Adaptive Neuro-fuzzy Interface System Model Research Articles

Prediction of colour strength in environmentally‐friendly dyeing of polyester fabric with madder using supercritical carbon dioxide

AbstractThe textile industry is one of the significant reasons for global water pollution, with dyeing processes being particularly environmentally detrimental. Researchers have explored alternative approaches to address this issue, such as using natural dyes, supercritical fluids and so forth. In addition to environment‐friendly approaches, reducing the number of experiments in studies, accurate production straightaway and using artificial intelligence (AI), one of the technologies of the present and the future that will provide significant support. Reaching clearer results with AI technology will not necessarily contribute to environment‐friendly technologies. However, AI techniques, including artificial neural networks (ANNs) and adaptive neuro fuzzy interface system (ANFIS) were employed to predict the colour strength (K/S) of the dyed fabric based on process parameters. A comprehensive experimental design involving pressure, temperature, and time variations was conducted, and the results were analysed using multi‐factor analysis of variance (MANOVA). The study demonstrates that supercritical carbon dioxide (scCO2) dyeing with madder on polyester fabric is a promising and environmentally friendly approach. Additionally, the optimised ANN and ANFIS models, aided by genetic algorithms (GAs), exhibit high predictive accuracy (less than 3%), providing insights into the impact of process parameters on colour strength. This research underscores the potential of AI‐driven automation in textile dyeing, offering solutions for dye formula prediction, colour matching, and defect detection, reducing the need for human intervention in these processes.

Physics-based and machine-learning models for accurate scour depth prediction.

Scour phenomena remain a significant cause of instability in offshore structures. The present study estimates scour depths using physics-based numerical modelling and machine-learning (ML) algorithms. For the ML prediction, datasets were collected from previous studies, and the trained models checked against the statistical measures and reported outcomes. The numerical assessment of the scour depth has been also carried out for the current and coupled wave-current environment within a computational fluid dynamics framework with the aid of the open-source platform REEF3D. The outcomes are validated against the previously reported experimental studies. The results obtained from ML schemes demonstrated that the artificial neural network and adaptive neuro-fuzzy interface system models have an elevated level of effectiveness compared with the other models. Whereas the numerical analysis results show a good agreement against the reported values. For the current only conditions, the normalized scour depth (S/D) at the front and rear end of the pier is 0.65 and 0.81. For the wave-current conditions, the normalized scour depth (S/D) is 0.26. The study highlights the importance of machine learning and physics-based numerical modelling to assess scour depth within a reasonable time frame without compromising accuracy. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.

Application of a hybrid ANFIS with metaheuristic algorithms to estimate the aeration design parameters

Abstract Cavitation is a common and complex hydraulic phenomenon on the chute spillways and may cause damage to the structure. Aeration in the water flow is one of the best ways to prevent cavitation. To design an aerator, estimation of aeration coefficient (β), jet length (L/h0), and jet impact angle on chute (tanγ) are important in this study. The potential of a hybrid adaptive neuro-fuzzy interface system (ANFIS) with metaheuristic algorithms was investigated to estimate the required parameters to design an aerator. The ANFIS was combined with four metaheuristic algorithms, including differential evolution (DE), ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO). Experimental data and dimensionless parameters were used to develop the proposed hybrid ANFIS models. Three statistical indicators, including root mean square error (RMSE), mean average error (MAE), and coefficient of determination (R2), were employed to compare the proposed methods with empirical relations. According to the statistical indicators, among the data-driven methods, the ANFIS–DE method had the best prediction in estimating β (RMSE = 0.018, R2 = 0.984, MAE = 0.013), L/h0 (RMSE = 1.293, R2 = 0.963, MAE = 1.082), and tanγ (RMSE = 0.009, R2 = 0.939, MAE = 0.007).

Magnetic and structural characterization of Sn doped cobalt ferrites; A visible light-driven photocatalysts for degradation of rhodamine-B and modeling the process by artificial intelligence tools

Tin (Sn) substituted cobalt ferrites Co1−xSnxFe2O4 with composition (x = 0.0, 0.1, 0.2, 0.3, 0.4 and 0.5) were synthesized utilizing sol-gel auto combustion method. All synthesized powders were characterized for their structural, magnetic and optical characterization after calcination. The single phase spinel with good crystallisation and a decreasing crystallite size with Sn substitution is confirmed by the powder X-ray diffraction (XRD) pattern. The magnetic measurements were carried out at room temperature where saturation magnetization values show sufficient magnetic nature of calcinated ferrites. The bandgap values for pure cobalt and Sn-substituted cobalt ferrites were closer to the experimental value. The visible light photocatalytic degradation of Rhodamine B was carried out in presence of Sn substituted cobalt ferrites that shows good catalytic activity up to 87% degradation for Co0.5Sn0.5Fe2O4 catalyst. Additionally, this study used Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Interface System (ANFIS) models to simulate degradation efficiency with inputs including time, pH, and catalyst dosage. The results revealed that the 3–2–2–1 structure ANN4 model outperformed the other ANN and ANFIS models in the testing phase (RMSE = 6.90% and NSE = 0.92). According to the findings of this study, artificial intelligence models are capable of precisely predicting Rhodamine B degradation.

Comparative Study of Soft Computing and Metaheuristic Models in Developing Reduced Exhaust Emission Characteristics for Diesel Engine Fueled with Various Blends of Biodiesel and Metallic Nanoadditive Mixtures: An ANFIS-GA-HSA Approach.

Since the discovery of petrol-based products, a surge in energy-requiring equipment has been established across the world. Recent depletion of the existing crude oil resources has motivated researchers to opt for and analyze potential fuels that could potentially provide a cost-effective and sustainable solution. The current study selects a waste plant known as Eichhornia crassipes through which biodiesel is generated, and its blends are tested in diesel engines for feasibility. Different models using soft computing and metaheuristic techniques are employed for the accurate prediction of performance and exhaust characteristics. The blends are further mixed with nanoadditives, thereby exploring and comparing the changes in performance characteristics. The input attributes considered in the study comprise engine load, blend percentage, nanoparticle concentration, and injection pressure, while the outcomes are brake thermal efficiency, brake specific energy consumption, carbon monoxide, unburnt hydrocarbon, and oxides of nitrogen. Models were further ranked and chosen based on their set of attributes using the ranking technique. The ranking criteria for models were based on cost, accuracy, and skill requirement. The ANFIS harmony search algorithm (HSA) reported a lower error rate, while the ANFIS model reported the lowest cost. The optimal combination achieved was 20.80 kW, 2.48047, 150.501 ppm, 4.05025 ppm, and 0.018326% for brake thermal efficiency (BTE), brake specific energy consumption (BSEC), oxides of nitrogen (NOx), unburnt hydrocarbons (UBHC), and carbon monoxide (CO), respectively, thereby furnishing better results than the adaptive neuro-fuzzy interface system (ANFIS) and the ANFIS-genetic algorithm model. Henceforth, integrating the results of ANFIS with an optimization technique with the harmony search algorithm (HSA) yields accurate results but at a comparatively higher cost.

Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models

Predicting crop yield is an important issue for farmers. Food security is important for decision-makers. The agriculture industry can more accurately supply human demand for food if the crop yield is predicted accurately. Tomato is one of the most important crops so that 160 million tonnes of tomatoes are produced annually around the world. In this study, tomato yield based on data of 40 cities of Iran country including annual average temperature (T), relative humidity (RH), effective rainfall (R), wind speed (WS), and Evapotranspiration (EV) for the period of 1968–2018 was predicted using a new Bayesian model averaging (BMA). The paper's main innovation is the use of the new BMA so that it allows the modellers to quantify the uncertainty of model parameters and inputs simultaneously. For this aim, first, the multiple Adaptive neuro-fuzzy interface system (ANFIS) and multi-layer perceptron (MLP) were used for predicting crop yield. To train the ANFIS and MLP model, a new algorithm, namely, multi verse optimization algorithm (MOA) was used. Also, the ability of MOA was benchmarked against the particle swarm optimization (PSO), and firefly algorithm (FFA). In the next level, the new BMA used the outputs of the ANFIS-MOA, MLP-MOA, ANFIS, FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP for predicting tomato yield in an ensemble framework. The five- input combination of RH, T, and R, WS, and EV gave the best result. The mean absolute error (MAE) of the BMA in the testing level was 20.12 (Ton/ha) while it was 24.12, 24.45, 24.67, 25.12, 29.12, 30.12, 31.12, and 33.45 for the ANFIS-MOA, MLP-MOA, ANFIS-FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP models. Regarding the results of uncertainty analysis, the uncertainty of BMA was lower than those of the ANFIS-MOA, MLP-MOA, ANFIS-FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP models while the MLP model provided the highest uncertainty. The results of this study indicated that BMA using multiple MLP and ANFIS model was useful for predicting tomato yield.

Multi-model ensemble prediction of pan evaporation based on the Copula Bayesian Model Averaging approach

Pan evaporation (Ep) is an efficient and practical tool for planning and managing water resources, understanding the water balance in hydrological processes, and developing irrigation systems, particularly in regions with limited water resources. Accurate prediction of Ep using easy-to-measure meteorological variables is beneficial for any region. The current study aimed to explore the applicability of Copula-based Bayesian Model Averaging (CBMA) for improving probabilistic Ep predictions in different climates of Iran. To this end, the parameters of the Adaptive Neuro-Fuzzy Interface System (ANFIS) were optimized using four meta-heuristic algorithms of Seagull Optimization Algorithm (SOA), Crow search Algorithm (CA), Firefly Algorithm (FFA), and Particle Swarm Optimization (PSO) for finding global solutions. The ANFIS-SOA, ANFIS-CA, ANFIS-FFA, ANFIS-PSO, and ANFIS models were implemented as inputs for employing ensemble CBMA and Bayesian Model Averaging (BMA) methods. Daily meteorological variables of average air temperature (Ta), sunshine hours (SH), relative humidity (RH), wind speed (WS), and Ep from six stations from 2000 to 2003 were applied. Evaluation of five Posterior Distribution Functions (PDFs) and three copula functions showed that Gumbel marginal distribution and Gumbel–Hougaard copula function provide the smallest Kolmogorov–Smirnov statistic indicator at the 5% significance level for all predictive models. The results established that the ensemble CBMA approach exhibited the highest prediction accuracy in all climates, followed by the BMA model, superior to the other individual models. The average root mean square error (RMSE) of the ensemble CBMA model was lower than BMA, ANFIS-SOA, ANFIS-CA, ANFIS-PSO, ANFIS-FFA, ANFIS, by 20.35%, 43.19%, 51.28%, 56.74%, 61.15%, and 64.36%, respectively. Furthermore, the uncertainty analysis indicated the Ep predictions were more confident after applying CBMA. In conclusion, we highly recommend applying the ensemble CBMA model to improve Ep’s prediction performance by standalone and hybrid machine learning models. The combination of Ta-SH was suggested to be used for robust predicting daily Ep in the regions of hot desert, cold desert, and cold semi-arid and Ta-RH in the humid subtropical area regarding the convenience of data acquisition.

Solar radiation prediction using improved soft computing models for semi-arid, slightly-arid and humid climates

In this research, monthly solar radiation is predicted in semi-dry, dry, and wet climates. Adaptive neurofuzzy interface system (ANFIS), radial basis function neural network (RBFNN), and multi-layer perceptron (MLP) models are used for predicting solar radiation (SR). Grasshopper algorithm (GOA) is utilized to improve the performance of ANFIS, RBFNN, and MLP models. Three stations in Iran, namely Rasht (humid climate), Yazd (semi-arid) and Tehran (slightly arid), are considered as the case studies. The accuracy of GOA is benchmarked against particle swarm optimization (PSO) and salp swarm algorithm (SSA). The results reveal that the best-input combination is relative humidity, wind speed, rainfall, and temperature at these three stations. A comprehensive study is performed to select the best-input combination. The main contribution of paper is to create new hybrid ANFIS models for predicting monthly solar radiation in different climates. Besides, effects of different parameters are comprehensively investigated on solar radiation. This study indicates that temperature is the most effective parameter for estimating SR in dry and semi-dry climate. It is found that rainfall plays a key role for estimating SR in a wet region. The main finding of this paper is that the determination of the most suitable input scenario for predicting SR is an important issue because different input scenarios in different climates provide different performances. Besides, the use of a robust optimization algorithm as a training method is a significant step of the modeling process of SR. Results indicate that mean absolute error (MAE) of ANFIS-GOA is 3.8% and 8.9% less in comparison with that of MLP-GOA and RBFNN-GOA, respectively during the training stage at Yazd station. Besides, MAE of ANFIS-GOA is 26% and 31% less than that of MLP-GOA and RBFNN-GOA, respectively during the training stage at Tehran station. Results indicate that NSE values of ANFIS-GOA, ANFIS-SSA, ANFIS-PSO, and ANFIS models are 0.94, 0.88, 0.84, and 0.79, respectively during the testing stage at Rasht station. It is found that ANFIS-GOA attains higher accuracy in predicting SR under different climates.

Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

The quality of a welded joint is determined by key attributes such as dilution and the weld bead geometry. Achieving optimal values associated with the above-mentioned attributes of welding is a challenging task. Selecting an appropriate method to derive the parameter optimality is the key focus of this paper. This study analyzes several versatile parametric optimization and prediction models as well as uses statistical and machine learning models for further processing. Statistical methods like grey-based Taguchi optimization is used to optimize the input parameters such as welding current, wire feed rate, welding speed, and contact tip to work distance (CTWD). Advanced features of artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) models are used to predict the values of dilution and the bead geometry obtained during the welding process. The results corresponding to the initial design of the welding process are used as training and testing data for ANN and ANFIS models. The proposed methodology is validated with various experimental results outside as well as inside the initial design. From the observations, the prediction results produced by machine learning models delivered significantly high relevance with the experimental data over the regression analysis.

Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking

Forecasting the compressive strength of concrete is a complex task owing to the interactions among concrete ingredients. In addition, an important characteristic of the concrete failure surface is its six-fold symmetry. In this study, an artificial neural network (ANN) and adaptive neuro fuzzy interface system (ANFIS) were employed to model the compressive strength of natural volcanic ash mortar (VAM) by using the six-fold symmetry of concrete failure. The modeling was correlated with four parameters. To train and test the projected models, data for more than 150 samples were collected from the literature. Furthermore, mortar samples with varying proportions of volcanic ash were prepared in the laboratory and tested, and the results were used to validate the models. The performance of the developed models was assessed using numerous statistical measures. The results show that both the ANN and ANFIS models accurately predict the compressive strength of VAM with R-square above 0.9 and lower error statistics. The permutation feature analysis confirmed that the age of specimens affects the strength of VAM the most, followed by the water-to-cement ratio, curing temperature, and percentage of volcanic ash.

Suspended sediment load prediction based on soft computing models and Black Widow Optimization Algorithm using an enhanced gamma test.

The suspended sediment load (SSL) prediction is one of the most important issues in water engineering. In this article, the adaptive neuro-fuzzy interface system (ANFIS) and support vector machine (SVM) were used to estimate the SLL of two main tributaries of the Telar River placed in the north of Iran. The main Telar River had two main tributaries, namely, the Telar and the Kasilian. A new evolutionary algorithm, namely, the black widow optimization algorithm (BWOA), was used to enhance the precision of the ANFIS and SVM models for predicting daily SSL. The lagged rainfall, temperature, discharge, and SSL were used as the inputs to the models. The present study used a new hybrid Gamma test to determine the best input scenario. In the next step, the best input combination was determined based on the gamma value. In this research, the abilities of the ANFIS-BWOA and SVM-BWOA were benchmarked with the ANFIS-bat algorithm (BA), SVM-BA, SVM-particle swarm optimization (PSO), and ANFIS-PSO. The mean absolute error (MAE) of ANFIS-BWOA was 0.40%, 2.2%, and 2.5% lower than those of ANFIS-BA, ANFIS-PSO, and ANFIS models in the training level for Telar River. It was concluded that the ANFIS-BWOA had the highest value of R2 among other models in the Telar River. The MAE of the ANFIS-BWOA, SVM-BWOA, SVM-PSO, SVM-BA, and SVM models were 899.12 (Ton/day), 934.23 (Ton/day), 987.12 (Ton/day), 976.12, and 989.12 (Ton/day), respectively, in the testing level for the Kasilian River. An uncertainty analysis was used to investigate the effect of uncertainty of the inputs (first scenario) and the model parameters (the second scenario) on the accuracy of models. It was observed that the input uncertainty higher than the parameter uncertainty.

Adaptive neuro-fuzzy interface system and neural network modeling for the drying kinetics of instant controlled pressure drop treated parboiled rice.

Hot air drying kinetics of paddy grains during instant controlled pressure drop (ICPD) assisted parboiling process and its impact on the quality and micro-structural properties of milled rice were investigated. Among five mathematical models, Midilli model showed best fitted outcomes for prediction of adequate drying behavior. For the mapping of moisture ratio (MR) as a function of treatment pressure (TP), decompressed state duration (DD) and drying time (DT), artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) were applied. ANFIS model (5-5-5) with Gaussian membership function demonstrated best performance when contrasted with 3-5-1 ANN architecture. Effective diffusivity of the drying process varied from 2.8 × 10-09 to 7.0 × 10-09 m2/s with the increase of TP and DD. In comparison of quality parameters with the variation of TP and DD, positive impacts on head rice yield (HRY), redness (a*) and yellowness (b*) values and negative consequences on cooking time (CT) and brightness (L*) value were observed. The outcomes additionally uncovered that parboiled rice obtained at 0.6 MPa TP, indicated best quality in terms of improved process performance, HRY, CT, color and micro-structural properties.

Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN

In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty analysis of predictions and spatial variation analysis. The six schemes, including grasshopper optimization algorithm (GOA), cat swarm optimization (CSO), weed algorithm (WA), genetic algorithm (GA), krill algorithm (KA), and particle swarm optimization (PSO), were used to hybridize for improving the performance of ANN, SVM, and ANFIS models. Groundwater level (GWL) data of Ardebil plain (Iran) for a period of 144 months were selected to evaluate the hybrid models. The pre-processing technique of principal component analysis (PCA) was applied to reduce input combinations from monthly time series up to 12-month prediction intervals. The results showed that the ANFIS-GOA was superior to the other hybrid models for predicting GWL in the first piezometer (RMSE:1.21, MAE:0.878, NSE:0.93, PBIAS:0.15, R2:0.93), second piezometer (RMSE:1.22, MAE:0.881, NSE:0.92, PBIAS:0.17, R2:0.94), and third piezometer (RMSE:1.23, MAE:0.911, NSE:0.91, PBIAS:0.19, R2:0.94) in the testing stage. The performance of hybrid models with optimization algorithms was far better than that of classical ANN, ANFIS, and SVM models without hybridization. The percent of improvements in the ANFIS-GOA versus standalone ANFIS in piezometer 10 were 14.4%, 3%, 17.8%, and 181% for RMSE, MAE, NSE, and PBIAS in training stage and 40.7%, 55%, 25%, and 132% in testing stage, respectively. The improvements for piezometer 6 in train step were 15%, 4%, 13%, and 208% and in test step were 33%, 44.6%, 16.3%, and 173%, respectively, that clearly confirm the superiority of developed hybridization schemes in GWL modelling. Uncertainty analysis showed that ANFIS-GOA and SVM had, respectively, the best and worst performances among other models. In general, GOA enhanced the accuracy of the ANFIS, ANN, and SVM models.

Modeling of mixed convection in an enclosure using multiple regression, artificial neural network, and adaptive neuro-fuzzy interface system models

In this study, the heat transfer characteristics of laminar combined forced convection through a horizontal duct are obtained with the help of the numerical methods. The effect of the geometrical parameters of the cavity and Reynolds number on the heat transfer is investigated. New heat transfer correlation for hydrodynamically fully developed, laminar combined forced convection through a horizontal duct is proposed with an average error of 6.98% and R2 of 0.8625. The obtained correlation results are compared with the artificial neural network and adaptive neuro-fuzzy interface system models. Due to the obtained results, good agreement is identified between the numerical results and predicted adaptive neuro-fuzzy interface system results. In conclusion, it is seen that adaptive neuro-fuzzy interface system can predict the Nusselt number distribution with a higher accuracy than the developed correlation and the artificial neural network model. The developed adaptive neuro-fuzzy interface system model predicts the Nusselt number with 1.07% mean average percentage error and 0.9983 R2 value. The effect of the different training algorithms and their ability to predict Nusselt number distribution are examined. According to the results, the Bayesian regulation algorithm gives the best approach with a 2.235% error. According to the examination that is performed in this study, the adaptive neuro-fuzzy interface system is a powerful, robust tool that can be used with confidence for predicting the thermal performance.

ANFIS Model for Prediction of Performance-Emission Paradigm of a DICI Engine Fueled with the Blends of Fish Oil Methyl Ester, n-Pentanol and Diesel

A precise, robust model for complex systems like IC Engines would be much beneficial because of environmental issues, fossil fuel depletion and accumulation of on-road vehicles. The present study depicts the compatibility of higher alcohols like n-pentanol that are produced in renewable ways as a promising blending additive with biodiesel fuels. Biodiesel prepared from the waste parts of the fishes is used to blend with petrodiesel. The methyl esters of fishoil biodiesel (MEFO) and n-pentanol are blended with petrodiesel at different proportions are tested on a four-stroke single cylinder DICI engine and results from witnesses the noble benefits of adding higher alcohols that are observed in both performance and as well as in emissions. The experimental paradigm is further fed to an artificial intelligent model to test the inherent predicting capability an Artificial Intelligent Adaptive Neuro-fuzzy Interface System (ANFIS). A sugeno network with brake power and percentage of biodiesel as input parameters and engine response paradigm such as BSFC, BTE, HC, CO and NOx as outputs are modelled and tested on a statistical platform. It was found that the proposed model is robust and efficient system identification tool to map the input-output response paradigm with high accuracy as the regression (R) values are ranging from 0.9967 to 0.9999, RMSE is ranging 0.000026 to 0.0000336 and MAPE is very low ranging from 0.0021 to 0.0028.

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

The present study elaborates the suitability of the artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) to predict the thermal performances of the thermoelectric generator system for waste heat recovery. Six ANN models and seven ANFIS models are formulated by considering hot gas temperatures and voltage load conditions as the inputs to predict current, power, and thermal efficiency of the thermoelectric generator system for waste heat recovery. The ANN model with the back-propagation algorithm, the Levenberg–Marquardt variant, Tan-Sigmoidal transfer function and 25 number of hidden neurons is found to be an optimum model to accurately predict current, power and thermal efficiency. For current, power and thermal efficiency, the ANFIS model with pi-5 or gauss-5-membership function is recommended as the optimum model when the prediction accuracy is important while the ANFIS model with gbell-3-membership function is suggested as the optimum model when the prediction cost plays a crucial role along with the prediction accuracy. The proposed optimal ANN and ANFIS models present higher prediction accuracy than the coupled numerical approach.

Accuracy enhancement for monthly evaporation predicting model utilizing evolutionary machine learning methods

Evaporation is an important parameter for water resource management. In this article, two case studies with different climates were considered in the prediction of monthly evaporation. The optimization algorithms, namely shark algorithm (SA) and firefly algorithms (FFAs), were used to train the adaptive neuro-fuzzy interface system (ANFIS), multilayer perceptron (MLP) model and radial basis function (RBF) model for the prediction of monthly evaporation. The monthly weather data from two stations, Mianeh station and Yazd station, operated by the Iran Meteorological Service were used to examine the proposed models. In the quantitative analysis, the hybrid ANFIS-SA improved the MAE index over the ANFIS, RBF, MLP, RBF-SA, MLP-SA, RBF-FFA, MLP-FFA and ANFIS-SA up to 47% during training and to 51% during testing while examining Yazd station. It should be mentioned that the higher RSR and MAE were attained by the hybrid soft computing (ANN-FFA, RBF-FFA and ANFIS-FFA) models in two stations. The results proved that the developed ANFIS models that have been integrated with shark algorithms could be considered as a powerful tool for predicting evaporation.

Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with Anfis, SVM, and Ann

In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty analysis of predictions and spatial variation analysis. The six schemes, including grasshopper optimization algorithm (GOA), cat swarm optimization (CSO), weed algorithm (WA), genetic algorithm (GA), krill algorithm (KA), and particle swarm optimization (PSO), were used to hybridize for improving the performance of ANN, SVM, and ANFIS models. Groundwater level (GWL) data of Ardebil plain (Iran) for a period of 144 months were selected to evaluate the hybrid models. The pre-processing technique of principal component analysis (PCA) was applied to reduce input combinations from monthly time series up to 12-month prediction intervals. The results showed that the ANFIS-GOA was superior to the other hybrid models for predicting GWL in the first piezometer (RMSE:1.21, MAE:0.878, NSE:0.93, PBIAS:0.15, R2:0.93), second piezometer (RMSE:1.22, MAE:0.881, NSE:0.92, PBIAS:0.17, R2:0.94), and third piezometer (RMSE:1.23, MAE:0.911, NSE:0.91, PBIAS:0.19, R2:0.94) in the testing stage. The performance of hybrid models with optimization algorithms was far better than that of classical ANN, ANFIS, and SVM models without hybridization. The percent of improvements in the ANFIS-GOA versus standalone ANFIS in piezometer 10 were 14.4%, 3%, 17.8%, and 181% for RMSE, MAE, NSE, and PBIAS in training stage and 40.7%, 55%, 25%, and 132% in testing stage, respectively. The improvements for piezometer 6 in train step were 15%, 4%, 13%, and 208% and in test step were 33%, 44.6%, 16.3%, and 173%, respectively, that clearly confirm the superiority of developed hybridization schemes in GWL modelling. Uncertainty analysis showed that ANFIS-GOA and SVM had, respectively, the best and worst performances among other models. In general, GOA enhanced the accuracy of the ANFIS, ANN, and SVM models.

Prediction of L10 and Leq Noise Levels Due to Vehicular Traffic in Urban Area Using ANN and Adaptive Neuro-Fuzzy Interface System (ANFIS) Approach

Recently in urban areas, road traffic noise is one of the primary sources of noise pollution. Variation in noise level is impacted by the synthesis of traffic and the percentage of heavy vehicles. Presentation to high noise levels may cause serious impact on the health of an individual or community residing near the roadside. Thus, predicting the vehicular traffic noise level is important. The present study aims at the formulation of regression, an artificial neural network (ANN) and an adaptive neuro-fuzzy interface system (ANFIS) model using the data of observed noise levels, traffic volume, and average speed of vehicles for the prediction of L10 and Leq. Measured noise levels are compared to the noise levels predicted by the experimental model. It is observed that the ANFIS approach is more superior when compared to output given by regression and an ANN model. Also, there exists a positive correlation between measured and predicted noise levels. The proposed ANFIS model can be utilized as a tool for traffic direction and planning of new roads in zones of similar land use pattern.

Estimation of Tunnel Desilter Sediment Removal Efficiency by ANFIS

The tunnel desilter is a simple and economical fluidic device which is the most suitable over other alternative devices for the region if water is abundantly available. The flow mechanism in the tunnel desilter is so complex that it is difficult to estimate the sediment removal efficiency accurately using a conventional regression. Hence, in the present study AI-based techniques, adaptive neurofuzzy interface system (ANFIS) and artificial neural network (ANN), were employed to estimate the sediment removal efficiency of the tunnel desilter using the data-sets collected by conducting the laboratory test. Findings of the sensitivity analysis showed that the size of the sediment was the most significant parameter followed by the concentration in the estimation of removal efficiency. The results of AI-based modeling were also compared with the available conventional predictive regression models, and it was found that the triangular membership function-based ANFIS model outperformed the other considered models. Further, ANN was also found to be giving comparable results.