scholarly journals Experimental study and modelling discharge coefficient of trapezoidal and rectangular piano key weirs

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Munish Kumar ◽  
Parveen Sihag ◽  
N. K. Tiwari ◽  
Subodh Ranjan
Keyword(s):  
Experimental Study ◽  
Random Forest ◽  
Discharge Coefficient ◽  
Predictive Accuracy ◽  
Cost Effective ◽  
Current Data ◽  
Tree Model ◽  
Data Set ◽  
Weir Height ◽  
Crest Length

AbstractCrest length is an important parameter in influencing the discharge handling capacity of a weir. Nonlinear weirs with longer crests are cost effective alternatives for those existing dam structures which are more susceptible to failure due to loss of storage capacity by reservoir silting problem, and insufficiency of the structure in evacuating the updated flow due to the limited space. Piano key weir is a type of nonlinear weir designed in the form of piano keys, over-hanged from both the upstream and the downstream with sloping floors founded on a base or footprint. These weirs can be easily placed over gravity dams due to smaller footprint than labyrinth weirs. The present study’s focus is on the comparative analysis of identical configurations of trapezoidal and rectangular piano key (PK) weirs. The importance of (crest length to width) L/W ratio and weir height (P) in affecting the discharge efficiency of both types of PK weirs is investigated in the experimental study. Furthermore, soft computing approaches are applied to the current data set obtained from both types of weirs by considering discharge coefficient ($$C_{\text{d}}$$Cd) as a function of dimensionless geometric variables of PK weirs. The modelling performance of random forest regression and M5 tree approach is tested in order to estimate the values of discharge coefficient. The results conclude higher predictive accuracy of random forest model over M5 tree model.

scholarly journals Determination Discharge Capacity of Triangular Labyrinth Side Weir Using Multi-Layer Neural Network (ANN-MLP)

2015 ◽  
Vol 10 (Special-Issue1) ◽  
pp. 111-119 ◽  
Author(s):  
Sohrab Karimi ◽  
Hossein Bonakdari ◽  
Azadeh Gholami
Keyword(s):  
Discharge Capacity ◽  
Discharge Coefficient ◽  
Vertex Angle ◽  
Hydraulic Parameters ◽  
Side Weir ◽  
Dimensionless Parameters ◽  
Weir Height ◽  
Crest Length ◽  
Side Weirs ◽  
Mlp Model

statistic indexes have been used to assess the accuracy of the results. The results of the examinations indicate that using MLP model along with simultaneous use of dimensionless parameters for the purposes of estimating discharge coefficient: the ratio of water behind the weir to the channel width (h/b), ratio of weir crest length to weir height (L/W), relative Froude number (F=V/√(2Side weirs are used in open channels to control flood and the flow passing through it. Discharge capacity is one of the crucial hydraulic parameters of side weirs. The aim of this study is to determine the effect of the intended dimensionless parameters on predicting the discharge coefficient of triangular labyrinth side weir. MAPE, RMSE, and Rgy)) and vertex angle (ϴ), offered the best results (MAPE= 0.67, R2= 0.99, RMSE = 0.009) in comparison with other models.

scholarly journals Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†

2021 ◽  
Vol 8 (3) ◽  
pp. 209-221
Author(s):  
Li-Li Wei ◽  
Yue-Shuai Pan ◽  
Yan Zhang ◽  
Kai Chen ◽  
Hao-Yu Wang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Random Forest Algorithm ◽  
Random Forest Regression ◽  
Data Set

Abstract Objective To study the application of a machine learning algorithm for predicting gestational diabetes mellitus (GDM) in early pregnancy. Methods This study identified indicators related to GDM through a literature review and expert discussion. Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis, and the collected indicators were retrospectively analyzed. Based on Python, the indicators were classified and modeled using a random forest regression algorithm, and the performance of the prediction model was analyzed. Results We obtained 4806 analyzable data from 1625 pregnant women. Among these, 3265 samples with all 67 indicators were used to establish data set F1; 4806 samples with 38 identical indicators were used to establish data set F2. Each of F1 and F2 was used for training the random forest algorithm. The overall predictive accuracy of the F1 model was 93.10%, area under the receiver operating characteristic curve (AUC) was 0.66, and the predictive accuracy of GDM-positive cases was 37.10%. The corresponding values for the F2 model were 88.70%, 0.87, and 79.44%. The results thus showed that the F2 prediction model performed better than the F1 model. To explore the impact of sacrificial indicators on GDM prediction, the F3 data set was established using 3265 samples (F1) with 38 indicators (F2). After training, the overall predictive accuracy of the F3 model was 91.60%, AUC was 0.58, and the predictive accuracy of positive cases was 15.85%. Conclusions In this study, a model for predicting GDM with several input variables (e.g., physical examination, past history, personal history, family history, and laboratory indicators) was established using a random forest regression algorithm. The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy. In addition, there are certain requirements for the proportions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.

scholarly journals Experimental study to estimate the discharge coefficient over inclined side spillway

2020 ◽  
Vol 7 (2) ◽  
pp. 44-54
Author(s):  
Ayat Mehdi kadhim ◽  
Faisal A. Majid
Keyword(s):  
Experimental Study ◽  
Froude Number ◽  
Water Surface ◽  
Discharge Coefficient ◽  
Flow Direction ◽  
Surface Profile ◽  
Side Weir ◽  
Discharge Water ◽  
Water Surface Profile ◽  
Crest Length

Abstract: Weir is usually used in different hydraulic purposes, mainly for head discharge-water relationship in channels. In this research, the flow has been carried out over the side of spillway using three cases of crest inclination by means of increasing one side of the weir a half centimeter each time with constant crest length equal to 15 cm. This means that the angle θ equals to (1.91˚, 3.82˚ and 5.71˚) respectively towards of the flow and is opposite to the flow with decreasing a half centimeter. Also in case of the breadth is horizontal (θ=0), seven cases have been tested. It is known that the greater amount of discharge occurs when the breadth is horizontal (θ=0). In case of the inclination of the weir is inclined opposite to the flow direction, the discharge is greater than that of which the weir inclined towards the flow direction for all cases of inclination. The greater discharge was obtained when decreasing the angle, which is opposite to the flow direction. The amount of discharge over the side of weir decreases by increasing the angle of the slope opposite to the direction of the flow and become more decreasing in case the inclination of side weir towards the flow. In case of increasing the angle of inclination in flow direction, the amount of discharge over side weir will be decreased. The effect of Froude number has also studied with the discharge coefficient and found that, they are proportionally related to each other. Also the water surface profile along the side spillway weir is studied and taken under consideration theoretically and experimentally in this research.

scholarly journals Development of ANN model for discharge prediction and optimal design of sharp-crested triangular plan form weir for maximum discharge using linked ANN–optimization model

2021 ◽  
Author(s):  
Md. Ayaz ◽  
Talib Mansoor
Keyword(s):  
Optimization Model ◽  
Prediction Error ◽  
Discharge Coefficient ◽  
Empirical Equation ◽  
Design Parameters ◽  
Ann Model ◽  
Maximum Discharge ◽  
Optimal Values ◽  
Weir Height ◽  
Crest Length

Abstract Triangular plan form weirs are advantageous over a normal weir in two ways. Within the limited channel width, use of such a weir increases the crest length and hence for a given head, increases the discharge and for a given discharge, reduces the head in comparison with a normal weir. In a previous study, researchers proposed an empirical equation to compute the discharge coefficient of a triangular plan form weir. The prediction error on the discharge coefficient was ±7% from the line of agreement. In the present study, an ANN model has been utilized to train randomly selected 70% data, with 15% tested and validation made for the remaining 15% data. The model predicts the discharge coefficient with a prediction error in the range of ±2.5% from the line of agreement, thereby decreasing the prediction error in Cd by 64%. Also, the sensitivity analysis of the developed ANN model has been performed for all the parameters (weir height, skew weir length and flow depth) involved in the study and the weir height was found to be the most sensitive parameter. Furthermore, the linked ANN–optimization model has been developed to find the optimal values of design parameters of a triangular plan form weir for maximum discharge.

scholarly journals Credit Card Fraud Detection and Prevention using Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1199-1201
Keyword(s):  
Random Forest ◽  
Credit Card ◽  
Graphical Model ◽  
Fraud Detection ◽  
Current Data ◽  
Data Sets ◽  
Random Forest Algorithm ◽  
Data Set ◽  
Credit Card Fraud ◽  
Credit Card Data

This research focused mainly on detecting credit card fraud in real world. We must collect the credit card data sets initially for qualified data set. Then provide queries on the user's credit card to test the data set. After random forest algorithm classification method using the already evaluated data set and providing current data set[1]. Finally, the accuracy of the results data is optimised. Then the processing of a number of attributes will be implemented, so that affecting fraud detection can be found in viewing the representation of the graphical model. The techniques efficiency is measured based on accuracy, flexibility, and specificity, precision. The results obtained with the use of the Random Forest Algorithm have proved much more effective

scholarly journals Investigation of trapezoidal sharp-crested side weir discharge coefficients under subcritical flow regimes using CFD

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Amir Ghaderi ◽  
Mehdi Dasineh ◽  
Saeed Abbasi ◽  
John Abraham
Keyword(s):  
Discharge Coefficient ◽  
Effective Parameters ◽  
Side Weir ◽  
Discharge Coefficients ◽  
Weir Height ◽  
Crest Length ◽  
Side Weirs ◽  
Upstream Flow ◽  
Water Elevation ◽  
Correlation Factors

AbstractSide weirs are utilized to regulate water surface and to control discharge and water elevation in rivers and channels. Here, the discharge coefficient for trapezoidal sharp-crested side weirs (TSCSW) and their affecting parameters are numerically investigated. To simulate the hydraulic and geometric characteristics of TSCSWs, three weir crest lengths of 15 cm, 20 cm and 30 cm with lengths of 20 cm, 30 cm and 40 cm and with two different sidewall slopes are utilized. The results show that for constant P/B (P: weir height, B: main channel width), the depth of flow along the channel and weir decreases as the crest length increases. Also, with increasing P/y1 ratio (P: weir height, y1: upstream flow depth), the discharge coefficient decreases for small crest lengths and increases for large crest lengths. The results show that for constant T/L ratio (T: passing flow width, L: side weir crest length), increasing the length, height and sidewall slope of a side weir will increase the discharge coefficient. It is observed that as the upstream Froude number increases for side weirs with longer crest lengths, the intensity of deviating flow and kinetic energy over the TSCSW will increase. Finally, some relations with high correlation factors are proposed for obtaining discharge coefficients using the dimensionless parameters of P/y1, T/L and Fr1. Based on proposed relations and sensitivity analysis, it is shown that T/L and P/y1 are the most effective parameters for reducing the discharge coefficient reduction.

Random Forest Refinement of Pairwise Potentials for Protein-ligand Decoy Detection

2019 ◽  
Author(s):  
Jun Pei ◽  
Zheng Zheng ◽  
Hyunji Kim ◽  
Lin Song ◽  
Sarah Walworth ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Probability Function ◽  
Pair Potential ◽  
Scoring Function ◽  
Stable Structure ◽  
Scoring Functions ◽  
Atom Pair ◽  
Data Set ◽  
Atom Pairs

An accurate scoring function is expected to correctly select the most stable structure from a set of pose candidates. One can hypothesize that a scoring function’s ability to identify the most stable structure might be improved by emphasizing the most relevant atom pairwise interactions. However, it is hard to evaluate the relevant importance for each atom pair using traditional means. With the introduction of machine learning methods, it has become possible to determine the relative importance for each atom pair present in a scoring function. In this work, we use the Random Forest (RF) method to refine a pair potential developed by our laboratory (GARF6) by identifying relevant atom pairs that optimize the performance of the potential on our given task. Our goal is to construct a machine learning (ML) model that can accurately differentiate the native ligand binding pose from candidate poses using a potential refined by RF optimization. We successfully constructed RF models on an unbalanced data set with the ‘comparison’ concept and, the resultant RF models were tested on CASF-2013.5 In a comparison of the performance of our RF models against 29 scoring functions, we found our models outperformed the other scoring functions in predicting the native pose. In addition, we used two artificial designed potential models to address the importance of the GARF potential in the RF models: (1) a scrambled probability function set, which was obtained by mixing up atom pairs and probability functions in GARF, and (2) a uniform probability function set, which share the same peak positions with GARF but have fixed peak heights. The results of accuracy comparison from RF models based on the scrambled, uniform, and original GARF potential clearly showed that the peak positions in the GARF potential are important while the well depths are not. <br>

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Clinical utility of Shabda and Sparsha Pareeksha in evaluation of Prakruta and Vaikruta Garbhavastha

2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Priyanka T K ◽  
V.N. K. Usha ◽  
Sucheta Kumari M
Keyword(s):  
Clinical Study ◽  
Clinical Utility ◽  
Neonatal Outcome ◽  
Predictive Accuracy ◽  
Cost Effective ◽  
Well Being ◽  
Prospective Clinical Study ◽  
Clinical Tool ◽  
Accuracy Rate ◽  
Fetal Pathology

Garbha is a conglomeration of biological mass with different strata including consciousness, needs an innovative clinical tool to evaluate its well being, which proves safe, potent, cost-effective and noninvasive. The idea of taking up this study was to sensitively predict the Prakrutavastha or well being w.r.t Garbha-pushti and ongoing Fetal Pathology, Vaikrutavastha w.s.r Garbhavyapads for a sharp interference to get a possible best neonatal outcome. The objective of this study was to calculate the predictive accuracy of evaluation of Garbhaspandanam on external Shabda and Sparsha Pareeksha. A Prospective Clinical study of Garbhaspandanam (FHS and FM) with external Shabda and Sparsha stimulation on maternal abdomen, from 24th week onwards was conducted in a cohort of 30 Singleton Pregnant women at Dept. of Prasuti Tantra and Stri Roga, S.D.M.C.A. Hospital, Udupi. Among the 9 cases in abnormal category, 2 cases had gone for IUD and one case though placed in abnormal category had responded relatively well to Shabda and Sparsha Pareeksha which may be due to the proper antenatal care and intervention given along with the patient’s Vatakara Nidana Parivarjana. Predictive Accuracy Rate on Shabda and Sparsha Pareeksha showed, FHS 70%, FM 76.7%; FHS 73.3%, FM 66.7% respectively. Shabda and Sparshapareeksha can be utilized as the Garbha - chetana - dyodakalakshana and can be performed as a routine antenatal bedside procedure, which can fairly detect the Prakruta and Vaikrutavastha of Garbha w.r.t Pushti. However larger prospective studies are required.

Knee Muscle Force Estimating Model Using Machine Learning Approach

2020 ◽  
Author(s):  
Anurag Sohane ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Muscle Force ◽  
Vastus Lateralis ◽  
Input Parameter ◽  
Research Work ◽  
Cost Effective ◽  
Coefficient Of Determination ◽  
Muscle Forces ◽  
Knee Muscle

Abstract Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

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