Application of artificial neural network, fuzzy logic and decision tree algorithms for modelling of streamflow at Kasol in India

2013 ◽  
Vol 68 (12) ◽  
pp. 2521-2526 ◽  
Author(s):  
A. R. Senthil kumar ◽  
Manish Kumar Goyal ◽  
C. S. P. Ojha ◽  
R. D. Singh ◽  
P. K. Swamee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Correlation Function ◽  
Decision Tree ◽  
Soft Computing ◽  
Auto Correlation ◽  
Soft Computing Techniques ◽  
Tree Algorithms ◽  
Persistence Model

The prediction of streamflow is required in many activities associated with the planning and operation of the components of a water resources system. Soft computing techniques have proven to be an efficient alternative to traditional methods for modelling qualitative and quantitative water resource variables such as streamflow, etc. The focus of this paper is to present the development of models using multiple linear regression (MLR), artificial neural network (ANN), fuzzy logic and decision tree algorithms such as M5 and REPTree for predicting the streamflow at Kasol located at the upstream of Bhakra reservoir in Sutlej basin in northern India. The input vector to the various models using different algorithms was derived considering statistical properties such as auto-correlation function, partial auto-correlation and cross-correlation function of the time series. It was found that REPtree model performed well compared to other soft computing techniques such as MLR, ANN, fuzzy logic, and M5P investigated in this study and the results of the REPTree model indicate that the entire range of streamflow values were simulated fairly well. The performance of the naïve persistence model was compared with other models and the requirement of the development of the naïve persistence model was also analysed by persistence index.

Soft case-based reasoning

2005 ◽  
Vol 20 (3) ◽  
pp. 267-269 ◽  
Author(s):  
WILLIAM CHEETHAM ◽  
SIMON SHIU ◽  
ROSINA O. WEBER
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Network Theory ◽  
Probabilistic Reasoning ◽  
Case Based Reasoning ◽  
Case Representation ◽  
Soft Computing Techniques ◽  
Neural Network Theory ◽  
Case Based

The aim of this commentary is to discuss the contribution of soft computing—a consortium of fuzzy logic, neural network theory, evolutionary computing, and probabilistic reasoning—to the development of case-based reasoning (CBR) systems. We will describe how soft computing has been used in case representation, retrieval, adaptation, reuse, and case-base maintenance, and then present a brief summary of six CBR applications that use soft computing techniques.

The Prediction of Needle Penetration Force in Woven Denim Fabrics Using Soft Computing Models

2014 ◽  
Vol 9 (4) ◽  
pp. 155892501400900 ◽  
Author(s):  
Ezzatollah Haghighat ◽  
Saeed Shaikhzadeh Najar ◽  
Seyed Mohammad Etrati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Logic Model ◽  
Needle Penetration ◽  
Fuzzy Logic Model ◽  
Penetration Force ◽  
Artificial Neural ◽  
Computing Models

The aim of this paper was to predict the needle penetration force in denim fabrics based on sewing parameters by using the fuzzy logic (FL) model. Moreover, the performance of fuzzy logic model is compared with that of the artificial neural network (ANN) model. The needle penetration force was measured on the Instron tensile tester. In order to plan the fuzzy logic model, the sewing needle size, number of fabric layers and fabric weight were taken into account as input parameters. The output parameter is needle penetration force. In addition, the same parameters and data are used in artificial neural network model. The results indicate that the needle penetration force can be predicted in terms of sewing parameters by using the fuzzy logic model. The difference between performance of fuzzy logic and neural network models is not meaningful ( RFL=0.971 and RANN=0.982). It is concluded that soft computing models such as fuzzy logic and artificial neural network can be utilized to forecast the needle penetration force in denim fabrics. Using the fuzzy logic model for predicting the needle penetration force in denim fabrics can help the garment manufacturer to acquire better knowledge about the sewing process. As a result, the sewing process may be improved, and also the quality of denim apparel increased.

scholarly journals Prediction of the Oscillatory Heat Transfer Coefficient in Thermoacoustic Refrigerators

2021 ◽  
Vol 13 (17) ◽  
pp. 9509
Author(s):  
Mosa Machesa ◽  
Lagouge Tartibu ◽  
Modestus Okwu
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Soft Computing ◽  
Particle Swarm Optimisation ◽  
Soft Computing Techniques ◽  
Artificial Neural ◽  
Thermoacoustic Refrigerators

Thermoacoustic refrigerators are emerging devices that make use of meaningful high-pressure sound waves to induce cooling. Despite the accelerated progress in the field of thermoacoustics, knowledge of the heat transfer process in the heat exchange of the devices is still developing. This work applies different soft computing techniques, namely, an artificial neural network trained by particle swarm optimisation (ANN-PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANNs) to predict the oscillatory heat transfer coefficient in the heat exchangers of a thermoacoustic device. This study provides the details of the parametric analysis of an artificial neural network model trained by particle swarm optimisation. The solution model considers the number of neurons, the swarm population, and the acceleration factors to develop and analyse the architecture of several models. The regression model (R2) and mean squared error (MSE) were used to evaluate the accuracy of the models. The result showed that the proposed soft computing techniques can potentially be used for the modelling and the analysis of the oscillatory heat transfer coefficient with a higher level of accuracy. The result reported in this study implies that the prediction of the OHTC can be considered for the enhancement of thermoacoustic refrigerators performances.

scholarly journals Applications of Soft Computing Techniques for Pulmonary Tuberculosis Diagnosis

2019 ◽  
Vol 8 (4) ◽  
pp. 119-127
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pulmonary Tuberculosis ◽  
Soft Computing ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Diagnostic Model ◽  
Soft Computing Techniques ◽  
Tuberculosis Diagnosis ◽  
Artificial Neural

Recently, several interesting research studies have been reported on soft computing approaches. Soft computing approaches are solving several kinds of problems and provide alternative solutions. Different Soft computing techniques or approaches have been applied in medical care data for effective diagnosis prediction. Those approaches implemented on diseases diagnosing of pulmonary tuberculosis and obtaining better results in comparison to traditional approaches. This approach is an aggregation of methodologies that were combined various model and provide solutions to those problems that are difficult to handle in real-world situations. Researchers keep developing of an accurate and reliable intelligent decision-making method for the construction of pulmonary tuberculosis diagnosis system. The existing diagnostic testing system procedures are not only tedious, they also take a long time to analyze. Therefore, the diagnosis of tuberculosis still requires further improvements to new rapid and accurate diagnostic model and techniques that enable higher sensitivity and specificity to be achieved, thus promoting disease control and Prevention. State of the art makes approaches to soft computing more powerful, more reliable and more efficient. The importance of this review paper is to distinguish the different soft computing approaches used to support pulmonary tuberculosis disease diagnosis, identification, prediction and intelligent classification. In the field, researchers and medical practitioners look forward to using approaches to soft computing. Some of these are an artificial neural network, genetic algorithm, and support vector machine, fuzzy logic etc. latest methods in the diagnostic field uses artificial neural network. Some of the other benefits of Artificial neural network is an easy - to - optimize, resources and adoptable non - linear modeling of expansive data sets and predictive inference accuracy demonstrating that artificial neural network could serve as a valuable decision support tool in various fields, including medicine

scholarly journals A soft computing approach to tunnel face stability in a probabilistic framework

2021 ◽  
Author(s):  
Enrico Soranzo ◽  
Carlotta Guardiani ◽  
Wei Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soft Computing ◽  
Support Pressure ◽  
Tunnel Face ◽  
Soft Computing Techniques ◽  
The Face ◽  
Tunnel Face Stability ◽  
Artificial Neural ◽  
Face Support

AbstractTunnel face is important for shallow tunnels to avoid collapses. In this study, tunnel face stability is studied with soft computing techniques. A database is created based on the literature which is used to train some broadly adopted soft computing techniques, ranging from linear regression to the artificial neural network. The soil dry density, cohesion, friction angle, cover depth and the tunnel diameter are used as the input parameters. The soft computing techniques state whether the face support is stable and predict the face support pressure. It is found that the artificial neural network outperforms the other techniques. The face support pressure is predicted with the artificial neural network for statistically distributed samples, and the failure probability is obtained with Monte Carlo simulations. In this way, the stability of the tunnel face can be reliably assessed and the support pressure can be estimated fairly accurately.

scholarly journals Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3373
Author(s):  
Ludek Cicmanec
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Regression Function ◽  
Large Data ◽  
Soil Strength ◽  
Data Sets ◽  
Bearing Strength ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The main objective of this paper is to describe a building process of a model predicting the soil strength at unpaved airport surfaces (unpaved runways, safety areas in runway proximity, runway strips, and runway end safety areas). The reason for building this model is to partially substitute frequent and meticulous inspections of an airport movement area comprising the bearing strength evaluation and provide an efficient tool to organize surface maintenance. Since the process of building such a model is complex for a physical model, it is anticipated that it might be addressed by a statistical model instead. Therefore, fuzzy logic (FL) and artificial neural network (ANN) capabilities are investigated and compared with linear regression function (LRF). Large data sets comprising the bearing strength and meteorological characteristics are applied to train the likely model variations to be subsequently compared with the application of standard statistical quantitative parameters. All the models prove that the inclusion of antecedent soil strength as an additional model input has an immense impact on the increase in model accuracy. Although the M7 model out of the ANN group displays the best performance, the M3 model is considered for practical implications being less complicated and having fewer inputs. In general, both the ANN and FL models outperform the LRF models well in all the categories. The FL models perform almost equally as well as the ANN but with slightly decreased accuracy.

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