A Comparative Analysis of Hidden Markov Model, Hybrid Support Vector Machines, and Hybrid Artificial Neural Fuzzy Inference System in Reservoir Inflow Forecasting (Case Study: The King Fahd Dam, Saudi Arabia)

The precise prediction of the streamflow of reservoirs is of considerable importance for many activities relating to water resource management, such as reservoir operation and flood and drought control and protection. This study aimed to develop and evaluate the applicability of a hidden Markov model (HMM) and two hybrid models, i.e., the support vector machine-genetic algorithm (SVM-GA) and artificial neural fuzzy inference system-genetic algorithm (ANFIS-GA), for reservoir inflow forecasting at the King Fahd dam, Saudi Arabia. The results obtained by the HMM model were compared with those for the two hybrid models ANFIS-GA and SVM-GA, and with those for individual SVM and ANFIS models based on performance evaluation indicators and visual inspection. The results of the comparison revealed that the ANFIS-GA model and ANFIS model provided superior results for forecasting monthly inflow with satisfactory accuracy in both training (R2 = 0.924, 0.857) and testing (R2 = 0.842, 0.810) models. The performance evaluation results for the developed models showed that the GA-induced improvement in the ANFIS and SVR forecasts was matched by an approximately 25% decrease in RMSE and around a 13% increase in Nash–Sutcliffe efficiency. The promising accuracy of the proposed models demonstrates their potential for applications in monthly inflow forecasting in the present semiarid region.

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A novel hybrid intelligent system: Genetic algorithm and rough set incorporated neural fuzzy inference system

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) ◽

10.1109/cec.2008.4631140 ◽

2008 ◽

Cited By ~ 3

Author(s):

D. Wang ◽

G. S. Ng ◽

C. Quek

Keyword(s):

Genetic Algorithm ◽

Rough Set ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Intelligent System ◽

Inference System ◽

Hybrid Intelligent System ◽

Neural Fuzzy

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Integration of adaptive neural fuzzy inference system and fuzzy rough set theory with support vector regression to urban growth modelling

Earth Science Informatics ◽

10.1007/s12145-020-00522-0 ◽

2020 ◽

Author(s):

D. Parvinnezhad ◽

M. R. Delavar ◽

B. C. Pijanowski ◽

C. Claramunt

Keyword(s):

Set Theory ◽

Rough Set ◽

Urban Growth ◽

Rough Set Theory ◽

Fuzzy Inference ◽

Support Vector ◽

Growth Modelling ◽

Fuzzy Rough Set ◽

Inference System ◽

Neural Fuzzy

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Optimization of Wells turbine performance using a hybrid artificial neural fuzzy inference system (ANFIS) - Genetic algorithm (GA)

Ocean Engineering ◽

10.1016/j.oceaneng.2021.108861 ◽

2021 ◽

Vol 226 ◽

pp. 108861

Author(s):

Ali Mahrooghi ◽

Esmail Lakzian

Keyword(s):

Genetic Algorithm ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Wells Turbine ◽

Inference System ◽

Turbine Performance ◽

Artificial Neural ◽

Neural Fuzzy

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Genetic algorithm-based drilling burr minimization using adaptive neuro-fuzzy inference system and support vector regression

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405419889183 ◽

2019 ◽

Vol 234 (5) ◽

pp. 956-968

Author(s):

Nripen Mondal ◽

Madhab Chandra Mandal ◽

Bishal Dey ◽

Santanu Das

Keyword(s):

Genetic Algorithm ◽

Support Vector Regression ◽

Process Parameters ◽

Predictive Models ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Support Vector ◽

Inference System ◽

Burr Height ◽

Neuro Fuzzy

Burrs are undesirable materials beyond the work piece surface during drilling or other machining processes, thus this should be as less as possible during manufacturing. The experimental study has been conducted according to the full factorial design method. A total of 27 experiments were conducted by drilling on an Aluminum 6061T6 plate by choosing three factors and three levels of process parameters like drill diameter, point angle and spindle speed. In this research article, two predictive models, namely, adaptive neuro-fuzzy inference system and support vector regression, are developed using experimental data to estimate burr height and burr thickness. Then, these predictive models have been used to find out optimum process parameters for minimum burr height and burr thickness using genetic algorithm. It has been found that both the models are able to predict burr size and thickness with good accuracy, while the adaptive neuro-fuzzy inference system performs better than support vector regression.

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Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling

CATENA ◽

10.1016/j.catena.2017.05.034 ◽

2017 ◽

Vol 157 ◽

pp. 310-324 ◽

Cited By ~ 129

Author(s):

Wei Chen ◽

Mahdi Panahi ◽

Hamid Reza Pourghasemi

Keyword(s):

Data Mining ◽

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Performance Evaluation ◽

Fuzzy Inference ◽

Spatial Modelling ◽

Swarm Optimization ◽

Inference System ◽

Ensemble Data ◽

Neuro Fuzzy

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GAWDN-NFIS: Neural-Fuzzy Inference System with a Genetic Algorithm Based on Weighted Data Normalization and Its Application in Medicine

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007) ◽

10.1109/fskd.2007.341 ◽

2007 ◽

Author(s):

Qun Song ◽

Tianmin Ma

Keyword(s):

Genetic Algorithm ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Data Normalization ◽

Inference System ◽

Weighted Data ◽

Neural Fuzzy

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Sentiment Analysis

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Handbook of Research on Pattern Engineering System Development for Big Data Analytics ◽

10.4018/978-1-5225-3870-7.ch009 ◽

2018 ◽

pp. 130-152 ◽

Cited By ~ 1

Author(s):

Syed Muzamil Basha ◽

Dharmendra Singh Rajput

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Daily Activity ◽

Opinion Mining ◽

Fuzzy Inference ◽

Human Life ◽

Machine Learning Algorithms ◽

Support Vector ◽

Inference System ◽

Neural Fuzzy

E-commerce has become a daily activity in human life. In it, the opinion and past experience related to particular product of others is playing a prominent role in selecting the product from the online market. In this chapter, the authors consider Tweets as a point of source to express users' emotions on particular subjects. This is scored with different sentiment scoring techniques. Since the patterns used in social media are relatively short, exact matches are uncommon, and taking advantage of partial matches allows one to significantly improve the accuracy of analysis on sentiments. The authors also focus on applying artificial neural fuzzy inference system (ANFIS) to train the model for better opinion mining. The scored sentiments are then classified using machine learning algorithms like support vector machine (SVM), decision tree, and naive Bayes.

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A genetic algorithm approach for optimization of machinery noise calculations

Noise & Vibration Worldwide ◽

10.1177/0957456519839409 ◽

2019 ◽

Vol 50 (4) ◽

pp. 112-123 ◽

Cited By ~ 1

Author(s):

DS Rao ◽

DP Tripathy

Keyword(s):

Genetic Algorithm ◽

Fuzzy Inference System ◽

Statistical Models ◽

Fuzzy Inference ◽

Operating Conditions ◽

Computational Time ◽

Support Vector ◽

Inference System ◽

Neuro Fuzzy ◽

Artificial Neural

Noise produced by various noise sources in the mines is considered as a serious environmental problem. Exposure to such noise levels is considered as hazardous to workers working in such conditions. Noise assessment was exercised in a highly mechanized opencast bauxite mine, located in eastern India according to the Director General of Mines Safety Technical Circular No. 18 of 1975 and No.5 of 1990. There are numerous approaches in the literature on machinery noise prediction based on statistical models, soft computing techniques such as fuzzy inference system, artificial neural networks, support vector machines, adaptive neuro fuzzy inference system and other classification methods. The main drawback of statistical models, fuzzy inference system, artificial neural network, support vector machine and adaptive neuro fuzzy inference system is lack of interpretation for human and optimization issues. An attempt has been made to examine the applicability of a genetic algorithm, to take the advantage of genetic structures to find an optimal sound pressure level of the machinery noise taking into consideration the distance, directivity index, sound power level and other attenuation parameters under several noisy operating conditions according to ISO 9613-2:1996, ISO 6395:2008 and other related standards. Genetic algorithm used in this article has several advantages: it can be applied for low, high and dynamical system and uses a simple procedure to determine the order and the parameters with high accuracy. Genetic algorithm model is trained and tested in MATLAB to find the optimum parameters. Experimental results show that genetic algorithm is able to converge and find the optimum values faster along with acceptable computational time. By comparing the predicted values with the measured values, it proves the effectiveness of the proposed model as a useful and efficient method for machinery noise optimization problems.

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