Performance Of Artificial Neural Network Research Articles

An integrated approach based landslide susceptibility mapping: case of Muzaffarabad region, Pakistan

Landslides result in the devastation of property and loss of lives. This study assesses landslide susceptibility by employing geographic information systems (GIS) and machine learning techniques, that is, support vector machine (SVM) and artificial neural network (ANN), with the integration of advanced optimization techniques, that is, particle swarm optimization (PSO). The landslide-inducing factors considered in this study include fault density, lithology, road density, slope, elevation, flow direction, aspect, earthquake intensity, curvature, Normalized Difference Water Index (NDWI), waterways density, rainfall, and Normalized Difference Vegetation Index (NDVI). The resulting landslide susceptibility maps (LSMs) showed that the areas falling under the high and very high susceptibility class have higher rainfall levels, weak lithology, high NDWI, and flow direction. The accuracy assessment of the techniques showed that ANN with an Area Under the Curve (AUC) of 0.81 performed better than SVM with an AUC of 0.78 without the optimization. Similarly, the performance of ANN was also better than SVM using PSO. During the integrated modeling, the AUC of PSO-ANN was 0.87, whereas the AUC of PSO–SVM was 0.84. The accuracy assessment of the produced LSMs also showed a similar trend in terms of accuracy percentage as that of the models.

Artificial neural network based prediction of ship speed under operating conditions for operational optimization

Ship speed is one of the most fundamental parameters which influences ship design, the energy efficiency of its operation, and safety. Therefore, ship speed selection and prediction under various environmental and operational conditions are of great concern recently for optimizing ship design and operational performance. Among the different approaches that address the ship speed topic, data-driven methodologies and Artificial Neural Network (ANN) techniques are attracting widespread interest due to its efficiency, accuracy, robustness, flexibility, and fault tolerance. Consequently, this study investigates multiple ANN model sizes and architectures to determine the suitable network parameters for ship speed prediction. Thus, we have a good balance between the model’s prediction accuracy and computational complexity. For this study, a publicly-available high-quality operational dataset suitable for benchmarking the results is utilized. This analysis also includes the effect of the data quantity and sampling duration on the data correlation and the ANN performance. The results indicate that the proposed ANN model can accurately predict ship speed under real operational conditions with an error of less than 1 knot. Furthermore, it has been shown that the proposed model can help with the decision-making and optimization processes of voyages planning and execution.

Post-earthquake regional structural damage evaluation based on artificial neural networks considering variant structural properties

The application of artificial neural networks (ANN) to regional seismic damage evaluation is still challenging due to variant structural properties and high computing requirements. This study aims to develop an ANN-based approach of regional seismic damage evaluation and investigate the ANN performance when structural properties like the fundamental period T and ductility factor μ change. By utilizing the Single-Degree-of-Freedom (SDOF) structures, ANN seismic classifiers, whose inputs are multiple intensity measures (IMs) and outputs are damage states, are trained for a structure portfolio with T from 0.3 to 3 s and μ from 1.5 to 6. The importance ranking of the multiple input IMs for classification indicates that the most important IMs for the correct classification of damage states vary when the structural properties change. Acceleration-related IMs play the most important role for short-period structures (e.g., less than 0.5 s), velocity-related IMs are most important for intermediate structures (e.g., 0.5 to 2.0 s), and displacement-related IMs are most important for long-period structures (e.g., larger than 2.0 s). This importance variation of IMs validates the necessity and advantage of ANN seismic classifiers with multiple IMs as input for the regional seismic damage evaluation. Further comparison of two different damage indices shows that the ANN performance will not be compromised by changing the damage index. Overall, the classification accuracy higher than 92% on the structure portfolio shows that the ANN-based regional seismic damage evaluation could be a robust and accurate approach with some limitations to be addressed in future studies.

α-Fe2O3-based artificial synaptic RRAM device for pattern recognition using artificial neural networks

We report on the α -Fe2O3-based artificial synaptic resistive random access memory device, which is a promising candidate for artificial neural networks (ANN) to recognize the images. The device consists of a structure Ag/α-Fe2O3/FTO and exhibits non-volatility with analog resistive switching characteristics. We successfully demonstrated synaptic learning rules such as long-term potentiation, long-term depression, and spike time-dependent plasticity. In addition, we also presented off-chip training to obtain good accuracy by backpropagation algorithm considering the synaptic weights obtained from α-Fe2O3 based artificial synaptic device. The proposed α-Fe2O3-based device was tested with the FMNIST and MNIST datasets and obtained a high pattern recognition accuracy of 88.06% and 97.6% test accuracy respectively. Such a high pattern recognition accuracy is attributed to the combination of the synaptic device performance as well as the novel weight mapping strategy used in the present work. Therefore, the ideal device characteristics and high ANN performance showed that the fabricated device can be useful for practical ANN implementation.

Physics-guided neural networks applied in rotor unbalance problems

Rotary systems are extremely important for the development of industrial production due to the large amount of work performed by these machines. However, rotating machines are prone to unbalance faults, which can reduce efficiency and, in the worst-case scenario, lead to catastrophic failure. Since artificial neural networks (ANNs) are very efficient at recognizing complex patterns, they are a useful tool to help diagnose and prevent rotor unbalance faults. Physics-Guided Machine Learning (PGML) is a class of machine learning algorithm that uses physical laws in its structure. In this paper, a method for unbalance fault identification using PGML is proposed, more specifically ANNs as machine learning—Physics-Guided Neural Networks (PGNN) is used. The first step adopts a standard ANN to locate the nodal position of the experimental fault. Afterwards, the PGNN is performed to quantify the unbalance magnitude and phase angle. Also, a comparison between the performance of the standard ANN and PGNN is accomplished. As input, the networks use simulation data of a rotor supported by hydrodynamic bearings modeled through the finite element method (FEM) and Reynolds’ equation. The results showed that the PGNN has smaller errors and better performance than the standard ANN. In addition, a small increase in the number of neurons improves the results of both networks.

Shunt faults detection and classification in electrical power transmission line systems based on artificial neural networks

PurposeThis study aims to focus on an adaptive method for fault detection and classification of fault types that trigger in three-phase transmission lines using artificial neural networks (ANNs). The proposed scheme can detect and classify several types of faults, including line-to-ground, line-to-line, double-line-to-ground, triple-line and triple-line-to-ground faults.Design/methodology/approachThe fundamental components of three-phase current and voltage were used as inputs in the ANNs. An analysis of the impact of variations in the fault resistance, fault type and fault inception time was conducted to evaluate the ANNs performance. The survey compares the performance of the multi-layer perceptron neural network (MLPNN) and Elman recurrent neural network trained with the backpropagation learning technique to improve each of the three phases of the fault detection and classification process. A detailed analysis validates the choice of the ANNs architecture based on the variation in the number of hidden neurons in each step.FindingsThe mean square error, root mean square error, mean absolute error and linear regression are measured to improve the efficiency of the ANN models for both fault detection and classification. The results indicate that the MLPNN can detect and classify faults with a satisfactory performance.Originality/valueThe smart adaptive scheme is fast and accurate for fault detection and classification in a single circuit transmission line when faced with different conditions and can be useful for transmission line protection schemes.

Comparison of 24 h Surface Ozone Forecast for Poland: CAMS Models vs Simple Statistical Models with Limited Number of Input Parameters

Surface ozone is usually measured in national networks, including the monitoring of gaseous components important for determining air quality and the short-term forecast of surface ozone. Here we consider the option of forecasting surface ozone based on measurements of only surface ozone and several weather parameters. This low-cost configuration can increase the number of locations that provide short-term surface ozone forecast important to local communities. 24 h prediction of the 1-h averaged concentration of surface ozone were presented for rural (Belsk, 20.79° E, 51.84° N) and suburban site (Racibórz, 18.19° E, 50.08° N) in Poland for the period 2018–2021 via simple statistical models dealing with a limited number of predictors. Multiple linear regression (MLR) and artificial neural network (ANN) models were examined separately for each season of the year using temperature, relative humidity, an hour of the day, and 1-day lagged surface ozone values. The performance of ANN (with R2 = 0.81 in Racibórz versus R2 = 0.75 at Belsk) was slightly better than the MLR model (with R2 = 0.78 in Racibórz versus R2 = 0.71 at Belsk). These statistical models were compared with advanced chemical–transport models provided by the Copernicus Atmosphere Monitoring Service. Despite the simplicity of the statistical models, they showed better performance in all seasons, with the exception of winter.

Stock price predictive analysis: An application of hybrid Barnacles Mating Optimizer with Artificial Neural Network

Artificial Neural Network (ANN) is an effective machine learning technique for addressing regression tasks. Nonetheless, the performance of ANN is highly dependent on the values of its parameters, specifically the weight and bias. To improve its predictive generalization, it is crucial to optimize these parameters. In this study, the Barnacles Mating Optimizer (BMO) is employed as an optimization tool to automatically optimize these parameters. As a relatively new optimization algorithm, it has been shown to be effective in addressing various optimization problems. The proposed hybrid predictive model of BMO-ANN is tested on time series data of stock price using six selected inputs to predict the next day’ closing prices. Evaluated based on Mean Square Error (MSE) and Root Mean Square Error (RMSPE), the proposed BMO-ANN exhibits significant superiority over the other identified hybrid algorithms. Additionally, the difference in means between BMO-ANN and other identified hybrid algorithms was found to be statistically significant, with a significance level of 0.05%.

Artificial neural network machine learning prediction of the smoking behavior and health risks perception of Indonesian health professionals.

Health professionals (HPs) can play an important role in influencing the smoking behavior of their patients and the implementation of smoke-free workplace policies. In some countries physicians and dentists may not have a no-smoking policy in place. Breathing in other people's tobacco smoke (second-hand smokers) increase the risk of smoking related diseases. Environmental Tobacco smoke ETS causes a similar range of diseases to active smoking, including various cancers, heart disease, stroke, and respiratory diseases. Little is known about the smoking-related attitudes and clinical practices of HPs in Indonesia. Evidence suggests that high smoking rates remain among male HPs; however, the risk perceptions and attitudes to smoking among Indonesian HPs have not been investigated using prediction model artificial neural networks. For this reason, we developed and validated an artificial neural network (ANN) to identify HPs with smoking behavior. The study population consisted of 240 HPs, including 108 (45%) physicians, and 132 (55%) dentists, with more female (n=159) than male participants (n=81) for both professions. Participants were randomly divided into two sets, the training (192) and test (48) sets. The input variables included gender, profession (doctor or dentist), knowledge regarding smoking-related diseases and awareness of smoking provided to their patients, smoke-free policy in place at their workplace, and smoking status. ANN was constructed with data from the training and selection sets and validated in the test set. The performance of ANN was simultaneously evaluated by discrimination and calibration. After the training, we completed the process using the test dataset with a multilayer perceptron network, determined by 36 input variables. Our results suggested that our final ANN concurrently had good precision (89%), accuracy (81%), sensitivity (85%), and area under the curve (AUC; 70%). ANN can be used as a promising tool for the prediction of smoking status based on health risk perceptions of HPs in Indonesia.

Effects of Different Training Datasets on Machine Learning Models for Pavement Performance Prediction

With improvements in data collection, storage, and processing, machine learning (ML) is gaining momentum as a behavior prediction method in the field of engineering. Several studies have evaluated these algorithms’ potential to predict pavement serviceability, however some challenges limit its use. Training data preprocessing has a great impact on the model’s predictive performance, is highly dependent on the modeler’s experience, and is not typically reported in engineering-related literature. The objective of this study was to assess the effects of data preprocessing, hyperparameter selection, and time series size on the model’s evaluation metrics. Therefore, this paper analyzes the performance of three ML algorithms on maximum deflection (D0) and international roughness index (IRI) prediction: support vector machine, random forest (RF), and artificial neural network (ANN). An R2 and mean square error (MSE) analysis was conducted on 12 training datasets, with two sizes of historical data and five stages of data preprocessing. The results indicated that ANN was the most accurate technique with an R2 of 0.99 and MSE of 20 ×10−3 mm on the D0 prediction and an R2 of 0.91 and MSE of 0.03 m/km on the IRI prediction. RF was also identified as an effective technique, generating similar results with less data preprocessing. The addition of structural and traffic categorical features to the training dataset resulted in the most significant improvement of the support vector regression and ANN performance metrics; the hyperparameter selection was effective only on IRI prediction, especially with the ANN algorithm.

Efficient estimation of average derivatives in NPIV models: Simulation comparisons of neural network estimators

Artificial Neural Networks (ANNs) can be viewed as nonlinear sieves that can approximate complex functions of high dimensional variables more effectively than linear sieves. We investigate the performance of various ANNs in nonparametric instrumental variables (NPIV) models of moderately high dimensional covariates that are relevant to empirical economics. We present two efficient procedures for estimation and inference on a weighted average derivative (WAD): an orthogonalized plug-in with optimally-weighted sieve minimum distance (OP-OSMD) procedure and a sieve efficient score (ES) procedure. Both estimators for WAD use ANN sieves to approximate the unknown NPIV function and are n-asymptotically normal and first-order equivalent. We provide a detailed practitioner’s recipe for implementing both efficient procedures. We compare their finite-sample performances in various simulation designs that involve smooth NPIV function of up to 13 continuous covariates, different nonlinearities and covariate correlations. Some Monte Carlo findings include: (1) tuning and optimization are more delicate in ANN estimation; (2) given proper tuning, both ANN estimators with various architectures can perform well; (3) easier to tune ANN OP-OSMD estimators than ANN ES estimators; (4) stable inferences are more difficult to achieve with ANN (than spline) estimators; (5) there are gaps between current implementations and approximation theories. Finally, we apply ANN NPIV to estimate average partial derivatives in two empirical demand examples with multivariate covariates.

Performance of ANN in Predicting the Tensile and Shear Strength of Al-Steel Explosive Clads

In this study, an artificial neural network (ANN) model is created to predict aluminium-stainless steel explosive clads' tensile and shear strengths. The parameters for the explosive cladding process, such as the loading ratio (mass ratio of the explosive and the flyer, 0.6-1.0), standoff distance (5-9 mm), preset angle (0°-10°), and groove in the base plate (V/Dovetail), were altered. The ANN algorithm was trained in Python using the tensile and shear strengths gathered from 80% of the experiments (60), trials, and prior results. The constructed model was evaluated utilizing the remaining experimental results. The ANN model successfully predicts the tensile and shear strengths with an accuracy of less than 10% deviation from the experimental result.

Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall–Runoff Simulation

Runoff forecasting is important for water resource management. Although deep learning models have substantially improved the accuracy of runoff prediction, the temporal and feature dependencies between rainfall–runoff time series elements have not been effectively exploited. In this work, we propose a new hybrid deep learning model to predict hourly streamflow: SA-CNN-LSTM (self-attention, convolutional neural network, and long short-term memory network). The advantages of CNN and LSTM in terms of data extraction from time series data are combined with the self-attention mechanism. By considering interdependences of the rainfall–runoff sequence between timesteps and between features, the prediction performance of the model is enhanced. We explored the performance of the model in the Mazhou Basin, China; we compared its performance with the performances of LSTM, CNN, ANN (artificial neural network), RF (random forest), SA-LSTM, and SA-CNN. Our analysis demonstrated that SA-CNN-LSTM demonstrated robust prediction with different flood magnitudes and different lead times; it was particularly effective within lead times of 1–5 h. Additionally, the performance of the self-attention mechanism with LSTM and CNN alone, respectively, was improved at some lead times; however, the overall performance was unstable. In contrast, the hybrid model integrating CNN, LSTM, and the self-attention mechanism exhibited better model performance and robustness. Overall, this study considers the importance of temporal and feature dependencies in hourly runoff prediction, then proposes a hybrid deep learning model to improve the performances of conventional models in runoff prediction.

Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries.

Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxide, Lithium Iron Phosphate, Lithium Manganese Oxide, Lithium Titanium Oxide, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. Results: Prediction results with overall Mean Absolute Percentage Error of 0.0126 have been obtained for XGBoost algorithm. Among these results, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide type cell chemistries stand out with their mean absolute percentage errors of 0.0035 and 0.0057 respectively. Also, algorithm fitting performance is relatively better for these chemistries at 100% state of charge and 60°C temperature compared to ANN results. ANN algorithm predicts with mean absolute error of approximately 0.0472 overall and 0.0238 and 0.03825 for Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. The fitting performance of ANN for Nickle Manganese Cobalt Oxide at 100% state of charge and 60°C temperature is especially poor compared to XGBoost. Conclusions: For an electric vehicle battery calendar ageing prediction application, XGBoost can establish itself as the primary choice more easily compared to ANN. The reason is XGBoost's error rates and fitting performance are more usable for such application especially for Nickel Cobalt Aluminum Oxide and Nickel Manganese Cobalt Oxide chemistries, which are amongst the most demanded cell chemistries for electric vehicle battery packs.

Estimation of reference evapotranspiration using artificial neural network models for semi-arid region of Haryana

The study was conducted to evaluate performance of artificial neural network (ANN) models for estimating reference evapotranspiration (ET0) for semi-arid region of Haryana state. Ten years (2011-2020) daily weather data of maximum and minimum temperature, relative humidity, wind speed and sun shine hours was collected from the meteorological observatory at CCS HAU, Hisar. Multilayer perceptron feed forward back propagation ANN models were evaluated for different training algorithms (10), number of hidden layers (1-3) and number of neurons in hidden layers (1-30). Training algorithms compared in the study were heuristic techniques (GDA, GDX, RP), conjugate gradient (CGF, CGP, CGB, SCG), quasi-Newton (BFG, OSS) and Levenberg-Marquardt (LM). Results were compared against standard FAO Penman-Monteith method. The study revealed that best performance for ANN was found with LM algorithm in single layer of 13 neurons exhibiting RMSE, R, ME and RPD values 0.306, 0.986, 0.976 and 6.63, respectively. ANN models showed good performance in prediction of reference evapotranspiration.

ANN based surrogate model for key Physico-chemical effects of cavitation

Intense and localised physico-chemical effects realised by cavitation such as generation of hydroxyl radicals, high-speed jets, and very high energy dissipation rates are being harnessed for a wide range of applications from emulsions, crystallisation, reactions to water treatment and waste valorisation. Single cavity models are typically used to quantitatively estimate such localised effects of cavity collapse. However, these models demand significant computing resources for resolving fast dynamics and therefore are very difficult, if not impossible, to integrate with CFD based cavitation device or reactor scale models. This severely limits the utility of device/ reactor scale models in simulating key applications of interest. In this work, we present, for the first time, artificial neural network (ANN) based surrogate models which accurately represent complex physico-chemical effects of cavity collapse. Recently developed cavity dynamics model was used for generating training data set encompassing both acoustic and hydrodynamic cavitation. Appropriate methodology for training ANN was developed. A shallow three hidden layer dense ANN was found to be more effective for estimating three main effects of cavity collapse: jet velocity, •OH generation and localised energy dissipation rate. The performance of trained ANN was then evaluated by comparing the predictions with the totally unseen data obtained from the cavity dynamics model. The developed ANN was shown to simulate unseen data very well not just within the range of training data (interpolation) but also beyond (extrapolation). Algebraic equations representing ANN are included to facilitate incorporation in device/ reactor scale CFD models. The presented methodology and results will be useful for developing high-fidelity CFD models of cavitation devices/ reactors based on key physico-chemical effects of cavity collapse.

A new hybrid meta-heuristic optimization method for predicting UTS for FSW of Al/Cu dissimilar materials

Joining of aluminum (Al) and copper (Cu) materials used in aerospace, automotive, chemistry, nuclear, electrical-electronic and energy sector with traditional fusion welding methods has some negative consequences. One of the major negativities is the serious decrease in the mechanical properties of the materials as a result of the high temperature that occurs during welding. Therefore, Friction Stir Welding (FSW) is of great importance in joining materials which are dissimilar and have low melting temperatures. In this study, a hybrid artificial neural network (ANN) model has been developed to predict the relationship between FSW parameters and ultimate tensile strength and to increase the prediction performance of ANN using heuristic algorithms by welding Al and Cu plates with FSW. The used hybrid approach consists of ANN and Particle Swarm Optimization/Teaching Learning Based Optimization techniques. The welding applications conducted with three different welding parameters. These parameters, rotation speed (525, 1025, 1525 rpm), welding speed (50, 75, 100 mm/min) and tool offset (0–0.75–1.5 mm) were determined as input parameters of the developed ANN model. Tensile and microhardness tests were applied to determine the mechanical properties in the experimental studies after welding process. In addition, Scanning Electron Microscope analysis and X-ray diffraction were used for microstructure and characterization of the fracture surface and the results obtained were interpreted and examined.

Dimensional Analysis in Error Reduction for Prediction of Nucleate Boiling Heat Flux by Artificial Neural Networks for Limited Dataset

Abstract In this work, artificial neural networks (ANNs) is used to predict nucleate boiling heat flux by learning from a dataset of twelve experimental parameters across 231 independent samples. An approach to reduce the number of parameters involved and to increase model accuracy is proposed and implemented. The proposed approach consists of two steps. In the first step, a feature importance study is performed to determine the most significant parameters. Only important features are used in the second step. In the second step, dimensional analysis is performed on these important parameters. Neural network analysis is then conducted based on dimensionless parameters. The results indicate that the proposed feature importance study and dimensional analysis can significantly improve the ANNs performance. It also show that model errors based on the reduced dataset are considerably lower than those based on the initial dataset. The study based on other machine learning models also shows that the reduced dataset generate better results. The results conclude that ANNs outperform other machine learning algorithms and outperform a well-known boiling correlation equation. Additionally, the feature importance study concludes that wall superheat, gravity and liquid subcooling are the three most significant parameters in the prediction of heat flux for nucleate boiling. Novel results quantifying parameter significance in surface tension dominated (SDB) and buoyancy dominated (BDB) boiling regimes have been reported. The results show that surface tension and liquid subcooling are the most significant parameters in SDB regime with a combined contribution percentage of 60%, while wall superheat and gravity are the most significant parameters in BDB regime with a combined contribution percentage of 70%.

Performance of ANN in predicting the depth to width ratio and tensile strength of UNS S32750 laser weld joints

The effectiveness of the pulsed mode laser weld joint is characterized by the supply of optical energy to the interface. The laser welding process parameters, such as laser power, pulse duration, travel speed and focus, determine the efficacy of the weld, which is defined by full penetration and free of pores and defects. However, expressing the relationship between various process parameters and mechanical strength is intricate due to the prevalence of non-linear relationship. The development of computational approaches aids in optimizing the laser welding parameters and reducing trial and error. Hence, the artificial neural network (ANN) model is developed, in a python environment, to predict the optimum process parameter values for a desirable depth to width ratio and maximum tensile strength of UNS S32750 laser butt weld joints. The developed model was assessed by experiments, not utilized for training. The ANN model predicts the depth to width ratio and tensile strength of the weld joints with an accuracy of 90% and less than 10% divergence from the experimental result. Furthermore, for the process, parametric conditions are: (power: 550 W, focus: –1 mm, pulse duration: 13 Hz and travel speed: 136 mm/min) to attain maximum tensile strength.

Characterization of biodiesel based on plastic pyrolysis oil (PPO) and coconut oil: Performance and emission analysis using RSM-ANN approach

The work presents comparative study of the production and characterisation of sustainable biodiesel fuel made from waste plastics and virgin coconut oil. For characterisation, various chemical tests were performed, including Fourier Transform Infrared (FTIR) spectroscopy, Thermogravimetric analysis (TGA), and Gas chromatography–mass spectrometry (GCMS). The emissions and performance of this fuel were investigated to determine whether it could be utilised in diesel engines without modification. RSM (response surface method) was used to design the experiments. ANN (artificial neural network) was used to model the relationship between the input and output parameters. It was seen that, 20 % hybrid blend with diesel showed a better output (33 %) than 10 % and 30 % blend. For 75 % of load, a value of 0.12 % (minimum) CO emission was obtained for the same blend with diesel fuel. Low proportion levels blends (10 %) have less amount of oxygen content, hence reduced in NOx (400 ppm). The ANN and RSM models were found to be fitting correctly with the experimental readings, with R2 ratios varying from 90 % to 93.5 %, respectively. The outcomes demonstrated that RSM and ANN were excellent modelling techniques with good accuracy. In addition, ANN's prediction performance (R2 = 0.9978 for BTE) was somewhat better than RSM's (R2 = 0.960 for BTE).