Adaptive Neural-based Fuzzy Inference System Research Articles

Modeling and estimation of fouling factor on the hot wire probe by smart paradigms

The fouling factor is a complex non-linear function of several feature variables. Accurate estimation of this measurable factor is applicable for controlling the heat transfer efficiency of heat exchangers. Artificial neural network (ANN) and adaptive neural-based fuzzy inference system (ANFIS) techniques are accurate approaches for the understanding treatment of the most complicated systems. The cascade-feedforward (CFF), multi-layer perceptron (MLP), radial basis function (RBF), and generalized regression (GR) neural networks and ANFIS were applied for predicting the fouling factor of the hot wire probe from some measured variables using a huge databank, including 1870 empirical dataset. Pearson's and Spearman's techniques confirmed the highest relation between considered features and the first order of fouling factor. The results demonstrate that the cascade feed-forward neural network containing eight hidden neurons was the best artificial intelligence (AI) model with excellent overall AARE = 3.44 %, MSD = 0.0000315, RMSD = 0.0056, and R2 = 0.9982. This work's significance lies in presenting an applicable and accurate tool for estimating fouling factors on hot wire probes to the research community and industry. This model can be considered a reliable replacement for empirical analyses that are often expensive and time-consuming.

Adaptive Neural-Based Fuzzy Inference System and Cooperation Search Algorithm for Simulating and Predicting Discharge Time Series Under Hydropower Reservoir Operation

Adaptive Neural-Based Fuzzy Inference System and Cooperation Search Algorithm for Simulating and Predicting Discharge Time Series Under Hydropower Reservoir Operation

Hybrid wavelet–artificial intelligence models in meteorological drought estimation

In this study, wavelet transform (W), which is one of the data pre-processing techniques, adaptive neural-based fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural networks (ANNs) were used to develop the drought estimation models of Canakkale, Turkey. For these models, 3-, 6-, 9- and 12-months drought indices were calculated by standard precipitation index (SPI) and by using precipitation data of Canakkale, Gokceada and Bozcaada stations between 1975 and 2010 years. Firstly, ANFIS, SVM and ANNs models were developed to estimate calculated drought indices. Then SPI values of Gokceada and Bozcaada stations were divided into sub-series by wavelet transform technique and these sub-series were used as input in W-ANFIS, W-SVM and W-ANNs models. When the developed models were compared, it was determined that the hybrid models developed by using preprocessing technique performed better. Among these models, it was observed that the W-ANFIS model gave the best results for 6-months period.

Reliability analysis and dynamic maintenance model based on fuzzy degradation approach

The rapid growth of population density and people demand based on railway services increases day by day. Hence, the maintenance (i.e., degradation and aging) and the decision-making processes of rail wheels are troublesome. There has been an impact of degradation stage for a system such as a repair, replacement, and inspection time. In this paper, an adaptive neural-based fuzzy inference system (ANFIS) rail wheel maintenance model is proposed using the modified spider monkey algorithm, with the parametric update. As the design of the wheel maintenance strategy, the degradation stage increases with system aging and it can be minimized using repair. In this way, this article presents the development of system reliability that is dealing with the establishment of a maintenance strategy. A higher running cost rate reduces the main contribution of the reparation cycle using an optimum maintenance model, and the locomotive wheels are employed to demonstrate the efficiency of the proposed methodology. Under optimal inspection time, the impact of system failures such as aging and degradation is determined successfully. The proposed model can able to identify the precise time when rail tracks fail to minimize the maintenance cost/time. This ANFIS and MSMA can increase the efficiency of maintenance activities and decrease the cost of maintenance in the long term. Similarly, the results demonstrate that the proposed maintenance model is more flexible, and the capability of gauge value prediction using real and estimated values is accurate.

Evaluating the performances of several artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management

Accurate runoff forecasting plays an important role in guaranteeing the sustainable utilization and management of water resources. Artificial intelligence methods can provide new possibilities for runoff prediction when the underlying physical relationship cannot be explicitly obtained. However, few reports evaluate the performances of various artificial intelligence methods in forecasting daily streamflow time series for sustainable water resources management by far. To refill this research gap, the potentials of five artificial intelligence methods in daily streamflow series prediction are examined, including artificial neural network (ANN), adaptive neural-based fuzzy inference system (ANFIS), extreme learning machine (ELM), Gaussian process regression (GPR) and support vector machine (SVM). Four quantitative statistical indexes are chosen as the evaluation benchmarks. The results from two huge hydropower reservoirs in China show that five artificial intelligence methods can achieve satisfying forecasting results, while the SVM, GPR and ELM methods can produce better performances than ANN and ANFIS in both training and testing phases with respective to four indexes. Thus, it is of great importance to carefully choose the appropriate forecasting models based on the actual characteristics of the studied reservoir.

ANFIS based system identification of underactuated systems

Abstract In this work, the effectiveness of the adaptive neural based fuzzy inference system (ANFIS) in identifying underactuated systems is illustrated. Two case studies of underactuated systems are used to validate the system identification i. e., linear inverted pendulum (LIP) and rotary inverted pendulum (RIP). Both the systems are treated as benchmark systems in modeling and control theory for their inherit nonlinear, unstable, and underactuated behavior. The systems are modeled with ANFIS using the input-output data acquired from the dynamic response of the nonlinear analytical model of the systems. The dynamic response of the ANFIS model is simulated and compared to the nonlinear mathematical model of the inverted pendulum systems. In order to check the effectiveness of the ANFIS model, mean square error is used as the performance index. From the obtained simulation results, it has been perceived that the ANFIS model performed satisfactorily within the trained operating range while a minor deviation is seen outside the trained operating region for both the case studies. Furthermore, the experimental validation of the of the proposed ANFIS model is done by comparing it with the experimental model of the rotary inverted pendulum. The obtained results show that the response of ANFIS model is in close agreement to the experimental model of the rotary inverted pendulum.

Modelling water quantity parameters using Artificial Intelligence techniques, A case study Abu-Ziriq Marsh in south of Iraq.

The low water quantity has a significant impact on the ecosystem and the food chain of living organisms, thus causing a loss of biodiversity and a lack of natural food sources. Abu-Ziriq Marsh, located in the south of Iraq, is chosen as the case study for the application of the proposed methodology. The aim of this study was to assess the ability of using three different models of Artificial Intelligence (AI) techniques: Adaptive Neural-based Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Multiple Regression Model (MLR) to predict and estimate the discharge of Abu-Ziriq Marsh by depending on flow release from upstream Al-Badaa regulator. Daily discharge of Al-Badaa regulator(QB ) and Abu-Ziriq Marsh(Qz ) were used in this study. The water quantity data, consisting of 720 records of daily data between the years 2017 and 2018, were used for training and testing the models. The training and testing data were randomly partitioned into 515 (70.5 %) and 215 (29.5 %) datasets, respectively. The performance of all models was assessed through the correlation coefficient (R), root mean square error (RMSE) and Nash–Sutcliffe efficiency coefficient (NSE). Results of RMSE, R and NSE for the calibration (validation) of ANFIS model were 4.11 (4.17), 0.87 (0.83) and 0.76 (0.70), respectively. The evaluation of the results indicates that ANFIS model is superior to other models. The identified ANFIS models can be used as tools for the computation of water quantity parameter(Qz ) in Iraqi Marshes.

Modeling Water Quality Parameters Using Data-Driven Models, a Case Study Abu-Ziriq Marsh in South of Iraq

Total dissolved solids (TDS) and electrical conductivity (EC) are important parameters in determining water quality for drinking and agricultural water, since they are directly associated to the concentration of salt in water and, hence, high values of these parameters cause low water quality indices. In addition, they play a significant role in hydrous life, effective water resources management and health studies. Thus, it is of critical importance to identify the optimum modeling method that would be capable to capture the behavior of these parameters. The aim of this study was to assess the ability of using three different models of artificial intelligence techniques: Adaptive neural based fuzzy inference system (ANFIS), artificial neural networks (ANNs) and Multiple Regression Model (MLR) to predict and estimate TDS and EC in Abu-Ziriq marsh south of Iraq. As so, eighty four monthly TDS and EC values collected from 2009 to 2018 were used in the evaluation. The collected data was randomly split into 75% for training and 25% for testing. The most effective input parameters to model TDS and EC were determined based on cross-correlation test. The three performance criteria: correlation coefficient (CC), root mean square error (RMSE) and Nash–Sutcliffe efficiency coefficient (NSE) were used to evaluate the performance of the developed models. It was found that nitrate (NO3), calcium (Ca+2), magnesium (Mg+2), total hardness (T.H), sulfate (SO4) and chloride (Cl−1) are the most influential inputs on TDS. While calcium (Ca+2), magnesium (Mg+2), total hardness (T.H), sulfate (SO4) and chloride (Cl−1) are the most effective on EC. The comparison of the results showed that the three models can satisfactorily estimate the total dissolved solids and electrical conductivity, but ANFIS model outperformed the ANN and MLR models in the three performance criteria: RMSE, CC and NSE during the calibration and validation periods in modeling the two water quality parameters. ANFIS is recommended to be used as a predictive model for TDS and EC in the Iraqi marshes.

The Use of Large-Scale Climate Indices in Monthly Reservoir Inflow Forecasting and Its Application on Time Series and Artificial Intelligence Models

Climate variability is strongly influencing hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. Large-scale climate indices can provide potential information about climate variability, as they usually have a direct or indirect correlation with hydrologic variables. This study aims to use large-scale climate indices in monthly reservoir inflow forecasting for considering climate variability. For this purpose, time series and artificial intelligence models, such as Seasonal AutoRegressive Integrated Moving Average (SARIMA), SARIMA with eXogenous variables (SARIMAX), Artificial Neural Network (ANN), Adaptive Neural-based Fuzzy Inference System (ANFIS), and Random Forest (RF) models were employed with two types of input variables, autoregressive variables (AR-) and a combination of autoregressive and exogenous variables (ARX-). Several statistical methods, including ensemble empirical mode decomposition (EEMD), were used to select the lagged climate indices. Finally, monthly reservoir inflow was forecasted by SARIMA, SARIMAX, AR-ANN, ARX-ANN, AR-ANFIS, ARX-ANFIS, AR-RF, and ARX-RF models. As a result, the use of climate indices in artificial intelligence models showed a potential to improve the model performance, and the ARX-ANN and AR-RF models generally showed the best performance among the employed models.

Modification of the Thomas model for predicting unsymmetrical breakthrough curves using an adaptive neural-based fuzzy inference system.

The Thomas equation is a popular model that has been widely used to predict breakthrough curves (BTCs) when describing the dynamic adsorption of different pollutants in a fixed-bed column system. However, BTCs commonly exhibit unsymmetrical patterns that cannot be predicted using empirical equations such as the Thomas model. Fortunately, adaptive neural-based fuzzy inference systems (ANFISs) can be used to model complex patterns found in adsorption processes in a fixed-bed column system. Consequently, a new hybrid model merging Thomas and an ANFIS was introduced to estimate the performance of BTCs, which were obtained for Cd(II) ion adsorption on ostrich bone ash-supported nanoscale zero-valent iron (nZVI). The results obtained showed that the fair performance of the Thomas model (NRMSE = 27.6% and Ef = 64.6%) improved to excellent (NRMSE = 3.8% and Ef = 93.8%) due to the unique strength of ANFISs in nonlinear modeling. The sensitivity analysis indicated that the initial solution pH was a more significant input variable influencing the hybrid model than the other operational factors. This approach proves the potential of this hybrid method to predict BTCs for the dynamic adsorption of Cd(II) ions by ostrich bone ash-supported nZVI particles.

DALGACIK-ADAPTIF AĞ TEMELLI BULANIK ÇIKARIM SISTEMLERI ILE DALAMAN ÇAYI AKIMLARININ MODELLENMESİ ÜZERİNE BİR ÇALIŞMA

The object of the study is to investigate a flow estimation model by using a combination of Wavelet Transform Technique (W) and Adaptive Neural Based Fuzzy Inference System (ANFIS). Many models has been applied in recent years for the prediction of Dalaman Stream flow in the south of Turkey. One of these studies was AR-ANFIS models which developed by Taylan (2008), its training data set was extended with synthetic series produced by autoregressive processes. In this study, W-ANFIS models were developed with sub-series generated by wavelet analysis by using extended training set of ANFIS models. It is seen that increasing the number of input data in training increases model performance. Compared with the developed models, it has been shown that the W-ANFIS hybrid models have a better predictive power than the AR-ANFIS models. Consequently, the W-ANFIS hybrid model could be used successfully in predicting of flow.

Application of an Adaptive Neural-Based Fuzzy Inference System Model for Predicting Leaf Area

ABSTRACTLeaf Area (LA) is a key index of plant productivity and growth. A multiple linear regression technique is commonly applied to estimate LA as a non-destructive and quick method, but this technique is limited under the realistic situation. Thus, it is indispensable to elaborate new models for estimation. In this research, the performance of the Adaptive Neural-Based Fuzzy Inference System (ANFIS) in predicting the LA of 61 plant species (C) was investigated. Four parameters including leaf length (L), leaf width (W), C, and specific coefficient (K) for each plant were selected as input data to the ANFIS model and the LA as the output. Seven different ANFIS models including different combinations of input data were constructed to reveal the sensitivity analysis of the models. The normalized root mean square error (NRMSE), mean residual error (MRE), and linear regression were applied between observed LA and estimated LA by the models. The results indicated that ANFIS4-K2min which employed all input data was the most accurate (NRMSE = 0.046 and R2 = 0.997) and ANFIS1 which employed only the K input was the worst (NRMSE = 0.452 and R2 = 0.778). In ranking, ANFIS4-K2ave, ANFIS4-K1min, ANFIS4-K1ave, ANFIS3, and ANFIS2 ranked second, third, fourth, fifth, and sixth, respectively. The sensitivity analysis indicated that the predicted LA is more sensitive to the K, followed by L, W, and C. The results displayed that estimations are slightly overestimated. This study demonstrated that the ANFIS model could be accurate and faster alternative to the available laborious and time-consuming methods for LA prediction.

A comparative study between popular statistical and machine learning methods for simulating volume of landslides

This study attempts to compare popular statistical methods (linear, logarithmic, quadratic, power and exponential functions) with machine learning methods (multi-layer perceptron (MLP), radial base function (RBF), adaptive neural-based fuzzy inference system (ANFIS) and support vector machine (SVM)) for simulating the volume of landslides based on their surface area (VL~AL) in the Kurdistan province, Iran. Performances of the models were validated using some commonly error functions including the Adjusted R2, F-test and AIC (Akaike Information Criteria). The results showed that the power model demonstrates the best performance compared to other statistical methods whereas the ANFIS model outperforms other machine learning approaches. Furthermore, the comparative results showed that machine learning methods indicate better performances than simple statistical methods for simulating the volume of landslides in the study area. In practice, the outputs of this research can help managers and investigators decrease the cost of field surveys and measurements of volumes of landslides in landslide hazard management projects.

Daily runoff prediction using the linear and non-linear models.

Runoff prediction, as a nonlinear and complex process, is essential for designing canals, water management and planning, flood control and predicting soil erosion. There are a number of techniques for runoff prediction based on the hydro-meteorological and geomorphological variables. In recent years, several soft computing techniques have been developed to predict runoff. There are some challenging issues in runoff modeling including the selection of appropriate inputs and determination of the optimum length of training and testing data sets. In this study, the gamma test (GT), forward selection and factor analysis were used to determine the best input combination. In addition, GT was applied to determine the optimum length of training and testing data sets. Results showed the input combination based on the GT method with five variables has better performance than other combinations. For modeling, among four techniques: artificial neural networks, local linear regression, an adaptive neural-based fuzzy inference system and support vector machine (SVM), results indicated the performance of the SVM model is better than other techniques for runoff prediction in the Amameh watershed.

Evaluation of adaptive neural-based fuzzy inference system approach for estimating saturated soil water content

The saturated soil water content (θ s ) is an important parameter in hydrological studies. In this paper, adaptive neural-based fuzzy inference system (ANFIS) was used for estimation of soil saturation percentage of some flood spreading areas in Iran. Soil particle size distribution (sand%, silt%, and clay%), bulk density and medium porosity (0.2–30 µm) were used to develop saturated soil water content pedotransfer functions (PTFs). Then, contributions of various member functions (MFs) were assessed on estimation of θ s . The results showed that the member function type has an important role in performance of ANFIS approach. In the present investigation, Gaussian curve (gaussmf) was found to be superior over the other MFs in estimating θ s . In all of the θ s PTFs, correlation between estimations of θ s and corresponding observations was the low. R2 values between measured and PTF-estimated θ s using ANFIS approach did not increase as some input predictors were used in the PTFs (from PTF1 to PTF5). Based on the results, it is suggested that ANFIS model can be applied for reasonable estimation of θ s and there is a need for obtain more information of the proposed approach especially for the selection of best member functions. Therefore, a good performance may be obtained when best member function would be selected in addition to the more effective PTF inputs.

A comparison of performance of artificial intelligence methods in prediction of dry sliding wear behavior

Developing a computational model for studying tribological performance is essential for computing accurate life cycle of various materials. Caused by the existence of complicated and nonlinear interactions between material surfaces, exact modeling of wear behavior is very difficult. Artificial intelligence (AI) can be used in distinguishing similar patterns in experimental data and predictive modeling of a certain material’s wear behavior. In this paper, artificial neural networks (ANNs) approach, adaptive neural-based fuzzy inference system (ANFIS) technique, and fuzzy clustering method (FCM) are used to develop a simple, accurate, and applicable model for predicting the wear behavior of sinter-hardened steel parts. Three different cooling rates (0.5, 2, and 3 °C/s) were applied on six specimens made out of pre-alloyed Astaloy 85 Mo and Distaloy AB powders by powder metallurgy (PM) method. Reciprocating dry sliding wear tests were carried out on these specimens with three different loads. The empirical results were assorted in two different batches. One was used along with the mentioned artificial intelligence approaches to develop three wear behavior models. In order to verify and compare these models, predicted results gained from these models were compared to the second batch of the results. Outcome of the models were promising. Using magnitude of two statistical functions, root-mean squared error and coefficient of multiple determinations, we showed that our predictions are in great correlation with experimental data and the best performance can be obtained using ANN method.

Estimation of dissolved oxygen using data-driven techniques in the Tai Po River, Hong Kong

This study investigates the applicability of multilinear regression (MLR), adaptive neural-based fuzzy inference system (ANFIS) and artificial neural networks (ANN) methods from data-driven techniques in estimation of the dissolved oxygen (DO), which is an important parameter in water quality, aquatic life, efficient water management and health care planning studies. The measured parameters covering 21 years of sample data for the years 1991–2011 in the Tai Po River, Hong Kong, were used to develop the models. The input parameters used to estimate DO are chloride (Cl), pH value (pH), electrical conductivity, temperature (Temp), nitrite nitrogen (NO2-N), nitrate nitrogen (NO3-N), ammonia nitrogen (NH4-N) and total phosphorous (T-P). The performance of the developed models was evaluated through the three performance criteria: correlation coefficient, root mean square error and the Nash–Sutcliffe efficiency coefficient. When the results of the developed models were compared with DO measurements using performance criteria, the ANN model shows better performance than the MLR and ANFIS models in estimation of DO concentration. Also, a sensitivity analysis was carried out to evaluate the relative importance of the input parameters in estimation of the DO. The most effective input parameters are determined as Cl, Temp, NO3-N, NO2-N, NH4-N and T-P parameters, respectively. Furthermore, the pH variable has the least contribution on the ANN model.

Development of an adaptive neural-based fuzzy inference system for feeding decision-making assessment in silver perch (Bidyanus bidyanus) culture

The primary objective of this study was to develop an adaptive neural-based fuzzy inference system (ANFIS) for feeding decision-making in aquaculture. Silver perch (Bidyanus bidyanus) were raised under semi-intensive conditions in Taiwan. Because dissolved oxygen (DO) is a key factor that is helpful in detecting the appetite of fish at the initial period of the feeding activity and because the flocking and struggle behaviors of food-searching fish have a transient effect on the measurement of the DO, a simple water-reused rearing tank was prepared to measure the DO to develop a fuzzy logic controller (FLC). In the equivalent ANFIS of the FLC, two linguistic variables were used to describe the food-searching state of the fish population and establish a rule base composed of 15 rules. Furthermore, an alternate hybrid learning approach, which is a fuzzy logic technique based on artificial neural networks, was suggested to quickly model the linguistic variables and evaluate their relative contributions. The results indicated that a decision threshold of 0.17, which was inferred using the fuzzy logic approach, considerably benefits the feeding decision; the high rate of accurate judgments (with an accuracy of 97.89%), which was obtained by the ANFIS model, was close to the actual food searching behaviors of fish. Therefore, the application of the ANFIS model to the feeding decision system in an aquaculture rearing tank has considerable potential for success.

Artificial neural network modeling of dissolved oxygen in reservoir

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.

Application of Artificial Neural Network and Adaptive Neural-based Fuzzy Inference System Techniques in Estimating of Virtual Water

Wheat, barley, sugerbeet, potato, alfalfa, and corn are common crops produced in Iran, which need the most virtual water volume compared to other crops. Determination of the virtual water for these crops would assist in better management of water resources. The main objective of this study is to find out the best technique for estimating and mapping of virtual water. In this research, the virtual water volume was determined by crop water requirement and crop yields using three ANN structures as well as ANFIS technique. Based on RMSE and R the comparison of obtained results predicted through the applied ANNs structures indicate that the RBF outperforms the other models for estimating virtual water for wheat, potato, corn, and barley. Moreover, a comparison between RBF and ANFIS revealed that ANFIS is a promising model, which can be efficient mathematical tool for estimation of crop‟s virtual water.