Subtractive Clustering Technique Research Articles

Effect of Friction Coefficient in Friction Stir Welding of B4C Reinforced AA5083 Metal Matrix Composites and Use of Fuzzy Clustering Technique for Weld Strength Prediction

The friction stir welding (FSW) method was used to weld B4C reinforced AA 5083 metal matrix composites in this study. By coating titanium nitride (TiN), aluminium chromium nitride (AlCrN), and diamond-like carbon (DLC) to a thickness of 4 microns, three FSW tools with square pin profiles were developed and the friction coefficients of 0.69, 0.32, and 0.2 were maintained. At three levels, the process factors such as tool rotating speed, transverse feed, and axial force were examined. For each tool, 15 samples were made using the central composite design. The influence of the friction coefficient on ultimate tensile strength, microstructural features, and tool condition was studied, and the flower pollination algorithm (FPA) technique was used to find the best process parameters for obtaining maximum ultimate tensile strength of FSW joints. The improved tensile strength of FSW joints was verified using a validation test. The coating has a considerable influence on the ultimate tensile strength, microstructure, and tool condition, according to the results of the tool’s friction coefficient. The results on the prediction of strength using the fuzzy clustering technique showed that the technique is effective in predicting the tensile strength values, with the root mean square error (RSME) of TiN, AlCrN, and DLC being 0.0027, 0.0016, and 0.0015, respectively, and the low RSME indicating that the prediction based on the fuzzy subtractive clustering technique is perfect and effective.

Fuzzy Models for Short Term Power Forecasting in Palestine

Short-Term Load Forecasting (STLF) is needed to efficiently manage the power systems. In this paper, two kinds of models that depend on the Fuzzy based techniques are developed to represent the STLF models in Palestine. Different types of these models have been developed using the available data sets that include the past electric load values and the climatic variables as inputs. It is shown that the climatic variables have a major effect on the predicted load. Various optimization techniques are used to develop the proposed models including hybrid and Backpropagation optimization techniques, Subtractive Clustering, and combining the Subtractive Clustering and Hybrid optimization techniques. The obtained results indicate the efficiency of the proposed models using the time and weather data.

Evolutionary optimization of biogas production from food, fruit, and vegetable (FFV) waste

The success of anaerobic digestion (AD) process for biogas production is contingent upon complex mix of operating factors, process conditions, and feedstock types, which could be affected by inadequate understanding of microbial, kinetic, and physicochemical processes. To address these limitations, efforts have been directed toward developing mathematical and intelligent models. Although mathematical models provide near-optimal solutions, they are time consuming, highly expensive, and demanding. Intelligent standalone models are also limited by their low predictive capability and inability to guarantee global optimal solution for the prediction of cumulative biogas yield for FFV waste. However, hyperparameter optimization of such models is essential to improve the prediction performance for cumulative biogas yield for FFV waste. Therefore, this study applies a genetic algorithm (GA) to optimize an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of cumulative biogas production. Seven (7) input variables, organic loading rate (OLR), volatile solids (VS), pH, hydraulic retention time (HRT), temperature, retention time, and reaction volume, were considered with cumulative biogas production as the output. The effect of varying clustering techniques was evaluated. The three (3) clustering techniques evaluated are fuzzy c-means and subtractive clustering and grid partitioning. The hybrid model was evaluated based on some verified statistical performance metrics. Optimal root mean squared error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE), and standard deviation error (error STD) of 0.0529, 0.0326,7.6742, and 0.0474, respectively, were reported at the model testing phase for the subtractive clustering technique being the best-performing model. The results confirm the capacity of hybrid evolutionary (genetic) algorithm based on subtractive clustering technique to predict the biogas yield from FFV and serve as an effective tool for the upscaling of anaerobic digestion units as well as in techno-economic studies toward more efficient energy utilization.Graphical abstract

Performance evaluation of adaptive neuro-fuzzy inference system for modelling dissolved oxygen of Kubanni Reservoir: A case study in Zaria, Nigeria

Background: Water quality evaluation require arduous laboratory and statistical analyses comprising of sample collection and sometimes transportation to laboratories, which may be expensive. In recent years, there has been an emergent need to monitor the dissolved oxygen (DO) concentrations of Kubanni reservoir as a result of anthropogenic and agricultural pollution. Hence, this study was conducted to apply adaptive neuro-fuzzy inference system (ANFIS)-based modelling in the prediction of DO of Kubanni reservoir. Methods: Water quality data for seven years were used to develop ANFIS models. Six water quality parameters, namely, total dissolved solids, free carbon dioxide, turbidity, temperature, manganese, and electrical conductivity, were selected for analysis based on their sensitivity. Subtractive clustering and grid partitioning techniques were considered when generating the fuzzy inference system (FIS). Three ANFIS models according to different lengths for training data and testing data were selected for modelling. Results: The results showed that Model-1 gave the best correlation (R-squared and adjusted R-squared of 0.852503 and 0.845000, respectively) for whole data using six input variables. While Model-3 gave the best correlation (R-squared and adjusted R-squared of 0.807791 and 0.799940, respectively) for whole data using three input variables. Conclusion: The performance efficiency of ANFIS model 1 using 6 inputs shows that the model is reliable for modelling water quality.

Fault location in series-compensated transmission lines using adaptive network-based fuzzy inference system

In this paper a new and accurate method for fault location in series-compensated transmission lines (SCTLs) is presented using adaptive network-based fuzzy inference system (ANFIS). In the proposed approach, the desired features are obtained using the Least-Squares Estimation (LSE) from two cycles of voltage and current data in only one line terminal. After that, extracted features, including the decaying DC offset component of the current, the fundamental components of current and voltage, and the phase difference between current and voltage for three-phase current and voltage signals, are normalized and applied as inputs to the fuzzy neural network for fault location. The subtractive clustering technique has been utilized to design the basic ANFIS. This study has scrutinized different cases, including two different ANFIS arrangements, implementing a single network to perform fault location on the entire line and a separate network for each line segment. Moreover, the efficiency of feature selection has been analyzed using the RReliefF algorithm. Various training patterns and tests have been provided in a test transmission system for different system conditions, such as different fault inception angles, fault locations, fault resistances, and structural conditions. The results validate that the proposed approach has accurately located the fault under a wide range of system variations.

Parameter Estimation-Based Slime Mold Algorithm of Photocatalytic Methane Reforming Process for Hydrogen Production

The key contribution of this paper is to determine the optimal operating parameters of the methane reforming process for hydrogen production. The proposed strategy contained two phases: ANFIS modelling and optimization. Four input controlling parameters were considered to increase the hydrogen: irradiation time (min), metal loading, methane concentration, and steam concentration. In the first phase, an ANFIS model was created with the help of the experimental data samples. The subtractive clustering (SC) technique was used to generate the fuzzy rules. In addition, the Gaussian-type and weighed average were used for the fuzzification and defuzzification methods, respectively. The reliability of the resulting model was assessed statistically by RMSE and the correlation (R2) measures. The small RMSE value and high R2 value of testing samples assured the correctness of the modelling phase, as they reached 0.0668 and 0.981, respectively. Based on the robust model, the optimization phase was applied. The slime mold algorithm (SMA), as a recent as well as simple optimizer, was applied to look for the best set of parameters that maximizes hydrogen production. The resulting values were compared by the findings of three competitive optimizers, namely particle swarm optimization (PSO), Harris hawks optimization (HHO), and evolutionary strategy HHO (EESHHO). By running the optimizers 30 times, the statistical results showed that the SMA obtained the maximum value with high mean, standard deviation, and median. Furthermore, the proposed strategy of combining the ANFIS modelling and the SMA optimizer produced an increase in the hydrogen production by 15.7% in comparison to both the experimental and traditional RSM techniques.

Application of hybrid ANFIS-based non-linear regression modeling to predict the %oil yield from grape peels: Effect of process parameters and FIS generation techniques

The present study evaluates the extraction process parameters of grape peel oil using non-linear hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The experiments were conducted at temperature (80–100 °C), and time (5–9 h) at 5 levels with output parameter as oil yield. The experimental result and factor interaction analysis shows that temperature had the most significant effect on the oil yield. The oil yield estimation performance indicators for the Fuzzy Inference (FIS) generation techniques are: Fuzzy C-means Clustering (FCM), [R 2 = 0.9102, MSE = 0.278, RMSE = 0.527], Subtractive Clustering (SC), [R 2 = 0.9090, MSE = 0.300, RMSE = 0.548] and Grid Partitioning (GP), [R 2 = 0.827, MSE = 0.668, RMSE = 0.817]. This indicates that Fuzzy C-means FIS generation fits better than Subtractive Clustering and Grid Partitioning techniques. For future research, a substantial volume of data is recommended for successful ANFIS training. • Oil yield extraction from grape peels using Soxhlet method. • Prediction of oil yield using hybrid ANFIS non-linear regression model. • Analysis of the effect of process parameters on oil yield. • FIS generating strategies utilized in ANFIS modeling are compared.

Investigations on the relationship among the porosity, permeability and pore throat size of transition zone samples in carbonate reservoirs using multiple regression analysis, artificial neural network and adaptive neuro-fuzzy interface system

Finding an accurate method for estimating permeability aside from well logs has been a difficult task for many years. The most commonly used methods targeted towards regression technique to understand the correlation between pore throat radii, porosity and permeability are Winland and Pittman equation approaches. While these methods are very common among petrophysicists, they do not give a good prediction in certain cases. Consequently, this paper investigates the relationship among porosity, permeability, and pore throat radii using three methods such as multiple regression analysis, artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) for application in transition zone permeability modeling. Firstly, a comprehensive mercury injection capillary pressure (MICP) test was conducted using 228 transition zone carbonate core samples from a field located in the Middle-East region. Multiple regression analysis was later performed to estimate the permeability using pore throat and porosity measurement. For the ANN, a two-layer feed-forward neural network with sigmoid hidden neurons and a linear output neuron was used. The technique involves training, validation, and testing of input/output data. However, for the ANFIS method, a hybrid optimization consisting of least-square and backpropagation gradient descent methods with a subtractive clustering technique was used. The ANFIS combines both the artificial neural network and fuzzy logic inference system (FIS) for the training, validation, and testing of input/output data. The results show that the best correlation for the multiple regression technique is achieved for pore throat radii with 35% mercury saturation (R35). However, for both the ANN and ANFIS techniques, pore throat radii with 55% mercury saturation (R55) gives the best result. Both the ANN and ANFIS are later found to be more effective and efficient and thus recommended as compared with the multiple regression technique commonly used in the industry.

Intelligent Handover Triggering Mechanism in 5G Ultra-Dense Networks Via Clustering-Based Reinforcement Learning

Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs.

Adaptive neuro-fuzzy approach for prediction of dewpoint pressure for gas condensate reservoirs

Dewpoint pressure is an important parameter for reservoir management and characterization. Gas condensate reservoirs experience significant reduction in productivity when initial reservoir pressure decreases below the dewpoint pressure. As such, an effective and efficient methods for prediction of this thermodynamic quantity are crucial for operational plans. In this article, a hybrid artificial intelligence model, based on adaptive neuro-fuzzy approach, for the prediction of gas condensate dewpoint pressure is presented. The proposed model combines the learning ability of artificial neural network and the capability of rule-based fuzzy inference system. First, fuzzy subtractive clustering technique is applied to a set of measured input/output data to identify an initial system based on extracted set of rules. The generated system is then trained using adaptive neuro-fuzzy inference system after which model validation and testing were performed. The performance of the proposed model is compared with existing methods. The results show that our proposed model outperforms the previous and existing methods with 99% accuracy and with the least root mean square error of 2.188 for some selected fluid sample.

Wind turbine power output very short-term forecast: A comparative study of data clustering techniques in a PSO-ANFIS model

The emergence of new sites for wind energy exploration in South Africa requires an accurate prediction of the potential power output of a typical utility-scale wind turbine in such areas. However, careful selection of data clustering technique is very essential as it has a significant impact on the accuracy of the prediction. Adaptive neurofuzzy inference system (ANFIS), both in its standalone and hybrid form has been applied in offline and online forecast in wind energy studies, however, the effect of clustering techniques has not been reported despite its significance. Therefore, this study investigates the effect of the choice of clustering algorithm on the performance of a standalone ANFIS and ANFIS optimized with particle swarm optimization (PSO) technique using a synthetic wind turbine power output data of a potential site in the Eastern Cape, South Africa. In this study a wind resource map for the Eastern Cape province was developed. Also, autoregressive ANFIS models and their hybrids with PSO were developed. Each model was evaluated based on three clustering techniques (grid partitioning (GP), subtractive clustering (SC), and fuzzy-c-means (FCM)). The gross wind power of the model wind turbine was estimated from the wind speed data collected from the potential site at 10 min data resolution using Windographer software. The standalone and hybrid models were trained and tested with 70% and 30% of the dataset respectively. The performance of each clustering technique was compared for both standalone and PSO-ANFIS models using known statistical metrics. From our findings, ANFIS standalone model clustered with SC performed best among the standalone models with a root mean square error (RMSE) of 0.132, mean absolute percentage error (MAPE) of 30.94, a mean absolute deviation (MAD) of 0.077, relative mean bias error (rMBE) of 0.190 and variance accounted for (VAF) of 94.307. Also, PSO-ANFIS model clustered with SC technique performed the best among the three hybrid models with RMSE of 0.127, MAPE of 28.11, MAD of 0.078, rMBE of 0.190 and VAF of 94.311. The ANFIS-SC model recorded the lowest computational time of 30.23secs among the standalone models. However, the PSO-ANFIS-SC model recorded a computational time of 47.21secs. Based on our findings, a hybrid ANFIS model gives better forecast accuracy compared to the standalone model, though with a trade-off in the computational time. Since, the choice of clustering technique was observed to play a vital role in the forecast accuracy of standalone and hybrid models, this study recommends SC technique for ANFIS modeling at both standalone and hybrid models.

A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks

As the global data traffic has significantly increased in the recent year, the ultra-dense deployment of cellular networks (UDN) is being proposed as one of the key technologies in the fifth-generation mobile communications system (5G) to provide a much higher density of radio resource. The densification of small base stations could introduce much higher inter-cell interference and lead user to meet the edge of coverage more frequently. As the current handover scheme was originally proposed for macro BS, it could cause serious handover issues in UDN i.e. ping-pong handover, handover failures and frequent handover. In order to address these handover challenges and provide a high quality of service (QoS) to the user in UDN. This paper proposed a novel handover scheme, which integrates both advantages of fuzzy logic and multiple attributes decision algorithms (MADM) to ensure handover process be triggered at the right time and connection be switched to the optimal neighbouring BS. To further enhance the performance of the proposed scheme, this paper also adopts the subtractive clustering technique by using historical data to define the optimal membership functions within the fuzzy system. Performance results show that the proposed handover scheme outperforms traditional approaches and can significantly minimise the number of handovers and the ping-pong handover while maintaining QoS at a relatively high level.

A Novel Automatic Modulation Classifier Using Graph-Based Constellation Analysis for <inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math> </inline-formula>-ary QAM

An innovative automatic modulation classification via graph-based constellation analysis for M-ary QAM signals is presented. In our framework, a unified mesh model for the constellation diagrams of the M-QAM signals within the modulation candidate set is first constructed and exploited to transform the received M-QAM signal into graph domain. The concise graph representation of the received M-QAM signal is established from its constellation according to the positions of the recovered symbols in the mesh model. Then, the modulation feature vector is built from the eigenvector(s) corresponding to the maximum eigenvalue of its adjacency matrix. The modulation type can be identified by measuring the angle between the feature vectors resulting from the training data and the test data. Monte Carlo simulation results and theoretical analysis demonstrate that the proposed method with lower computational complexity can provide superior performance to the existing subtractive clustering technique, and is robust to the residual phase and timing offsets.

Self-feedback image retrieval algorithm based on annular color moments

Content-based image retrieval (CBIR) extracts visual content features (such as color, texture, and shape) of a sample image to retrieve another similar image. Due to the existence of the semantic gap, retrieval results are often unsatisfactory. A CBIR method based on relevance feedback (RF) can reduce the semantic gap and achieve a high-retrieval accuracy by establishing a correlation between low-level image features and high-level semantics via human-computer interaction. However, the complicated human-computer interface of RF increases the burden on users; hence, some scholars have proposed the pseudo-relevance feedback (PRF) technology. To further contribute to the research, this paper proposes a self-feedback image retrieval algorithm based on annular color moments. In this approach, hashing sequences of color moments based on annular segmentation are extracted to be used as feature vectors for initial retrieval. Based on this result, improved subtractive clustering and correlation feedback techniques are used for extended queries. Thus, a self-feedback method without user participation is realized. The experimental results show that the accuracy of image retrieval can be improved, and the proposed algorithm is robust to image rotation, scaling, and translation.

Ant Colony Optimization of Interval Type-2 Fuzzy C-Means with Subtractive Clustering and Multi-Round Sampling for Large Data

Fuzzy C-Means (FCM) is widely accepted as a clustering technique. However, it cannot often manage different uncertainties associated with data. Interval Type-2 Fuzzy C-Means (IT2FCM) is an improvement over FCM since it can model and minimize the effect of uncertainty efficiently. However, IT2FCM for large data often gets trapped in local optima and fails to find optimal cluster centers. To overcome this challenge an Ant Colony-based Optimization (ACO) is proposed. Another challenge encountered is determining the number of clusters to perform clustering. Subtractive clustering (SC) is an efficient technique to estimate appropriate number of clusters. Though for large datasets the convergence rate of ACO and SC becomes high and thus, it becomes challenging to cluster data and evaluate correct number of clusters. To encounter the challenges of large dataset, Multi-Round Sampling (MRS) technique is proposed. IT2FCM-ACO with SC and MRS technique performs clustering on subsets of data and determines suitable cluster centers and cluster number. The obtained clusters are then extended to the entire dataset. This eliminates the need for IT2FCM to work on the complete dataset. Thus, the objective of this paper is to optimize IT2FCM using ACO algorithm and to estimate the optimal number of clusters using SC while employing MRS to handle the challenges of voluminous data. Results obtained from several clustering evaluation measures shows the improved performance of IT2FCM-ACO-MRS compared to ITFCM-ACO and IT2FCM. Speed up for different sample size of dataset is computed and is found that IT2FCM-ACO-MRS is ≈1–5 times faster than IT2FCM and IT2FCM-ACO for medium datasets whereas for large datasets it is reported to be ≈ 30–150 times faster.

Fan mill state estimation based on acoustic signature analysis

Two new, real time, noninvasive techniques for the detection of states of impact plates in fan mills at thermal power plants, using acoustic transducers as sensor elements, are proposed in this paper. Both methods rely on analysis of recorded acoustic signals in the time and frequency domains. One method uses a linear dimension reduction procedure and the state of the impellers is assessed by analyzing statistical distance as a metric. The second method uses a subtractive clustering technique to determine the cluster centers in multidimensional space and introduces the Euclidean distance ratio as a metric to estimate the amount of wear of the impellers. These data-driven methods are tested on real acoustic signals recorded at the thermal power plant TEKO Kostolac A1 in Serbia and shown to be effective in an extremely noisy environment. A comparison of the methods is made bearing in mind the efficiency and computational complexity of the algorithms.

Fuzzy inference model for assessing occupational risks in construction sites

Occupational risk assessment is a key measure to reach safety in construction industries. The assessment process is involved with many parameters which are difficult to assess, due to inadequate data or imprecise information. So, traditional quantitative approaches fail, frequently, to assess risk levels and to identify adequate preventive measures. A Takagi-Sugeno type fuzzy inference system is developed in this article to overcome these lacunas. In the model formulation process, the risk factors and controlling factors for accidental injuries are considered as input parameters. Safety levels of each type of injury prone body parts are evaluated by using analytical hierarchy process. Subtractive clustering technique is used to reduce the number of rules and thereby an initial fuzzy inference system is generated. Finally, the initial model is updated by tuning all the parameters corresponding to the input variables using a hybrid learning process. The developed methodology has been applied to few selected construction sites in India. The derived results validate the applicability of the developed model for assessing risks in construction sites and also identifies the pertinent progress of existing safety strategies.

Prediction of road track paths by a fuzzy model

ABSTRACTThis research proposes a fuzzy model built to predict the track paths of a motor vehicle on a two-lane road and two-way traffic. The purpose of this study stems from the awareness that the position of the vehicle within the cross-section hardly coincides with the axis of the lane but deviates from this one because of some conditions generally not provided in the road standards. To solve this problem, the authors prepared an instrumented vehicle, measuring many variables during some trials in a rural road located in Sicily, Italy. The resulting database was treated at first with subtractive clustering techniques to correctly size the membership functions and rules without the subjective help of an analyst. Therefore, the fuzzy model was built only on the basis of the measured data, and the result was the position of the vehicle within the cross-section of the examined road. This research can help the analyst to look for the real cause of user's misbehavior even when the variables involved are fully respectful with the road standard to modify or eliminate disturbing elements in the understanding of the road by the users.

A Softcomputing Knowledge Areas Model

Recently, ten knowledge areas (KAs) of project management have been published by the PMBOK® Guide. They comprise specific skills and experiences to ensure accomplishing project goals, and include management of: integration, scope, cost, time, quality, communications, procurement, risk, human resources and stakeholders. This research paper focuses on the ten required KAs for a project manager or a project to be successful. It aims at applying the Softcomputing modeling techniques to describe the relations between the 47 processes and the KAs. Such a model will enable users to predict the overall competencies of the project management. Thus, it provides an assessment tool to envisage, visualize and indicate the overall performance and competency of a project. The proposed Softcomputing Knowledge Areas Model (SKAM) is a two-stage model. The first stage involves ten models. Each model describes relations between a specific KA and its related processes. The outputs of these ten models will feed into the second stage that will represent the relationship between all the ten KAs and the overall predicted competencies of a project. A combination of Subtractive Clustering and Neurofuzzy modeling techniques are used. Three measures are used to validate the adequacy of the models: the mean average percentage errors, the correlation coefficient and the maximum percentage errors. The highest achieved values for these measures are0.5751, 0.9999 and 4.7283, respectively. However, although the preliminary findings of the proposed SKAM model are promising, more testing is still required before declaring the adequacy of applying the Softcomputing modeling approach in the project management field.

Adaptive Network-Based Fuzzy Inference Systems Coupled with Genetic Algorithms for Predicting Soil Permeability Coefficient

This paper extends hybrid-type optimization models of genetic algorithm adaptive network-based fuzzy inference system (GA-ANFIS) for predicting the soil permeability coefficient (SPC) of different types of soil. In these models, GA optimizes parameters of a subtractive clustering technique that controls the structure of the ANFIS model's fuzzy rule base. Simultaneously, a hybrid leaning algorithm is employed in the ANFIS, as a trained fuzzy inference system (FIS), which optimally determines the parameter sets of the examined FISs in ANFIS. Using an updated large database of SPCs consisting of 338 fine-grained, 178 mixed and 94 granular soil samples, GA-ANFIS framework constructs different models of predicting the permeability coefficient of respectively fine-grained, mixed and granular soils. A fuzzy C-mean technique has been used to cluster the entire data samples of each type of soil and divide them uniformly into training and testing data sets. Different prediction models of SPC have been trained and tested for each of the three soil types, and the appropriate models have been selected. The selected models have been compared with ANN and modified-by-GA empirical prediction models. Results show that the constructed GA-ANFIS models outperform the other models in terms of the prediction accuracy and the generalization capability.