Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

In this paper, we compare and analyze the prediction performance of existing ANFIS (Adaptive Neuro Fuzzy Inference System) models. The ANFIS model is a fusion of neural networks with learning ability, adaptability, and computational ability, and a fuzzy system with human-like thinking ability and reasoning ability. In this paper, we analyze the prediction performance of ANFIS models using different input space partitioning methods. ANFIS 1 uses SC (Subtractive Clustering), a clustering method based on the density of data, to calculate the center of the cluster by calculating potential values inversely proportional to the distances of the different inputs. ANFIS 2 uses Fuzzy C-Means (FCM) clustering, which is a clustering method based on degree of belonging to data, and classifies each data belonging to each cluster according to degree of affiliation. ANFIS 3 is a method of using Context based Fuzzy C-Means (CFCM) clustering, which is a clustering method considering characteristics of input and output space based on FCM clustering. It creates clusters for each context generated according to characteristics of output space, to classify the data. To evaluate these ANFIS models, we use short term electricity price forecasting data and Boston housing data. The prediction performance is based on the Root Mean Squared Error (RMSE) method. Experimental results show that the prediction performance of the ANFIS model is different depending on the characteristics of each data. Furthermore, we confirmed that the prediction performance of ANFIS model using CFCM clustering method is constant.

The present article aims to forecast streamflow by using artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and autoregressive moving average (ARMA). For this purpose, the daily streamflow time series of two hydrometry stations of Hajighoshan and Tamar on Gorgan River are used for two periods of 1983–2007 and 1974–2007, respectively. Root mean square error (RMSE) and correlation coefficient (R) statistics are employed to evaluate the performance of the ANNs, ANFIS, and ARMA models for forecasting streamflow (1 day ahead). Comparison of the results reveals that the ANFIS model outperforms the ARMA model. Based on the results of validation stage, for the forecasting 1 day ahead streamflow, ANN with RMSE = 0.028 m3/s and R = 0.59 for the Hajighoshan station and RMSE = 0.013 m3/s and R = 0.44 for the Tamar station were found to be superior to the ANFIS with RMSE = 1.98 m3/s and R = 0.42 for the Hajighoshan station and RMSE = 2.18 m3/s and R = 0.22 for the Tamar station. In addition, for 2 day and 3 day ahead streamflow forecasts, the ANN models show superiority in the accuracy of forecasting streamflow compared with the ANFIS models.

The applicability of artificial neural networks (ANNs) and the adaptive neuro-fuzzy inference system (ANFIS) for determination of mean velocity and discharge of natural streams is investigated. The 2,184 field data obtained from four different sites on the Sarimsakli and Sosun streams in central Turkey were used in the study. ANNs and ANFIS models use the inputs, water surface velocity and water surface slope, to estimate the mean velocity and discharges of natural streams. The accuracies of both models were compared with the multiple-linear regression (MLR) model. The comparison results showed that the ANFIS model performed better than the ANNs and regression models for estimating mean velocity and discharge. The ANN model also showed better accuracy than the MLR model. The root mean square errors (RMSE) and mean absolute relative errors (MARE) of the MLR model were reduced by 88 and 91 % using the ANFIS model in estimating discharges, respectively. It is found that the optimal ANFIS model with RMSE of 0,063, MARE of 3,47 and determination coefficient (R2) of 0,996 in the test period is superior in estimation of discharge than the MLR model with RMSE of 0,532, MARE of 38,9 and R2 of 0,776, respectively. The study reveals that the ANFIS technique can be successfully used for estimating the mean velocity and discharge of natural streams by using only the inputs of water surface velocity and water surface slope.

This paper presents the possibility of using Adaptive Neuro Fuzzy Inference System for Power Transformer Paper Condition Assessment. The dielectric characteristics, dissolved gasses, and furan of 108 running transformers is collected. The 2-furaldehyde (2FAL) data is transformed to Degree of Polymerization (DP), and then statistically analysed to get independent variables as the predictor for the transformer paper condition assessment. CO and CO2 are well known as one of the product of cellulose degradation, while interfacial tension, acidity, and color from the oil are statistically correlated with furan. ANFIS (Adaptive Neuro-Fuzzy Inference System) and Multiple Regression (MR) model is built based on the previous statistical analysis, and then the result is evaluated and compared, resulting in better accuracy of ANFIS model. Three different evaluation criteria MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error) calculated from ANFIS prediction are lower than those from MR model, with the MAPE of ANFIS model is 15.38%.

This study used two methodologies to model microwave drying kinetics of tomato slices: artificial neural networks (ANN) and the adaptive neuro-fuzzy inference system (ANFIS).The tomatoes were pre-treated with water blanching (WBP), ascorbic acid (AAP), and sodium metabisulphite (SBP).The tomatoes were then dried in the microwave at 90, 180, and 360 W after being sliced into 4-, 6-, and 8-mm thicknesses.After fitting ANN and ANFIS models to the experimental drying data, the optimal model topology was identified.The predictive accuracy of these models was assessed through these metrics: the coefficient of determination (R 2 ), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), by contrasting the projected results with experimental data.The results showed a range of 0.92 to 3.75 h for drying time, 0.28 to 2.86 x 10 -8 m 2 /s for Deff, and 0.0027 to 0.0063 kWh/kg for SEC.The results indicated a highperformance capacity of ANFIS compared to ANN, with a higher R 2 of 1.0000 and a lower MSE of 0.9999 to 1.0000, an RMSE of 1.45x10 -11 to 0.00309, and a MAE of 1.15x10 -11 to 0.00296.Consequently, the ANFIS model demonstrated superior predictive capabilities compared to the ANN model, achieving a strong fit with the observed data.

Suspended sediment modeling is an important subject for decision‐makers at the catchment level. Accurate and reliable modeling of suspended sediment load (SSL) is important for planning, managing, and designing of water resource structures and river systems. The objective of this study was to develop artificial intelligence‐ (AI‐) based ensemble methods for modeling SSL in Katar catchment, Ethiopia. In this paper, three single AI‐based models, that is, support vector machine (SVM), adaptive neurofuzzy inference system (ANFIS), feed‐forward neural network (FFNN), and one conventional multilinear regression (MLR) modes, were used for SSL modeling. Besides, four different ensemble methods, neural network ensemble (NNE), ANFIS ensemble (AE), weighted average ensemble (WAE), and simple average ensemble (SAE), were developed by combining the outputs of the four single models to improve their predictive performance. The study used two‐year (2016‐2017) discharge and SSL data for training and verification of the applied models. Determination coefficient (DC) and root mean square error (RMSE) were used to evaluate the performances of the developed models. Based on the performance measure results, the ANFIS model provides higher efficiency than the other developed single models. Out of all developed ensemble models, the nonlinear ANFIS model combination method was found to be the most accurate method and could increase the efficiency of SVM, MLR, ANFIS, and FFNN models by 19.02%, 37%, 9.73%, and 16.3%, respectively, at the verification stage. Overall, the proposed ensemble models in general and the AI‐based ensemble in particular provide excellent performance in SSL estimation.

Today, in the field of science and technology, huge forecasting applications are used by scholars to forecast future values. Nowadays, using estimating the flood forecasting for peak flow discharges is very common for the risk assessment annually by quantitative data collections from different resources. The very famous and longest rivers of Pakistan i.e. Indus River and other rivers too like River Jhelum, River Kabul, and River Chenab are the prime sources of flooding. These rivers are the prime tributaries of the Indus River System. Pakistan's longest river, River Indus, is connected with the seven (7) gauge stations called Dams and barrages, and they are playing a vital role in the generation of electricity and also in irrigation for Pakistan. In this research paper, we calculated the flood risk for the Indus using the streamflow discharges on the daily basis. At present, Adaptive Neuro-Fuzzy Inference System (ANFIS) model is widely used to analyze these hydrological time series data. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) merges the potentiality of Fuzzy Inference Systems (FIS) and Artificial Neural Networks (ANN) to work out problems of different kinds. For this purpose, we used the data for the years from 2002 to 2012 daily (6-months each year) streamflow period. In our analysis, the root means square error (RMSE) shows that the ANFIS model generated more satisfactory results than other models with minimum prediction errors. The ANFIS model is more reliable and has the feasibility of integrating the essence of a fuzzy system into the real world.1–28

Electrochemical-kinetics, MD-simulation and multi-input single-output (MISO) modeling using adaptive neuro-fuzzy inference system (ANFIS) prediction for dexamethasone drug as eco-friendly corrosion inhibitor for mild steel in 2 M HCl electrolyte

Oil production estimation plays a critical role in economic plans for local governments and organizations. Therefore, many studies applied different Artificial Intelligence (AI) based methods to estimate oil production in different countries. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a well-known model that has been successfully employed in various applications, including time-series forecasting. However, the ANFIS model faces critical shortcomings in its parameters during the configuration process. From this point, this paper works to solve the drawbacks of the ANFIS by optimizing ANFIS parameters using a modified Aquila Optimizer (AO) with the Opposition-Based Learning (OBL) technique. The main idea of the developed model, AOOBL-ANFIS, is to enhance the search process of the AO and use the AOOBL to boost the performance of the ANFIS. The proposed model is evaluated using real-world oil production datasets collected from different oilfields using several performance metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2), Standard Deviation (Std), and computational time. Moreover, the AOOBL-ANFIS model is compared to several modified ANFIS models include Particle Swarm Optimization (PSO)-ANFIS, Grey Wolf Optimizer (GWO)-ANFIS, Sine Cosine Algorithm (SCA)-ANFIS, Slime Mold Algorithm (SMA)-ANFIS, and Genetic Algorithm (GA)-ANFIS, respectively. Additionally, it is compared to well-known time series forecasting methods, namely, Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Neural Network (NN). The outcomes verified the high performance of the AOOBL-ANFIS, which outperformed the classic ANFIS model and the compared models.

Intensification of heat and mass transfer process in MHD carreau nanofluid flow containing gyrotactic microorganisms

River flow forecasting is an essential procedure that is necessary for proper reservoir operation. Accurate forecasting results in good control of water availability, refined operation of reservoirs and improved hydropower generation. Therefore, it becomes crucial to develop forecasting models for river inflow. Several approaches have been proposed over the past few years based on stochastic modeling or artificial intelligence (AI) techniques. In this article, an adaptive neuro-fuzzy inference system (ANFIS) model is proposed to forecast the inflow for the Nile River at Aswan High Dam (AHD) on monthly basis. A major advantage of the fuzzy system is its ability to deal with imprecision and vagueness in inflow database. The ANFIS model divides the input space into fuzzy sub-spaces and maps the output using a set of linear functions. A historical database of monthly inflows at AHD recorded over the past 130 years is used to train the ANFIS model and test its performance. The performance of the ANFIS model is compared to a recently developed artificial neural networks (ANN) model. The results show that the ANFIS model was capable of providing higher inflow forecasting accuracy specially at extreme inflow events compared with that of the ANN model. It is concluded that the ANFIS model can be quite beneficial in water management of Lake Nasser reservoir at AHD.

Currently, manufacturing industries focus on intelligent manufacturing. Prediction and monitoring of tool wear are essential in any material removal process, and implementation of a tool condition monitoring system (TCMS) is necessary. This work presents the flank wear prediction during the hard turning of EN8 steel using the deep learning (DL) algorithm. The turning operation is conducted with three levels of selected parameters. CNMG 120408 grade, TiN‐coated cemented carbide tool is used for turning. Cutting force and flank wear are assessed under dry‐cutting conditions. DL algorithms such as adaptive neuro‐fuzzy inference system (ANFIS) and convolutional auto encoder (CAE) are used to predict the flank wear of the single‐point cutting tool. The DL model is developed with turning parameters and cutting force to predict flank wear. The different ANFIS and CAE models are employed to develop the prediction model. Grid‐based ANFIS structure with Gauss membership function performed better than ANFIS models. The ANFIS model’s average testing error of 0.0074011 mm and prediction accuracy of 99.81% are achieved.

The current study looks at the effectiveness of the removal of nickel (II) from aqueous solution using an adsorption method in a laboratory‐size reactor. An artificial neural network (ANN) and an adaptive neuro‐fuzzy inference system (ANFIS) were used in this study to predict blend hydrogels adsorption potential in the removal of nickel (II) from aqueous solution. Four operational variables, including initial Ni (II) concentration (mg/L), pH, contact duration (min), and adsorbent dose (mg/L), were used as an input with removal percentage (%) as the only output; they were studied to assess their impact on the adsorption study in the ANFIS model. In contrast, 70% of the data was used for training, while 15% of the data was used in testing and validation to build the ANN model. Feedforward propagation with the Levenberg–Marquardt algorithm was employed to train the network. The use of ANN and ANFIS models for experiments was used to optimize, construct, and develop prediction models for Ni (II) adsorption using blend hydrogels. The adsorption isotherm and kinetic models were also used to describe the process. The results show that ANN and ANFIS models are promising prediction approaches that can be applied to successfully predict metal ions adsorption. According to this finding, the root mean square errors (RMSE), absolute average relative errors (AARE), average relative errors (ARE), mean squared deviation (MSE), and R 2 for Ni (II) in the training dataset were 0.061, 0.078, 0.017, 0.019, and 0.986, respectively, for ANN. In the ANFIS model, the RMSE, AARE, ARE, MSE, and R 2 were 0.0129, 0.0119, 0.028, 0.030, and 0.995, respectively. The adsorption process was spontaneous and well explained by the Langmuir model, and chemisorption was the primary control. The morphology, functional groups, thermal characteristics, and crystallinity of blend hydrogels were all assessed.

In this paper, a hybrid meta-heuristic artificial intelligence (AI) method, named “ANFIS–PSOGA,” is developed for modeling the discharge coefficient of circular side orifices for the first time. For this purpose, the adaptive neuro fuzzy inference system (ANFIS) network is optimized by means of two robust optimization algorithms: the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm. Furthermore, the Monte Carlo simulations are utilized to enhance the performance of the AI hybrid model. Besides, the k-fold cross-validation method is employed in order to verify the numerical results. Initially, the performance of three fuzzy inference system (FIS) generations of the ANFIS model including grid partitioning, subtractive clustering and fuzzy C-means clustering are evaluated. After that, using the parameters affecting the discharge coefficient, eleven ANFIS models are defined and then the best ANFIS model and the most influencing input parameters are identified through the conduction of a sensitivity analysis. For the superior ANFIS model, mean absolute percentage error, root-mean-square error and the correlation coefficient (R) are obtained 0.021, 0.020 and 0.871, respectively. Also, the Froude number (Fr) is detected as the most effective input parameter. Moreover, the discharge coefficient of the circular side orifices and the flow field within main channels along the side orifices are simulated through the adoption of a computational fluid dynamics (CFD) model. Additionally, the flow field at three different cross sections (beginning, middle and end of orifice) along the side weir is evaluated. For instance, by advancing the flow toward the side orifice, the Froude number is reduced. Besides, at each cross section, the maximum shear stress is estimated to occur in the vicinity of the crest of the circular side orifice. According to the CFD results, the maximum lateral velocity is estimated near the circular side orifice. In addition, the results yielded by the ANFIS, ANFIS–PSO, ANFIS–PSOGA and CFD models are compared and an uncertainty analysis is carried out for the models. Based on the comparison, ANFIS–PSOGA has the best performance in simulating the discharge coefficient. Ultimately, a simple and practical computer code for the best model (ANFIS–PSOGA) is proposed which can be easily used by scholars and engineers in practical usages.

The energy and draft requirements of a disk plow have been recognized as essential factors when attempting to correctly match it with tractor power. This study examines the possible of using an adaptive neuro-fuzzy inference system (ANFIS) approach and its performance compared to a multiple linear regression (MLR) model to determine the energy and draft requirements of a disk plow. A total of 133 data patterns were obtained by conducting experiments in the field and from the literature. Of these 133 data points, 121 were arbitrarily selected and used for training, and the remaining 12 were used for testing the models. The input variables were plowing depth, plowing speed, soil texture index, initial soil moisture content, initial soil bulk density, disk diameter, disk angle, and disk tilt angle, and output variable was draft of the disk plow. Four membership functions were used with ANFIS: a triangular membership function, generalized bell-shaped membership function, trapezoidal membership function, and Gaussian curve membership function. An evaluation of the outcomes of the ANFIS and MLR modeling shows that the triangular membership function performed better than the other functions. When the ANFIS model draft predictions were compared to the measured values, the average relative error was –1.97%. A comparison of the ANFIS model with other approaches showed that the energy and draft requirements of the disk plow could be estimated with satisfactory accuracy. Keywords: ANFIS, MLR, disk plow, draft, tillage DOI: 10.25165/j.ijabe.20201302.4077 Citation: Al-Dosary N M N, Al-Hamed S A, Aboukarima A M. Application of adaptive neuro-fuzzy inference system to predict draft and energy requirements of a disk plow. Int J Agric & Biol Eng, 2020; 13(2): 198–207.