Estimation of discharge correction factor of modified Parshall flume using ANFIS and ANN

2020 ◽  
Vol 1 (105) ◽  
pp. 17-30
Author(s):  
D. Saran ◽  
N.K. Tiwari
Keyword(s):  
Sensitivity Analysis ◽  
Soft Computing ◽  
Correction Factor ◽  
Prediction Models ◽  
Training Dataset ◽  
Inference System ◽  
Testing Dataset ◽  
Soft Computing Techniques ◽  
Regression Techniques ◽  
Prediction Capability

Purpose: To evaluate and compare the capability of ANFIS (Adaptive Neuro-Fuzzy-Inference System), ANN (Artificial Neural Network), and MNLR (Multiple Non-linear Regression) techniques in the estimation and formulation of Discharge Correction Factor (Cd) of modified Parshall flumes as based on linear relations and errors between input and output data. Design/methodology/approach: Acknowledging the necessity of further research in this field, experiments were conducted in the Hydraulics Laboratory of Civil Engineering Department, National Institute of Technology, Kurukshetra, India. The Parshall flume characteristics, associated longitudinal slopes and the discharge passing through the flume were varied. Consequent water depths at specific points in Parshall flumes were noted and the values of Cd were computed. In this manner, a data set of 128 observations was acquired. This was bifurcated arbitrarily into a training dataset consisting of 88 observations and a testing dataset consisting of 40 observations. Models developed using the training dataset were checked on the testing dataset for comparison of the performance of each predictive model. Further, an empirical relationship was formulated establishing Cd as a function of flume characteristics, longitudinal slope, and water depth at specific points using the MNLR technique. Moreover, Cd was estimated using soft computing tools; ANFIS and ANN. Finally, a sensitivity analysis was done to find out the flume variable having the greatest influence on the estimation of Cd. Findings: The predictive accuracy of the ANN-based model was found to be better than the model developed using ANFIS, followed by the model developed using the MNLR technique. Further, sensitivity analysis results indicated that primary depth reading (Ha) as input parameter has the greatest influence on the prediction capability of the developed model. Research limitations/implications: Since the soft computing models are data based learning, hence the prediction capability of these models may dwindle if data is selected beyond the current data range, which is based on the experiments conducted under specific conditions. Further, since the present study has faced time and facility constraints, hence there is still a huge scope of research in this field. Different lateral slopes, combined laterallongitudinal slopes, and more modified Parshall flume models of larger sizes can be added to increase the versatility of the current research. Practical implications: Cd of modified Parshall flumes can be predicted using the ANNbased prediction model more accurately as compared to other considered techniques. Originality/value: The comparative analysis of prediction models, as well as the formulation of relation, has been conducted in this study. In all the previous works, little to no soft computing techniques have been applied for the analysis of Parshall flumes. Even the regression techniques have been applied only on Parshall flumes of standard sizes. However, this paper includes not only Parshall flume of standard size but also a modified Parshall flume in its pursuit of predicting Cd with the help of ANN and ANFIS based prediction models along with MNLR technique

Modeling of Wind Speed Profile using Soft Computing Techniques

2015 ◽  
pp. 252-273
Author(s):  
Pijush Samui ◽  
Yıldırım Dalkiliç
Keyword(s):  
Wind Speed ◽  
Soft Computing ◽  
Kernel Functions ◽  
Training Dataset ◽  
Support Vector ◽  
Wind Speed Profile ◽  
Testing Dataset ◽  
Soft Computing Techniques ◽  
Adaptive Regression ◽  
Regression Techniques

This chapter examines the capability of three soft computing techniques (Genetic Programming [GP], Support Vector Machine [SVM], and Multivariate Adaptive Regression Spline [MARS]) for prediction of wind speed in Nigeria. Latitude, longitude, altitude, and the month of the year have been used as inputs of GP, RVM, and MARS models. The output of GP, SVM, and MARS is wind speed. GP, SVM, and MARS have been used as regression techniques. To develop GP, MARS, and SVM, the datasets have been divided into the following two groups: 1) Training Dataset – this is required to develop GP, MPMR, and RVM models. This study uses 18 stations' data as a training dataset. 2) Testing Dataset – this is required to verify the developed GP, MPMR, and RVM models. The remaining 10 stations data have been used as testing dataset. Radial basis function has been used as kernel functions for SVM. A detailed comparative study between the developed GP, SVM, and MARS models is performed in this chapter.

scholarly journals NEURO-FUZZY APPROACH TO PREDICTIVE MODELING OF EMISSIONS FROM BIODIESEL POWERED TRANSIT BUSES

Transport ◽  
2012 ◽  
Vol 26 (4) ◽  
pp. 334-352 ◽  
Author(s):  
Kasthurirangan Gopalakrishnan ◽  
Abhisek Mudgal ◽  
Shauna Hallmark
Keyword(s):  
Soft Computing ◽  
Real World ◽  
Fuzzy Inference System ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Inference System ◽  
Measuring Device ◽  
Transit Buses ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques

The rise in freight passenger transportation is responsible for air pollution, green house gas emissions (especially CO2) and high fuel demand. New engine technology and fuels are discovered and tested throughout the world. Biodiesel, an alternative for diesel, has been seen as a solution. However, the amount of emissions generated by a biodiesel fueled vehicle has not been understood well since most research studies of this kind reported in the literature were conducted in the laboratory. In the present study, emissions (NOx, HC, CO, CO2 and PM) were measured from biodiesel fueled transit buses using an on-road emissions measuring device known as the Portable Emissions Measurement System (PEMS). On-road study is important in terms of understanding the amount of emissions generated under the real traffic and environmental conditions. Emissions were measured on buses fueled with regular diesel (B0), B10 blend (10% biodiesel + 90% diesel) and B20 blend (20% biodiesel + 80% diesel). This paper demonstrates the use of hybrid soft-computing techniques such as the neuro-fuzzy technique for developing emissions prediction models from real-world data. Hybrid soft-computing techniques have been shown to work well in handling data prone to noise and uncertainty, which is characteristic of real-world scenario. Two neuro-fuzzy methodologies were considered in this study: the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS). A brief review of model development, recommended parametric settings, and statistical evaluation of prediction performance of both techniques are discussed. In general, the ANFIS showed better prediction accuracy for the individual emissions compared to DENFIS although the prediction accuracies are comparable.

scholarly journals Seismic Response Prediction of Buildings with Base Isolation Using Advanced Soft Computing Approaches

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Mosbeh R. Kaloop ◽  
Jong Wan Hu
Keyword(s):  
Support Vector Machine ◽  
Soft Computing ◽  
Prediction Models ◽  
Base Isolation ◽  
Least Square ◽  
Support Vector ◽  
Isolation System ◽  
Inference System ◽  
Comparison Results ◽  
Soft Computing Techniques

Modeling response of structures under seismic loads is an important factor in Civil Engineering as it crucially affects the design and management of structures, especially for the high-risk areas. In this study, novel applications of advanced soft computing techniques are utilized for predicting the behavior of centrically braced frame (CBF) buildings with lead-rubber bearing (LRB) isolation system under ground motion effects. These techniques include least square support vector machine (LSSVM), wavelet neural networks (WNN), and adaptive neurofuzzy inference system (ANFIS) along with wavelet denoising. The simulation of a 2D frame model and eight ground motions are considered in this study to evaluate the prediction models. The comparison results indicate that the least square support vector machine is superior to other techniques in estimating the behavior of smart structures.

Comparative study for deriving stagedischarge– sediment concentration relationships using soft computing techniques

2021 ◽  
Vol 2 (104) ◽  
pp. 57-76
Author(s):  
P. Sihag ◽  
M.R. Sadikhani ◽  
V. Vambol ◽  
S. Vambol ◽  
A.K. Prabhakar ◽  
...  
Keyword(s):  
Random Forest ◽  
Soft Computing ◽  
Sediment Load ◽  
Prediction Models ◽  
Absolute Error ◽  
Load Prediction ◽  
Statistical Parameters ◽  
Hydro Power ◽  
Input Combination ◽  
Soft Computing Techniques

Purpose: Knowledge of sediment load carried by any river is essential for designing and planning of hydro power and irrigation projects. So the aim of this study is to develop and evaluating the best soft-computing-based model with M5P and Random Forest regressionbased techniques for computation of sediment using datasets of daily discharge, daily gauge and sediment load at the Champua gauging site of the Upper Baitarani river basin of India. Design/methodology/approach: Last few decades, the soft computing techniques based models have been successfully used in water resources modelling and estimation. In this study, the potential of tree based models are examined by developing and comparing sediment load prediction models, based on M5P tree and Random forest regression (RF). Several M5P and RF based models have been applied to a gauging site of the Baitarani River at Odisha, India. To evaluate the performance of the selected M5P and RF-based models, three most popular statistical parameters are selected such as coefficient of correlation, root mean square error and mean absolute error. Findings: A comparison of the results suggested that RF-based model could be applied successfully for the prediction of sediment load concentration with a relatively higher magnitude of prediction accuracy. In RF-based models Qt, Q(t-1), Q(t-2), S(t-1), S(t-2), Ht and H(t-1) combination based M10 model work superior than other combination based models. Another major outcome of this investigation is Qt, Q(t-1) and S(t-1) based model M4 works better than other input combination based models using M5P technique. The optimum input combination is Qt, Q(t-1) and S(t-1) for the prediction of sediment load concentration of the Baitarani River at Odisha, India. Research limitations/implications: The developed models were tested for Baitarani River at Odisha, India.

Soft-Computing Control of Ball and Beam System

2018 ◽  
Vol 9 (4) ◽  
pp. 1-21 ◽  
Author(s):  
Ashwani Kharola ◽  
Pravin P. Patil
Keyword(s):  
Soft Computing ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Learning Ability ◽  
Gaussian Shape ◽  
Inference System ◽  
Neural Controllers ◽  
Beam System ◽  
Soft Computing Techniques ◽  
Ball And Beam System

This article derives a mathematical model and compares different soft-computing techniques for control of a highly dynamic ball and beam system. The techniques which were incorporated for control of proposed system were fuzzy logic, proportional-integral-derivative (PID), adaptive neuro fuzzy inference system (ANFIS) and neural networks. Initially, a fuzzy controller has been developed using seven gaussian shape membership functions. The article illustrates briefly both learning ability and parameter estimation properties of ANFIS and neural controllers. The results of PID controller were collected and used for training of ANFIS and Neural controllers. A Matlab simulink model of a ball and beam system has been derived for simulating and comparing different controllers. The performances of controllers were measured and compared in terms of settling time and steady state error. Simulation results proved the superiority of ANFIS over other control techniques.

scholarly journals DYNAMICAL CONTACT PARAMETER IDENTIFICATION OF SPINDLE-HOLDER-TOOL ASSEMBLIES USING SOFT COMPUTING TECHNIQUES

2020 ◽  
pp. 565
Author(s):  
Đorđe Čiča ◽  
Milan Zeljković ◽  
Saša Tešić
Keyword(s):  
Soft Computing ◽  
Fuzzy Inference ◽  
Contact Dynamics ◽  
Accurate Identification ◽  
Inference System ◽  
Contact Parameter ◽  
Soft Computing Techniques ◽  
Cutting Processes ◽  
Contact Parameters ◽  
Tool Assembly

In industry, the capability to predict the tool point frequency response function (FRF) is an essential matter in order to ensure the stability of cutting processes. Fast and accurate identification of contact parameters in spindle-holder-tool assemblies is very important issue in machining dynamics analysis. This work is an attempt to illustrate the utility of soft computing techniques in identification and prediction contact parameters of spindle-holder-tool assemblies. In this paper, three soft computing techniques, namely, genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) were used for identification of contact dynamics in spindle-holder-tool assemblies. In order to verify the proposed identification approaches, numerical and experimental analysis of the spindle-holder-tool assembly was carried out and the results are presented. Finally, a model based on the adaptive neural fuzzy inference system (ANFIS) was used to predict the dynamical contact parameters at the holder-tool interface of a spindle-holder-tool assembly. Accuracy and performance of the ANFIS model has been found to be satisfactory while validated with experimental results.

Cracked Rotor Diagnostics Using Soft Computing

2007 ◽  
Author(s):  
B. Samanta ◽  
C. Nataraj
Keyword(s):  
Soft Computing ◽  
Transient Response ◽  
Fuzzy Inference ◽  
Cracked Rotor ◽  
Inference System ◽  
Time Frequency ◽  
Wavelet Energy ◽  
Soft Computing Techniques ◽  
Detection And Diagnosis ◽  
Run Up

A study is presented for detection and diagnostics of cracked rotors using soft computing techniques like adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and genetic algorithms (GA). A simple model for a cracked rotor is used to simulate its transient response during startup for different levels of cracks. The transient response is processed through continuous wavelet transform (CWT) to extract time-frequency features for the normal and cracked conditions of the rotor. Several features including the wavelet energy distributions and the grey moment vectors (GMV) of the CWT scalograms are used as inputs for diagnosis of crack level. The parameters of the classifiers, ANFIS and ANN, along with the features from wavelet energy distribution and grey moment vectors are selected using GA maximizing the diagnostic success. The classifiers are trained with a subset of the data with known crack levels and tested using the other set of data (testing data), not used in training. The procedure is illustrated using the simulation data of a simple de Laval rotor with a ‘breathing’ crack for different crack levels during run-up through its critical speed. A comparison of diagnostic performance for the classifiers is presented. Results show the effectiveness of the proposed approach in detection and diagnosis of cracked rotors.

scholarly journals Prediction of Pullout Behavior of Belled Piles through Various Machine Learning Modelling Techniques

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3678 ◽  
Author(s):  
Dieu Tien Bui ◽  
Hossein Moayedi ◽  
Mu’azu Mohammed Abdullahi ◽  
Ahmad Safuan A Rashid ◽  
Hoang Nguyen
Keyword(s):  
Neural Network ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Least Square ◽  
General Regression Neural Network ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Inference System ◽  
Testing Dataset

The main goal of this study is to estimate the pullout forces by developing various modelling technique like feedforward neural network (FFNN), radial basis functions neural networks (RBNN), general regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS). A hybrid learning algorithm, including a back-propagation and least square estimation, is utilized to train ANFIS in MATLAB (software). Accordingly, 432 samples have been applied, through which 300 samples have been considered as training dataset with 132 ones for testing dataset. All results have been analyzed by ANFIS, in which the reliability has been confirmed through the comparing of the results. Consequently, regarding FFNN, RBNN, GRNN, and ANFIS, statistical indexes of coefficient of determination (R2), variance account for (VAF) and root mean square error (RMSE) in the values of (0.957, 0.968, 0.939, 0.902, 0.998), (95.677, 96.814, 93.884, 90.131, 97.442) and (2.176, 1.608, 3.001, 4.39, 0.058) have been achieved for training datasets and the values of (0.951, 0.913, 0.729, 0.685 and 0.995), (95.04, 91.13, 72.745, 66.228, 96.247) and (2.433, 4.032, 8.005, 10.188 and 1.252) are for testing datasets indicating a satisfied reliability of ANFIS in estimating of pullout behavior of belled piles.

scholarly journals Sistema de inferencia borroso basado en redes adaptativas para la evaluación de proyectos

2015 ◽  
Vol 19 (2) ◽  
pp. 53
Author(s):  
Anié Bermudez Peña ◽  
José Alejandro Lugo García ◽  
Pedro Yobanis Piñero Pérez
Keyword(s):  
Soft Computing ◽  
Key Management ◽  
Fuzzy Inference ◽  
Management Tool ◽  
Primary Data ◽  
Inference System ◽  
Computing Technique ◽  
Management Indicators ◽  
Soft Computing Techniques ◽  
Automated Tools

In this article, a set of key management indicators related to performance of execution, planning, costs, effectiveness, human resources, data quality, and logistics, are considered for the evaluation of a project. Several automated tools support project managers in this task. However, these tools are still insufficient to accurately assess projects in organizations with continuous improvement management styles and with presence of uncertainty in the primary data. An alternative solution is the introduction of soft computing techniques, allowing gains in robustness, efficiency, and adaptability in these tools. This paper presents an adaptivenetwork- based fuzzy inference system (ANFIS) to optimize projects evaluation made with the Xedro-GESPRO tool. The implementation of the system allowed the adjustment of fuzzy sets parameters in the inference rules for the assessment of projects, based on the automatic calculation of indicators. The contribution of this research lies in the application of ANFIS soft computing technique to optimize the evaluation of projects integrated with the management tool. The results contribute to the improvement of existing decision-making support tools into organizations towards project-oriented production. 

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