Feed-forward Back-propagation Artificial Neural Network Research Articles

Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models

Assessing riverine pollutant loads is a more realistic method for analysing point and non-point anthropogenic pollution sources throughout a watershed. This study compares numerous mathematical modelling strategies for estimating riverine loads based on the chosen water quality parameters: Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended Solids (SS), and Ammoniacal Nitrogen (NH3–N). A riverine load model was developed by employing various input variables including river flow and pollutant concentration values collected at several monitoring sites. Among the mathematical modelling methods employed are artificial neural networks with feed-forward backpropagation algorithms and radial basis functions. The classical multiple linear regression (MLR) statistical model was used for the comparison. Four widely used statistical performance assessment metrics were adopted to evaluate the performance of the various developed models: the root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), and coefficient of determination (R2). The considerable number of errors (with RMSE, MAE, and MRE) discovered in estimating riverine loads using the multiple linear regression (MLR) statistical model can be attributed to the nonlinear relationship between the independent variables (Q and Cx) and dependent variables (W). The feed-forward neural network model with a backpropagation algorithm and Bayesian regularisation training algorithm outperformed the radial basis neural network. This finding implies that, in addition to suspended sediment loads, riverine loads may be predicted using an artificial neural network using pollutant concentration (Cx) and river discharge (Q) as input variables. Other geographical and temporal fluctuation characteristics that may impact river water quality, on the other hand, may be incorporated as input variables to enhance riverine load prediction. Finally, riverine load analyses were successfully conducted to reduce the riverine load.

Impact of Arrhenius activation energy on magnetic nanofluid flow over a slendering stretchable sheet with nonlinear radiative heat transfer: A machine learning algorithm

Thermal systems in the contemporary world need effective cooling and heating methods. Heat transfer technology plays a vital role in various industries, including aerospace, electronic equipment, automobiles, and transportation. Considering the importance of effective cooling and heating processes, this framework explores the hydromagnetic stagnation point flow with binary chemical reactions, ohmic heating, Arrhenius activation energy, nonlinear thermal radiation, thermophoresis, and Brownian motion. At first, the flow equations are determined in a formal way as dimensional partial differential equations. Then, they are transformed into simpler, dimensionless ordinary differential equations using self-similar variables. The present study describes a novel approach to intelligent numerical computing, utilizing a Levenberg-Marquard algorithm-based MLP (Multi-Layer Perceptron) feed-forward back-propagation ANN (Artificial Neural Networks) implementation. The collection of data was conducted to facilitate the evaluation, validation, and instruction of the artificial neural network model. The utilization of appropriate self-similarity variables has facilitated the conversion of fluid transport equations into ordinary differential equations. The Runge–Kutta–Fehlberg (RKF) technique was used to solve these equations. The numerical results are presented visually for numerous constraints of the governing parameters. The increased values of the velocity ratio enhanced the fluid velocity. The nonlinear thermal radiation and temperature differential characteristics significantly raise the temperature of the fluid. Increasing the nonlinear radiation and magnetic field parameters improves the rate of heat transfer. The temperature of the fluid decreases as the size of stagnation increases. The skin friction coefficients decrease as the wall thickness and magnetic parameters increase.

Energy and economic analysis of building integrated photovoltaic thermal system: Seasonal dynamic modeling assisted with machine learning-aided method and multi-objective genetic optimization

Building integrated photovoltaic thermal (BIPV/T) systems offer a highly effective means of generating clean energy for both electricity and heating purposes in residential buildings. Hence, this article introduces a new BIPV/T system to optimally minimize the energy consumption of a household residential building. The meticulous design of the proposed BIPV/T system is accomplished through MATLAB/Simulink® dynamic modeling. Performance analysis for the BIPV/T system is performed under different seasonal conditions with in-depth techno-economic analyses to estimate the expected enhancement in the thermal, electrical, and economic performance of the system. Moreover, a sensitivity analysis is conducted to explore the impact of various factors on the energetic and economic performances of the proposed BIPV/T system. More so, the two-layer feed-forward back-propagation artificial neural network modeling is developed to accurately predict the hourly solar radiation and ambient temperature for the BIPV/T. Additionally, a multi-objective optimization using the NSGA-II method is also conducted for the minimization of the total BIPV/T plant area and maximization of the total efficiency and net thermal power of the system as well as to estimate the optimized operating conditions for input variables across different seasons within the provided ranges. The sensitivity analysis revealed that higher solar flux levels lead to increased electric output power of the BIPV/T plant, but total efficiency decreases due to higher thermal losses. Moreover, the proposed NSGA-II shows a feasible method to attain a maximum net thermal power and optimal total efficiency of 5320 W and 63% with a minimal total plant area of 32.89 m2 that attained a very low deviation index from the ideal solution. The levelised cost of electricity is obtained as 0.10 $/kWh under the optimal conditions. Thus, these findings offer valuable insights into the potential of BIPV/T systems as a sustainable and efficient energy solution for residential applications.

Predictive Quality Analytics of Surface Roughness in Turning Operation Using Polynomial and Artificial Neural Network Models

The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless steel. Predictive models will make it possible to estimate the surface roughness from the mechanical properties of steel and thus support decision making about supplier selection or acceptance of a material supply. This research presents a step-by-step decision-making procedure, which enables the trained staff to make quick decisions based on commonly available information in the Mill Test Certificate (MTC). A new multivariate second-order polynomial model and feedforward backpropagation artificial neural network (ANN) models have been developed using input variables from the MTC: Tensile Strength, Yield Strength, Elongation, and Hardness. Models were used to enhance the methodological robustness in formulating the decision if the predicted surface roughness is outside the required range, even before accepting the delivery. Both models can accurately predict surface roughness, while the ANN model is more accurate than the polynomial model; however, the predictive model is sensitive to the accuracy of the input data, and the model’s prediction is valid only under precisely defined conditions.

Peristaltic transport of Sutterby nanofluid flow in an inclined tapered channel with an artificial neural network model and biomedical engineering application

Modern energy systems are finding new applications for magnetohydrodynamic rheological bio-inspired pumping systems. The incorporation of the electrically conductive qualities of flowing liquids into the biological geometries, rheological behavior, and propulsion processes of these systems was a significant effort. Additional enhancements to transport properties are possible with the use of nanofluids. Due to their several applications in physiology and industry, including urine dynamics, chyme migration in the gastrointestinal system, and the hemodynamics of tiny blood arteries. Peristaltic processes also move spermatozoa in the human reproductive system and embryos in the uterus. The present research examines heat transport in a two-dimensional deformable channel containing magnetic viscoelastic nanofluids by considering all of these factors concurrently, which is vulnerable to peristaltic waves and hall current under ion slip and other situations. Nanofluid rheology makes use of the Sutterby fluid model, while nanoscale effects are modeled using the Buongiorno model. The current study introduces an innovative numerical computing solver utilizing a Multilayer Perceptron feed-forward back-propagation artificial neural network (ANN) with the Levenberg–Marquardt algorithm. Data were collected for testing, certifying, and training the ANN model. In order to make the dimensional PDEs dimensionless, the non-similar variables are employed and calculated by the Homotopy perturbation technique. The effects of developing parameters such as Sutterby fluid parameter, Froude number, thermophoresis, ion-slip parameter, Brownian motion, radiation, Eckert number, and Hall parameter on velocity, temperature, and concentration are demonstrated. The machine learning model chooses data, builds and trains a network, and subsequently assesses its performance using the mean square error metric. Current results declare that the improving Reynolds number tends to increase the pressure rise. Improving the Hall parameter is shown to result in a decrease in velocity. When raising a fluid's parameter, the temperature profile rises.

Artificial neural network-empowered projected future rainfall intensity-duration-frequency curves under changing climate

With the increasing trend of greenhouse gases in the atmosphere, by 2052 the temperature is expected to rise by 1.5 °C from the Pre-industrial Period, affecting future extreme rainfall events. This necessitates quantifying extreme hydrologic events to plan and design hydrologic and hydraulic structures using rainfall Intensity-Duration-Frequency (IDF) curves to adapt to future climate scenarios. This study developed future projected IDF curves for the Southeast United States using disaggregated sub-hourly (15-, 30-, and 45-min) monthly maximum rainfall from 2030 to 2059 using five climate models under the Representative Concentration Pathway 8.5 scenario. A computationally efficient feed-forward back-propagation Artificial Neural Network (ANN)-based approach was found to be significantly superior for disaggregating rainfall to a stochastic model with an average Nash–Sutcliffe efficiency (NSE) ranging from 0.67 to 0.84. The study found that there is an increasing rate of future projected annual maximum rainfall intensities in the range of 7% to 36% with reference to the historical period. The spatial variation in future projected extreme rainfall depths showed that the Gulf-Atlantic coast and the Appalachian Mountains are expected to receive more extreme rainfalls.

Appraisal of rock dynamic, physical, and mechanical properties and forecasting shear wave velocity using machine learning and statistical methods

Direct determination of shear wave velocity requires time, cost, and high accuracy due to the complexity of the rock texture. In current research statistical and intelligent approaches have been used to predict the shear wave velocity of rock samples. Also, a new correlation between dynamic and static rock properties was established and the shear wave velocity was estimated based on index tests using Gaussian process regression, multivariate linear regression, feedforward back-propagation artificial neural network, and K-nearest neighbor methods. In total, 120 data related to limestone and sandstone samples of the main projects were used for modeling. Water absorption, compressional wave velocity, and density were used as inputs. The outcomes revealed that the PW/SW ratio is equal to 1.69. Various statistics were used to check the method results. The statistical results showed that it is possible to forecast Ed, and SW with high accuracy. Also, the precision of the GPR was higher than the FBP-ANN, statistical analysis, and KNN. Estimation of SW by GPR showed R of 0.992, and RMSE of 0.06, respectively. These four methods were able to estimate the SW with a mean variation percentage of +0.19%. It's important to consider that these models are best suited for predicting SW when the predictor indicators fall within the same range as this study.

Artificial neural network model of non-Darcy MHD Sutterby hybrid nanofluid flow over a curved permeable surface: Solar energy applications

The conversion of solar radiation to thermal energy has recently much interest as the requirement for renewable heat and power grows. Due to their enhanced ability to promote heat transmission, nanofluids can significantly improve solar-thermal systems' efficiency. This section aims to study the heat transfer behavior of the Sutterby hybrid nanofluid flow of magnetohydrodynamics in the presence of a non-uniform heat source/sink and linear thermal radiation over a non-Darcy curved permeable surface. A novel implementation of an intelligent numerical computing solver based on multi-layer perceptron (MLP) feed-forward back-propagation artificial neural network (ANN) with the Levenberg-Marquard algorithm is provided in the current study. Data were gathered for the ANN model's testing, certification, and training. Established mathematical equations are nonlinear, which are resolved for velocity, the temperature in addition to the skin friction coefficient, and the rate of heat transfer by using the bvp4c with MATLAB solver. The ANN model selects data, constructs and trains a network, then evaluates its efficacy via mean square error. Graphs illustrate the impact of a wide range of physical factors on variables, including pressure, velocity, and temperature. In the entire study, the thermal energy improved by the SiO2 (silicon dioxide) - Au (gold) hybrid nanofluid than the SiO2-TiO2 (titanium dioxide) hybrid nanofluid. The higher internal heat generation/absorption parameter values increase the temperature.

Exploring the Influence of Induced Magnetic Fields and Double-Diffusive Convection on Carreau Nanofluid Flow through Diverse Geometries: A Comparative Study Using Numerical and ANN Approaches

This current investigation aims to explore the significance of induced magnetic fields and double-diffusive convection in the radiative flow of Carreau nanofluid through three distinct geometries. To simplify the fluid transport equations, appropriate self-similarity variables were employed, converting them into ordinary differential equations. These equations were subsequently solved using the Runge–Kutta–Fehlberg (RKF) method. Through graphical representations like graphs and tables, the study demonstrates how various dynamic factors influence the fluid’s transport characteristics. Additionally, the artificial neural network (ANN) approach is considered an alternative method to handle fluid flow issues, significantly reducing processing time. In this study, a novel intelligent numerical computing approach was adopted, implementing a Levenberg–Marquardt algorithm-based MLP feed-forward back-propagation ANN. Data collection was conducted to evaluate, validate, and guide the artificial neural network model. Throughout all the investigated geometries, both velocity and induced magnetic profiles exhibit a declining trend for higher values of the magnetic parameter. An increase in the Dufour number corresponds to a rise in the nanofluid temperature. The concentration of nanofluid increases with higher values of the Soret number. Similarly, the nanofluid velocity increases with higher velocity slip parameter values, while the fluid temperature exhibits opposite behavior, decreasing with increasing velocity slip parameter values.

A 3-layered feedforward back-propagation ANN-based SVPWM control for neutral point clamped converter for PV grid integration

Most of the power electronic converters based on the devices such as Silicon Controlled Rectifiers (SCRs) have been broadly utilized in home, business, and modern use in recent years. Despite their many benefits, these power electronic converters have major issues such as pulling harmonic current and the reactive part of the current from the supply, as well as having a highly nonlinear characteristic. The harmonics produced by the current supplied by these nonlinear elements cause voltage distortion at the common coupling point, which is causing problems for the functioning of number of sensitive instruments and other consumer appliances. Artificial Neural Networks (ANN) are a type of Artificial Intelligence (AI) approach that has been applied to improve the efficiency and regulation of the converter. In order to avoid the need for a Digital Signal Processors (DSP) by avoiding the online timing computations for various voltage space vectors in various regions and sectors and produce higher pulse resolution, an ANN-based space vector pulse width modulation (SVPWM) technique is proposed in this paper. The analysis of a 3-layered feedforward back propagation ANN algorithm based SVPWM control for NPC converter used to integrate PV source to grid has been evaluated and found to be better as compared to traditional techniques.

Experimental data-driven and phenomenological modeling approaches targeting the enhancement of CaTiO3 photocatalytic efficiency

Optimization based on mathematical models has received growing attention in materials science. The first part of the work aims to optimize the photocatalytic activity of CaTiO3 for rhodamine B (RhB) degradation under UV-A irradiation, based on two developed mathematical models. Thirty hydrothermal syntheses of CaTiO3 were carried out according to the Box-Behnken design, considering synthesis temperature (X1), duration (X2), and concentration of shaping agent (X3) as input variables for two different Ca2+ sources: Ca(NO3)2 and CaCl2 (X4). The conversion of the studied pollutant after 4 h was situated in the range of 20–80%. Second-order regression and feedforward backpropagation artificial neural network models were developed, considering the synthesis conditions (X1, X2, X3, X4) as input and the conversion as output variables. The proposed model-based methodology for the optimization of CaTiO3 photocatalytic efficiency finally directed to the experimentally attained value of 96% for 200 °C (X1, opt), 23.17 h (X2, opt), 0.67 M (X3, opt), CaCl2 (X4, opt). Furthermore, in the second part of the study, the morphological, structural, textural, and optical properties of selected CaTiO3 samples were investigated via scanning electron microscopy, X-ray diffractometry, N2 sorption, and diffuse reflectance spectroscopy. Finally, the kinetic parameters for adsorption (kads: 0.10–0.67 m·h−1), desorption (kdes: 79–150 mmol·m−2·h−1), degradation (kdegr: 0.001–0.010 mmol·m−2(1−α)·W−α·h−1), and intensity exponent (α: 0.54–0.55) were fitted using an optimization procedure, considering the experimentally determined and model-predicted apparent reaction rate constants. The obtained kinetic parameters were correlated with the specific surface area of the catalysts and the conversion of RhB.

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.

Ku-Band SIW Filter with High Fractional Bandwidth Optimized Using Feed Forward Back Propagation ANN

An Artificial Neural Network (ANN) based Substrate Integrated Waveguide Band Pass Filter (SIW BPF) is proposed in this paper. A Feed Forward Back Propagation Neural Network (FF-BP NN) is utilized to optimize the filter dimensions. The Gradient Descent with Momentum and Adaptive Learning Rate algorithm is used to train the network at a learning rate of 0.01. The high pass characteristics of the SIW is converted into band pass by incorporating U slots on its top plane. Defected Ground Structure (DGS) is utilized in the bottom plane to improve the impedance matching. To validate, the prototype is fabricated using RT/Duroid 5880 and tested. The proposed filter has a center frequency at 14.68 GHz with a wide pass band from 12.92 to 16.43 GHz with a 3-dB Fractional Bandwidth (FBW) of 24%, return loss more than 20 dB and insertion loss of about 1.9 dB within the pass band. The filter has a small dimension of about 0.63 , where λ g is the guided wavelength at the center frequency. This filter offers wide passband, smaller size, low insertion loss, good return loss and it is useful in Ku-band satellite communication applications.

Elucidating the prediction capability of neural network model for estimation of crop water stress index of rice

ABSTRACT The crop water stress index is receiving significant attention these days, especially in arid and semiarid regions, for quantifying water stress and effective irrigation scheduling. Nowadays, machine learning techniques such as neural networks are being widely used to determine CWSI. In the present study, Self-Organizing Maps (SOM) and Feed Forward-Back Propagation Artificial Neural Networks (FF-BP-ANN) are compared while determining the CWSI of rice crop. Irrigation field experiments with varying degrees of irrigation were conducted at the irrigation field laboratory of the Indian Institute of Technology, Roorkee, during the growing season of the rice crop. The CWSI of rice was computed empirically by measuring key meteorological variables (relative humidity, air temperature, and canopy temperature). The empirically computed CWSI was compared with SOM and FF-BP-ANN predicted CWSI. For the lower CWSI baseline of rice, a linear relationship between air and canopy temperature difference (Tc-Ta) and, air vapour pressure deficit (AVPD) was developed, whereas air temperature plus 3°C was taken for the upper CWSI baselines. The performance of SOM and FF-BP-ANN were compared by computing Nash–Sutcliffe efficiency (NSE), index of agreement (d), root mean squared error (RMSE), and coefficient of correlation (R2). It is found that FF-BP-ANN (R2 = 0.97, NSE = 0.92, d = 0.95, RMSE = 0.006) performs better than SOM (R2 = 0.88, NSE = 0.87, d = 0.9, RMSE = 0.075).

Evaluating Different Machine Learning Models for Runoff Modelling

Estimation and forecasting of hydrological factors are of particular importance in hydrological modelling, and surface runoff is one of the most important of these factors. Machine learning (ML) models have attracted the attention of researchers in this field. So, this article aims to evaluate several types of ML models such as autoregressive integrated moving average (ARIMA), feed forward back propagation artificial neural network (FFBP-ANN), and adaptive neuro-fuzzy inference system (ANFIS) models in order to estimate runoff values at Al-Jawadiya meteostation in the Orontes River basin in Syria. A large number of ARIMA models were built and the seasonal effect on the models also verified. After that, FFBP-ANN models were used with the change in the number of inputs, the number of hidden layers, and the number of neurons in the hidden layer. Also, a large number of FIS models have been built and artificial neural algorithms have been used in the process of model parameters optimization. The results showed a preference for artificial intelligence models in general over ARIMA models, as well as a slight preference for FFBP-ANN models over ANFIS models. This study recommends expanding the use of ML models to reach the best models for forecasting hydrological factors.

Artificial neural network based delamination prediction in composite plates using vibration signals

Dynamic loading on composite components may induce damages such as cracks, delaminations, etc. and development of an early damage detection technique for delaminations is one of the most important aspects in ensuring the integrity and safety of composite components. The presence of damages such as delaminations on the composites reduces its stiffness and further changes the dynamic behaviour of the structures. As the loss in stiffness leads to changes in the natural frequencies, mode shapes, and other aspects of the structure, vibration analysis may be the ideal technique to employ in this case. In this research work, the supervised feed-forward multilayer back-propagation Artificial Neural Network (ANN) is used to determine the position and area of delaminations in GFRP plates using changes in natural frequencies as inputs. The natural frequencies were obtained by finite element analysis and results are validated by experimentation. The findings show that the suggested technique can satisfactorily estimate the location and extent of delaminations in composite plates.

Artificial Neural Network Model for the Prediction of Thermal Conductivity of Saturated Liquid Refrigerants and n-Alkanes.

In this paper, a feed-forward back-propagation artificial neural network (ANN) is proposed to correlate and predict the thermal conductivity from the triple point temperature up to 0.98 times critical temperature (T c) for 23 refrigerants and 11 n-alkanes. It requires the temperature (T) as well as the molecular mass (M), acentric factor (ω), critical temperature, and critical pressure (P c) as input variables. The optimal ANN model is obtained by a trial-and-error procedure and consists of the input layer and the output layer together with one hidden layer with seven neurons. This ANN model can not only correlate the thermal conductivity but also accurately predict the thermal conductivity of refrigerants and n-alkanes. The correlation coefficients (R) in the training and testing phases are 0.9994 and 0.9993, respectively. Furthermore, the average absolute deviation (AAD) values are less than 1% for 14 out of 34 fluids, less than 2% for 28 fluids, and less than 4.5% for all the considered fluids.

Application of Artificial Neural Network to Predict the Properties of Permeable Concrete

The structure of permeable concrete has been the primary reason for its use in construction. Permeable concrete is composed of water, cement, aggregate, and little- to no-fines resulting in the presence of a significant number of voids. This makes permeable concrete an ideal solution to water accumulation issues as it acts as a drainage system. This study employs a feedforward backpropagation artificial neural network model that combines experimental laboratory data from previous studies with appropriate network architectures and training techniques. The purpose of the analysis is to develop a reliable functional relationship, based on water-cement ratio, aggregate-cement ratio, and density parameters, with which to estimate the compressive strength, porosity, and water permeability of permeable concrete. Multiple linear regression correlations are also established to predict and correlate these inputs and outputs. The two derived methods are then compared and discussed. The results reveal that ANN is better to anticipate the permeable concrete properties than regression analysis.

Prediction of gamma ray spectrum for 22Na source by feed forward back propagation ANN model

The radiation has been used in a variety of different fields since its discovery and thus its measurement becomes vital in these industries. Different type detector may be used to measure gamma rays depends on the purposes of measurements. Gamma ray energy spectrum is an important to determine either elemental analysing of a sample or radiation shielding purposes. On the other hand, Artificial Neural Network (ANN) may be used to predict and analysing of gamma-ray spectrum. In this study, gamma ray spectrum from 22Na source detected in NaI (Tl) detector was estimated by ANN. There have been installed ten different ANN models to find the network structure that produces the best predictive value for the gamma ray spectrum NaI (Tl) Detector. Estimation study has been continued with the ANN model with be possessed of lowest error value. ANN model was created by using energy, distance and gamma-rays energy spectrum (called Io) values. In the ANN model developed using the feed forward back propagation algorithm, were used artificial neurons two in the input layer, ten in the hidden layer and one in the output layer. For the case of present work, the experimental data was used 70% for education, 20% for validation and 10% for testing. The estimated values obtained with the ANN model were compared with the experimental results and a good correlation has been found between them (R2 = 0.99).

Peak ground acceleration prediction using artificial neural networks for Kachchh, Gujarat, India

The uncertainty in the empirical ground motion prediction models (GMMs) for any region depends on several parameters. In the present work, we apply an artificial neural network (ANN) to design a GMM of peak ground acceleration (PGA) for Kachchh, Gujarat, India, utilizing independent input parameters viz., moment magnitudes, hypocentral distances, focal depths and site proxy (in terms of average seismic shear-wave velocity from the surface to a depth of 30 m (Vs30)). The study has been performed using a PGA dataset consisting of eight engineering seismoscope records of the 2001 Mw7.7 Bhuj earthquake and 237 strong-motion records of 32 significant Bhuj aftershocks of Mw3.3–5.6 (during 2002–2008) with epicentral distances ranging from 1.0 to 288 km. We apply a feed-forward back propagation ANN method with 8 hidden nodes, which is found to be optimal for the selected PGA database and input–output mapping. The standard deviation of the error has been utilized to examine the performance of our model. We also test the ground motion predictability of our ANN model using real recordings of the 2001 Bhuj mainshock, two Mw5.6 Kachchh aftershocks and the 1999 Mw6.4 Chamoli mainshock. The standard deviation of PGA prediction error estimates in log10 units is found to be ± 0.2554. Also, the model predictability of our ANN model suggests a good prediction of the PGA for earthquakes of Mw5.6–7.7, which are occurring in Kachchh, Gujarat, India.