrete has been considered as the final target. Seven performance evaluation indices such as Correlation Coefficient (CC), Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) have been used to evaluate the performance of the developed models. Outcomes of performance evaluation indices recommend that the Polynomial kernel function based SVR model works better than other developed models with CC values as 0.9919, 0.9901, R2 values as 0.9839, 0.9803, NSE values as 0.9832, 0.9800, and lower values of RMSE are 1.5680, 1.9384, MAE is 0.7854, 1.5155, Bias are 0.2353, 0.1350 and SI are 0.0347, 0.0414 for training and testing stages, respectively. The sensitivity investigation shows that the curing time (T) is the vital input variable affecting the prediction of the compressive strength of bacterial concrete, using this data set.

scholarly journals The Stunting Tool for Early Prevention: development and external validation of a novel tool to predict risk of stunting in children at 3 years of age

2019 ◽  
Vol 4 (6) ◽  
pp. e001801
Author(s):  
Sarah Hanieh ◽  
Sabine Braat ◽  
Julie A Simpson ◽  
Tran Thi Thu Ha ◽  
Thach D Tran ◽  
...  
Keyword(s):  
Characteristic Curve ◽  
External Validation ◽  
Model Development ◽  
Validation Data ◽  
Data Set ◽  
Growth Faltering ◽  
Development Data ◽  
Gestational Age At Birth ◽  
Development And Validation ◽  
The Impact

IntroductionGlobally, an estimated 151 million children under 5 years of age still suffer from the adverse effects of stunting. We sought to develop and externally validate an early life predictive model that could be applied in infancy to accurately predict risk of stunting in preschool children.MethodsWe conducted two separate prospective cohort studies in Vietnam that intensively monitored children from early pregnancy until 3 years of age. They included 1168 and 475 live-born infants for model development and validation, respectively. Logistic regression on child stunting at 3 years of age was performed for model development, and the predicted probabilities for stunting were used to evaluate the performance of this model in the validation data set.ResultsStunting prevalence was 16.9% (172 of 1015) in the development data set and 16.4% (70 of 426) in the validation data set. Key predictors included in the final model were paternal and maternal height, maternal weekly weight gain during pregnancy, infant sex, gestational age at birth, and infant weight and length at 6 months of age. The area under the receiver operating characteristic curve in the validation data set was 0.85 (95% Confidence Interval, 0.80–0.90).ConclusionThis tool applied to infants at 6 months of age provided valid prediction of risk of stunting at 3 years of age using a readily available set of parental and infant measures. Further research is required to examine the impact of preventive measures introduced at 6 months of age on those identified as being at risk of growth faltering at 3 years of age.

scholarly journals DEVELOPMENT AND VALIDATION OF UV SPECTROPHOTOMETRICMETHOD FOR QUANTITATIVE ESTIMATION OF GLIPIZIDEIN PHARMACEUTICAL DOSAGE FORM

2020 ◽  
pp. 104-107
Author(s):  
ANUJA SURYAWANSHI ◽  
AFAQUEANSARI ◽  
MALLINATH KALSHETTI
Keyword(s):  
Dosage Form ◽  
Limit Of Detection ◽  
Quantitative Estimation ◽  
The Novel ◽  
Limit Of Quantification ◽  
Pharmaceutical Dosage Form ◽  
Novel Method ◽  
Bulk Drug ◽  
Development And Validation

Objective: The present work is aimed to develop a simple, rapid, selective and economical UV spectrophotometric method for quantitative determination of Glipizideinbulk and pharmaceutical dosage form. Methods: In this method Dimethyl Form amide (DMF) was used as solvent, the absorption maxima was found to be275 nm in DMF. The developed method was validated for linearity, accuracy, precision, ruggedness, robustness, LOD and LOQ in accordance with the requirements of ICH guideline. Results: The linearity was found to be 10-60 µg/ml having linear equation y=0.017x-0.006 with correlation coefficient of 0.997. The% recovery was found to be in the range of 98.7-100%. The % RSD for intra-day and inter-day precision was found to be 0.569923 and 0.40169 respectively. The limit of detection (LOD) and limit of quantification (LOQ) was found to be3.06 µg/ml and 9.27 µg/ml respectively. Conclusion: The developed method was validated as per ICH Q2(R1) guidelines. The novel method is applicable for the analysis of bulk drug in its pharmaceutical dosage form.

scholarly journals A New Hybrid MCDM Model for Personnel Selection Based on a Novel Grey PIPRECIA and Grey OCRA Methods

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1698
Author(s):  
Alptekin Ulutaş ◽  
Gabrijela Popovic ◽  
Dragisa Stanujkic ◽  
Darjan Karabasevic ◽  
Edmundas Kazimieras Zavadskas ◽  
...  
Keyword(s):  
Personnel Selection ◽  
Multiple Criteria Decision Making ◽  
Final Decision ◽  
The Novel ◽  
Decision Environment ◽  
Criteria Importance ◽  
Available Information ◽  
Importance Assessment ◽  
The Given

People represent one of the most significant resources of an organization, and therefore, personnel selection is one of the problems that organizations have increasingly been facing. The criteria that influence the final decision are usually opposing, so the application of multiple-criteria decision-making methods (MCDM) represents a suitable way for the facilitation of the given process. Additionally, the decision environment is characterized by the vagueness and uncertainty and, because of that, it is very hard to express the criteria over the exact crisp numbers. To acknowledge the unpredictability and obscurity of the available information important for the selection of the optimal candidate, a hybrid grey MCDM model for personnel selection is proposed in this paper. As an extension of the PIPRECIA method, the novel Grey Pivot Pairwise Relative Criteria Importance Assessment—the PIPRECIA-G method—is proposed and used for the determination of criteria importance. The PIPRECIA-G method preserved the good features of the PIPRECIA, but its superiority is reflected in its ability to deal with input data that are vague and grey. For the final ranking of the considered alternative candidates, the OCRA-G method is used. Basing the decision process and candidate selection on the two grey extended MCDM methods contributes to the increase of the reliability and confidence in the performed selection.

The Role and Place of Budget Procedure in the System

2003 ◽  
pp. 42-49 ◽  
Author(s):  
E. Bushmin
Keyword(s):  
Budget Process ◽  
Public Expenditures ◽  
Principal Role ◽  
Process Trends ◽  
The Given ◽  
Rules Of The Game

The article is devoted to the analysis of improving budget process trends. The author offers the concept of "financial technologism". Its usage should promote an essential improvement of the budget process. The given concept is based on the fact that the regulation of budget procedure is the process of determination of "rules of the game", and the order of interaction of different institutions within the framework of the budget process, and the trends and volumes of expenses are the strategy of institutions. The procedure within the budget process plays a principal role as compared with the trends and volumes of public expenditures.

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