Cascade Forward Neural Network Research Articles

Machine learning-based model for the intelligent estimation of critical heat flux in nanofluids

The rising demand for advanced energy systems requires enhanced thermal management strategies to maximize resource utilization and productivity. This is quite an important industrial and academic trend as the efficiency of energy systems depends on the cooling systems. This study intends to address the critical need for efficient heat transfer mechanisms in industrial energy systems, particularly those relying on pool boiling conditions, by mainly focusing on Critical Heat Flux (CHF). In fact, CHF keeps a limit in thermal system design, beyond which the efficiency of the system drops. Recent research materials have highlighted nanofluids’ superior heat transfer properties over conventional pure fluids, like water, which makes them a considerable substitution for improving CHF in cooling systems. However, the broad variability in experimental outcomes challenges the development of a unified predictive model. Besides, Machine Learning (ML) based prediction has shown great accuracy for modeling of the designing parameters, including CHF. Utilizing ML algorithms—Cascade Forward Neural Network (CFNN), Extreme Gradient Boosting (XGBoost), Extra Tree, and Light Gradient Boosting Method (LightGBM)— four predictive models have been developed and the benchmark shows CFNN’s superior accuracy with an average goodness of fit of 89.32%, significantly higher than any available model in the literature. Also, the iterative stability analysis demonstrated that this model with a 0.0348 standard deviation and 0.0268 mean absolute deviation is the most stable and robust method that its performance minorly changes with input data. The novelty of the work mainly lies in the prediction of CHF with these advanced algorithm models to enhance the reliability and accuracy of CHF prediction for designing purposes, which are capable of considering many effective parameters into account with much higher accuracy than mathematical fittings. This study not only explains the complex interplay of nanofluid parameters affecting CHF but also offers practical implications for the design of more efficient thermal management systems, thereby contributing to the broader field of energy system enhancement through innovative cooling solutions.

A comprehensive machine learning-based investigation for the index-value prediction of 2G HTS coated conductor tapes

Index-value, or so-called n-value prediction is of paramount importance for understanding the superconductors’ behaviour specially when modeling of superconductors is needed. This parameter is dependent on several physical quantities including temperature, the magnetic field’s density and orientation, and affects the behaviour of high-temperature superconducting devices made out of coated conductors in terms of losses and quench propagation. In this paper, a comprehensive analysis of many machine learning (ML) methods for estimating the n-value has been carried out. The results demonstrated that cascade forward neural network (CFNN) excels in this scope. Despite needing considerably higher training time when compared to the other attempted models, it performs at the highest accuracy, with 0.48 root mean squared error (RMSE) and 99.72% Pearson coefficient for goodness of fit (R-squared). In contrast, the rigid regression method had the worst predictions with 4.92 RMSE and 37.29% R-squared. Also, random forest, boosting methods, and simple feed forward neural network can be considered as a middle accuracy model with faster training time than CFNN. The findings of this study not only advance modeling of superconductors but also pave the way for applications and further research on ML plug-and-play codes for superconducting studies including modeling of superconducting devices.

A hybrid approach to soft sensor development for distillation-in-series plant under input data low variability

This paper presents a hybrid approach for integrating fundamental process knowledge with measurement data to soft sensor (SS) development with improved estimation capability. Measurement data from sensors are collected and used as inputs for a first-principles model to emulate the data close to restrictions of the operating regulations, thus addressing a low variability problem of the inputs. Next, variables from measurement data and results of the first-principles modeling are combined to extend the training dataset for SSs, which become of a hybrid type in nature. To improve an estimation capability, a cascade-forward neural network and algorithm for alternating conditional expectation for nonparametric SS development was used. It was shown that the estimation capabilities of the developed SS can be improved by extending the training dataset with first-principles model data approximating the upper and lower limits of the process regime, the size of which in total does not exceed 21% of industrial data alone. As a result, the designed hybrid SS demonstrates a better efficacy in predicting quality index of the targeted distillation product with significantly reduced mean absolute error.

Artificial intelligence-based surrogate model for computation of the electric field of high voltage transmission line ceramic insulator with corona ring

Purpose The ionization of the air surrounding the phase conductor in high-voltage transmission lines results in a phenomenon known as the Corona effect. To avoid this, Corona rings are used to dampen the electric field imposed on the insulator. The purpose of this study is to present a fast and intelligent surrogate model for determination of the electric field imposed on the surface of a 120 kV composite insulator, in presence of the Corona ring. Design/methodology/approach Usually, the structural design parameters of the Corona ring are selected through an optimization procedure combined with some numerical simulations such as finite element method (FEM). These methods are slow and computationally expensive and thus, extremely reducing the speed of optimization problems. In this paper, a novel surrogate model was proposed that could calculate the maximum electric field imposed on a ceramic insulator in a 120 kV line. The surrogate model was created based on the different scenarios of height, radius and inner radius of the Corona ring, as the inputs of the model, while the maximum electric field on the body of the insulator was considered as the output. Findings The proposed model was based on artificial intelligence techniques that have high accuracy and low computational time. Three methods were used here to develop the AI-based surrogate model, namely, Cascade forward neural network (CFNN), support vector regression and K-nearest neighbors regression. The results indicated that the CFNN has the highest accuracy among these methods with 99.81% R-squared and only 0.045468 root mean squared error while the testing time is less than 10 ms. Originality/value To the best of the authors’ knowledge, for the first time, a surrogate method is proposed for the prediction of the maximum electric field imposed on the high voltage insulators in the presence Corona ring which is faster than any conventional finite element method.

Translating Sumerian Symbols into French Letters

Sumerian symbols, which look like cuneiform letters, were used in a very old writing style. In this paper, a new method to translate the Sumerian symbols into French letters is introduced. It is based on the Cascade-Forward Neural Network (CFNN). The CFNN is exploited and adapted for the translation issue. It accepts a cuneiform letter image as an input and produces an appropriate output that refers to the translated french letter. Reasonable image augmentations are employed. These augmentations are for the: left direction rotations, right direction rotations and multiple translation directions (to the left, right, bottom and top). Total of 780 images are utilized for rotations and 338 images are collected for translations. This work can successfully attain the performance of 100%.

Towards Reliable Barrier Systems: A Constrained XGBoost Model Coupled with Gray Wolf Optimization for Maximum Swelling Pressure of Bentonite

Bentonite and bentonite mixtures are used as buffer material for deep geological radioactive waste repositories. The proper determination of bentonite maximum swelling pressure is important as it influences the long-term stability and sealing characteristics of the barrier system. We developed a constrained machine learning model based on extreme gradient boosting (XGBoost) algorithm tuned with grey wolf optimization (GWO) to determine the maximum swelling pressure of bentonite and bentonite mixtures. We integrated a penalty term into the XGBoost algorithm’s loss function to constrain the model during the training phase ensuring meaningful predictions. To this end, we compiled a dataset containing 305 experimental data of bentonite maximum swelling pressure and other relevant soil properties including montmorillonite content, liquid limit, plastic limit, plasticity index, initial water content, and soil dry density to develop the model. The performance of the hybrid GWO-XGBoost model was benchmarked against feed-forward and cascade-forward neural network models. The GWO-XGBoost model showed a better performance in estimating the experimental values comparing to the other models, thereby demonstrating its effectiveness and reliability in determining the maximum swelling pressure of bentonite and bentonite mixtures.

Toward Estimating CO2 Solubility in Pure Water and Brine Using Cascade Forward Neural Network and Generalized Regression Neural Network: Application to CO2 Dissolution Trapping in Saline Aquifers.

Predicting carbon dioxide (CO2) solubility in water and brine is crucial for understanding carbon capture and storage (CCS) processes. Accurate solubility predictions inform the feasibility and effectiveness of CO2 dissolution trapping, a key mechanism in carbon sequestration in saline aquifers. In this work, a comprehensive data set comprising 1278 experimental solubility data points for CO2-brine systems was assembled, encompassing diverse operating conditions. These data encompassed brines containing six different salts: NaCl, KCl, NaHCO3, CaCl2, MgCl2, and Na2SO4. Also, this databank encompassed temperature spanning from 273.15 to 453.15 K and a pressure range spanning 0.06-100 MPa. To model this solubility databank, cascade forward neural network (CFNN) and generalized regression neural network (GRNN) were employed. Furthermore, three optimization algorithms, namely, Bayesian Regularization (BR), Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton, and Levenberg-Marquardt (LM), were applied to enhance the performance of the CFNN models. The CFNN-LM model showcased average absolute percent relative error (AAPRE) values of 5.37% for the overall data set, 5.26% for the training subset, and 5.85% for the testing subset. Overall, the CFNN-LM model stands out as the most accurate among the models crafted in this study, boasting the highest overall R2 value of 0.9949 among the other models. Based on sensitivity analysis, pressure exerts the most significant influence and stands as the sole parameter with a positive impact on CO2 solubility in brine. Conversely, temperature and the concentration of all six salts considered in the model exhibited a negative impact. All salts exert a negative impact on CO2 solubility due to their salting-out effect, with varying degrees of influence. The salting-out effects of the salts can be ranked as follows: from the most pronounced to the least: MgCl2 > CaCl2 > NaCl > KCl > NaHCO3 > Na2SO4. By employing the leverage approach, only a few instances of potential suspected and out-of-leverage data were found. The relatively low count of identified potential suspected and out-of-leverage data, given the expansive solubility database, underscores the reliability and accuracy of both the data set and the CFNN-LM model's performance in this survey.

Prediction of the optimal hybrid train trajectory by using artificial neural network models

AbstractThis paper presents the development and validation of two artificial neural networks (ANN) models, utilising time and power‐based architectures, to accurately predict key parameters of a hydrogen hybrid train profile and its optimal trajectory. The research employs a hybrid train simulator (HTS) to authenticate the ANN models, which were trained using simulated trajectories from five unique hybrid trains on a designated route. The models’ performance was evaluated by computing the mean square normalisation error and mean absolute performance error, while the output's reliability was confirmed through the HTS. The results indicate that both ANN models proficiently predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade‐forward neural network (CFNN) topology in the time‐based architecture surpasses the feed‐forward neural network (FFNN) topology concerning mean squared error (MSE) and maximum error in the power‐based architecture. Specifically, the CFNN topology within the time‐based structure exhibits a slightly lower MSE and maximum error than its power‐based counterpart. Additionally, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time‐based architecture exhibits lower differences (0.18% and 0.85%) compared to the power‐based architecture (0.46% and 0.92%).

Retracted: Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625

Retracted: Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625

Predicting the solubility of hydrogen in hydrocarbon fractions: Advanced data-driven machine learning approach and equation of state

BackgroundHydrogen is a free carbon source for energy that attracts massive interest to be utilized during energy transition period in the near future. Molecular hydrogen soon will be a key component in fuels productions. It can be produced through catalytic steam and natural gas. For such processes the accurate estimation of solubility of hydrogen in many alkanes’ solution can have a major impact on the design and optimization of such petrochemical processes. MethodsIn this study, advanced data-driven scheme consisting in a committee machine intelligent system (CMIS) model was proposed for predicting the solubility of hydrogen in many hydrocarbon liquids raging from light n-alkanes C1 to heavy n-alkanes of C46. The CMIS was gained by combining multilayer perceptron (MLP) and cascaded forward neural network (CFNN) beneath a sole model using gene expression programming (GEP). A wide range database of hydrogen solubility in a binary system of hydrogen and various n-alkanes types was considered for developing the intelligent model. Besides, the reliability of the physics-based models, namely the Peng–Robinson equations of state (PR EOS) was checked for predicting the solubility of H2 in n-alkanes. Significant findingsThe main results demonstrated that the developed CMIS scheme can estimate the solubility of hydrogen in n-alkanes with a quite low computational cost and excellent performance, where the yielded overall RMSE and R2 values were 0.0033 and 0.9972, respectively. Besides, the established machine learning based model is capable of providing a good match against a wide range of experiment data. Furthermore, it is found that the implemented CMIS paradigm is superior to the existing intelligent and physics-based models in terms of accuracy. Lastly, the result of this work is vital for increasing the accuracy of hydrogen solubility in various hydrocarbon solvents required for process optimization of the hydrogen production units.

A machine learning approach combined with wavelet analysis for automatic detection of Pc5 geomagnetic pulsations observed at geostationary orbits

Pc5 geomagnetic pulsations are ultra-low frequency (ULF) waves whose period lies within 150–600 s. Their detection is crucial for accurate space weather modelling, monitoring, and analysis. Identification of these pulsations using manual approaches is difficult and time-consuming because of the smaller magnitudes and the limited durations within which they occur. To overcome this challenge, we propose a robust Cascade Forward Neural Network (CFNN) model combined with the wavelet technique for automatically detecting Pc5 events from satellite datasets observed at geostationary orbits. The dataset used in this study is the magnetic field vector measurements retrieved from the Geostationary Operational Environmental Satellite-10 (GOES-10) from 2000 to 2009. Pc5 geomagnetic pulsations were extracted from the toroidal component of the field perturbation using a bandpass Butterworth filter. Continuous wavelet transform (CWT) analysis using the mother Morlet wavelet was utilized to validate the integrity of the extracted signal in the time–frequency domain. The extracted Pc5 signal was decomposed into details and approximations using Daubechies wavelet transform to separate the intelligible signal from the incoherent noise that left their trace on the magnetic field time series. The detail signal was subjected to denoising using the heuristic Stein Unbiased Risk Estimate (SURE) approach with soft thresholding to obtain the denoised Pc5 events. The denoised Pc5 events were utilized as the target in the machine learning whereas the inputs were obtained from 5-dimensional space transformation of the toroidal component of the time series. The developed algorithm performed well and demonstrated a detection accuracy of 80 % and a Root Mean Square Error (RMSE) of 0.13 nT indicating a high performance in detecting Pc5 events. For effective validation of the model, Pc5 events detected by our model were correlated with the Kp index and the amplitude of the Pc5 events observed at geostationary orbit. A good correlation was obtained in both cases, making our model a practical and preferred choice for Pc5 pulsation detection in contrast to conventional frequency analysis tools which take much time for signal processing on large data sets.

A New Intelligent Estimation Method Based on the Cascade-Forward Neural Network for the Electric and Magnetic Fields in the Vicinity of the High Voltage Overhead Transmission Lines

The evaluation and estimation of the electric and magnetic field (EMF) intensity in the vicinity of overhead transmission lines (OHTL) is of paramount importance for residents’ healthcare and industrial monitoring purposes. Using artificial intelligence (AI) techniques makes researchers able to estimate EMF with extremely high accuracy in a significantly short time. In this paper, two models based on the Artificial Neural Network (ANN) have been developed for estimating electric and magnetic fields, i.e., feed-forward neural network (FFNN) and cascade-forward neural network (CFNN). By performing the sensitivity analysis on controlling/hyper-parameters of these two ANN models, the best setup resulting in the highest possible accuracy considering their response time has been chosen. Overall, the CFNN achieved a significant 56% reduction in Root Mean Squared Error (RMSE) for the electric field and a 5% reduction for the magnetic field, compared to the FFNN. This indicates that the CFNN model provided more accurate predictions, particularly for the electric field than the proposed methods in other recent works, making it a promising choice for this application. When the model is trained, it will be tested by a different dataset. Then, the accuracy and response time of the model for new data points of that layout will be evaluated through this process. The model can predict the fields with an accuracy near 99.999% of the actual values in times under 10 ms. Also, the results of sensitivity analysis indicated that the CFNN models with triple and double hidden layers are the best options for the electric and magnetic field estimation, respectively.

Optimized data-driven approach for remaining useful life prediction of Lithium-ion batteries based on sliding window and systematic sampling

The prediction of remaining useful life (RUL) in lithium-ion batteries (LIB) serves as a critical health index for evaluating battery parameters, including efficiency, robustness, and accuracy. Existing RUL prediction techniques encounter challenges related to data extraction based on battery parameters and data samples, as well as the selection of appropriate model hyperparameters to achieve accurate outcomes. To address these issues, this article presents a novel framework for RUL prediction using a hybrid optimized data-driven approach, which combines a cascaded forward neural network (CFNN) with the innovative jellyfish optimization (JFO) technique. Initially, a comprehensive 46-data feature-based framework is developed for training the proposed hybrid JFO-CFNN model, employing a mathematical systematic sampling (SS) method. The SS method facilitates the selection of 15 relevant data features, from the LIB parameters such as temperature, current, and voltage, from each charging cycle. Additionally, the developed framework mitigates the capacity regeneration effect present with the capacity degradation profile by employing the overlapping sliding window (OSW) technique. Moreover, the CFNN model parameters, such as hidden neurons and learning rate, are optimized using the newly introduced meta-heuristic JFO technique. To validate the performance of the proposed hybrid JFO-CFNN model, comparisons are made with other models such as the JFO-optimized artificial neural network (ANN) and deep neural network (DNN) model as well as with another battery dataset obtained from the Massachusetts Institute of Technology (MIT) Stanford battery database. The outcomes based on the NASA battery suggested the average RMSE of the proposed JFO-CFNN model below 0.1 and 0.15 for NASA and MIT Stanford battery datasets. The experimental results demonstrate the high prediction accuracy and applicability of the proposed hybrid JFO-CFNN model.

Optimal artificial neural network configurations for hourly solar irradiation estimation

<span lang="EN-US">Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.</span>

Scrutinizing the Performances of Hybrid ANN Models for Forecasting Condition of Bridges

AbstractBridges all over the world are vulnerable to severe deterioration agents meanwhile their maintenance budgets are being tightened. This state of affairs necessitates the establishment of an autonomous deterioration model to predict the performance condition of bridges. This research paper explores the implementation of a set of intelligent data driven models to analyze the future condition ratings of bridge decks. These models comprise support vector machines, Gaussian process regression, regression tree, back propagation artificial neural network, Elman recurrent neural network, cascade forward neural network, long short‐term memory network and deep convolutional neural network. The performance comparison is carried out using five evaluation metrics of root mean squared percentage error, mean absolute percentage error, root mean squared error mean absolute error and relative absolute error. The models herein are developed and validated using the structural deterioration information retrieved from the National Bridge Inventory (NBI). It can be argued that the developed deterioration model could be implemented by departments of transportation to analyze and monitor the performance condition behavior of bridge components over their useful lifetime.

Binary Hunter-Prey Optimization with Machine Learning-Based Cybersecurity Solution on Internet of Things Environment.

Internet of Things (IoT) enables day-to-day objects to connect with the Internet and transmit and receive data for meaningful purposes. Recently, IoT has resulted in many revolutions in all sectors. Nonetheless, security risks to IoT networks and devices are persistently disruptive due to the growth of Internet technology. Phishing becomes a common threat to Internet users, where the attacker aims to fraudulently extract confidential data of the system or user by using websites, fictitious emails, etc. Due to the dramatic growth in IoT devices, hackers target IoT gadgets, including smart cars, security cameras, and so on, and perpetrate phishing attacks to gain control over the vulnerable device for malicious purposes. These scams have been increasing and advancing over the last few years. To resolve these problems, this paper presents a binary Hunter-prey optimization with a machine learning-based phishing attack detection (BHPO-MLPAD) method in the IoT environment. The BHPO-MLPAD technique can find phishing attacks through feature selection and classification. In the presented BHPO-MLPAD technique, the BHPO algorithm primarily chooses an optimal subset of features. The cascaded forward neural network (CFNN) model is employed for phishing attack detection. To adjust the parameter values of the CFNN model, the variable step fruit fly optimization (VFFO) algorithm is utilized. The performance assessment of the BHPO-MLPAD method takes place on the benchmark dataset. The results inferred the betterment of the BHPO-MLPAD technique over compared approaches in different evaluation measures.

Brent Crude Oil Price Forecasting using the Cascade Forward Neural Network

Crude oil is one of the most traded non-food products or commodities in the world. In Indonesia, crude oil will still be a contributor to the gross domestic product in 2021. The excessive consumption of fuel oil (BBM) in Indonesia has resulted in a scarcity of crude oil, especially diesel. Forecasting the price of Brent crude oil is an important effort to anticipate fluctuations in the price of fuel oil. The cascade-forward neural network (CFNN) method is proposed to forecast fuel prices because of its superiority in fluctuating data types. The data used in this research is the price of Brent crude oil in the period January 2008 to December 2022. The CFNN method will be evaluated using the mean absolute percentage error (MAPE) to choose the best architectural model. The best Architectural Model is used to predict the next 12 months. After 10 architectural model trials, 2-6-1 became the best model with a MAPE data training value of 6.3473% and MAPE data testing of 9.4689%. Forecasting the results for Brent crude oil for the next 12 months tends to experience a downward trend until December 2023.

CFNN for Identifying Poisonous Plants

Identification of poisonous plants is a hard challenge for researchers because of the great similarity between poisonous and non- poisonous plants. Traditional methods to identify poisonous plant can be tiresome, therefore, automated poisonous plants identification system is needed. In this work, cascade forward neural network framework is proposed to identify poisonous plants based on their leaves. The proposed system was evaluated on both (poisonous leaves/non-poisonous leaves) which are collected using smart phone and internet. Combination of shape features and statistical features are extracted from leaf then fed to cascade-forward neural network which used TRAINLM function for training. 500 samples of leaf images are used, 250 samples are poisonous, the remaining 250 samples are non-poisonous.300 samples used in training, 200 samples for testing. Our system is achieved an accuracy value of 99.5%.

A theoretical and deep learning hybrid model for predicting surface roughness of diamond-turned polycrystalline materials

Polycrystalline materials are extensively employed in industry. Its surface roughness significantly affects the working performance. Material defects, particularly grain boundaries, have a great impact on the achieved surface roughness of polycrystalline materials. However, it is difficult to establish a purely theoretical model for surface roughness with consideration of the grain boundary effect using conventional analytical methods. In this work, a theoretical and deep learning hybrid model for predicting the surface roughness of diamond-turned polycrystalline materials is proposed. The kinematic–dynamic roughness component in relation to the tool profile duplication effect, work material plastic side flow, relative vibration between the diamond tool and workpiece, etc, is theoretically calculated. The material-defect roughness component is modeled with a cascade forward neural network. In the neural network, the ratio of maximum undeformed chip thickness to cutting edge radius R TS, work material properties (misorientation angle θ g and grain size d g), and spindle rotation speed n s are configured as input variables. The material-defect roughness component is set as the output variable. To validate the developed model, polycrystalline copper with a gradient distribution of grains prepared by friction stir processing is machined with various processing parameters and different diamond tools. Compared with the previously developed model, obvious improvement in the prediction accuracy is observed with this hybrid prediction model. Based on this model, the influences of different factors on the surface roughness of polycrystalline materials are discussed. The influencing mechanism of the misorientation angle and grain size is quantitatively analyzed. Two fracture modes, including transcrystalline and intercrystalline fractures at different R TS values, are observed. Meanwhile, optimal processing parameters are obtained with a simulated annealing algorithm. Cutting experiments are performed with the optimal parameters, and a flat surface finish with Sa 1.314 nm is finally achieved. The developed model and corresponding new findings in this work are beneficial for accurately predicting the surface roughness of polycrystalline materials and understanding the impacting mechanism of material defects in diamond turning.

Optimized Data-Driven Models for Prediction of Flyrock due to Blasting in Surface Mines

Using explosive material to fragment rock masses is a common and economical method in surface mines. Nevertheless, this method can lead to some environmental problems in the surrounding regions. Flyrock is one of the most dangerous effects induced by blasting which needs to be estimated to reduce the potential risk of damage. In other words, the minimization of flyrock can lead to sustainability of surroundings environment in blasting sites. To this aim, the present study develops several new hybrid models for predicting flyrock. The proposed models were based on a cascaded forward neural network (CFNN) trained by the Levenberg–Marquardt algorithm (LMA), and also the combination of least squares support vector machine (LSSVM) and three optimization algorithms, i.e., gravitational search algorithm (GSA), whale optimization algorithm (WOA), and artificial bee colony (ABC). To construct the models, a database collected from three granite quarry sites, located in Malaysia, was applied. The prediction values were then checked and evaluated using some statistical criteria. The results revealed that all proposed models were acceptable in predicting the flyrock. Among them, the LSSVM-WOA was a more robust model than the others and predicted the flyrock values with a high degree of accuracy.