Group Method Of Data Handling Network Research Articles

Predicting default risk bancassurance using GMDH and dce-GMDH neural network models

Purpose This study aims to develop a novel approach for predicting default risk in bancassurance, which plays a crucial role in the relationship between interest rates in banks and premium rates in insurance companies. The proposed method aims to improve default risk predictions and assist with client segmentation in the banking system. Design/methodology/approach This research introduces the group method of data handling (GMDH) technique and a diversified classifier ensemble based on GMDH (dce-GMDH) for predicting default risk. The data set comprises information from 30,000 credit card clients of a large bank in Taiwan, with the output variable being a dummy variable distinguishing between default risk (0) and non-default risk (1), whereas the input variables comprise 23 distinct features characterizing each customer. Findings The results of this study show promising outcomes, highlighting the usefulness of the proposed technique for bancassurance and client segmentation. Remarkably, the dce-GMDH model consistently outperforms the conventional GMDH model, demonstrating its superiority in predicting default risk based on various error criteria. Originality/value This study presents a unique approach to predicting default risk in bancassurance by using the GMDH and dce-GMDH neural network models. The proposed method offers a valuable contribution to the field by showcasing improved accuracy and enhanced applicability within the banking sector, offering valuable insights and potential avenues for further exploration.

Introducing effective parameters for predicting job burnout using a self-organizing method based on group method of data handling neural network.

In addition to affecting people's bodily and mental health, the Covid-19 epidemic has also altered the emotional and mental well-being of many workers. Especially in the realm of institutions and privately held enterprises, which encountered a plethora of constraints due to the peculiar circumstances of the epidemic. It was thus anticipated that the present study would use a group method of data handling (GMDH) neural network for analyzing the relationship of demographic factors, Coronavirus, resilience, and the burnout in startups. The test methodology was quantitative. The research examined 384 startup directors and representatives, which is a sizable proportion of the limitless community. The BRCS, the MBI-GS, and custom-made assessments of stress due to the Coronavirus were all used to collect data. Cronbach's alpha confirmed the polls' dependability, and an expert panel confirmed the surveys' authenticity. The GMDH neural network's inherent potential for self-organization was used to choose the most useful properties automatically. The trained network has a three-layered topology with 4, 3, and 2 neurons in each of the hidden layers. The GMDH network has significantly reduced the computational load by using just 7 parameters of marital status, stress of covid-19, job experience, professional efficiency, gender, age, and resilience for burnout categorization. After comparing the neural network's output with the acquired data, it was determined that the constructed network accurately classified all of the information. Among the achievements of this research, high accuracy in predicting job burnout, checking the performance of neural network in determining job burnout and introducing effective characteristics in determination of this parameter can be mentioned.

Estimating the energy dissipation of flow passing over triangular and trapezoidal plan weirs using the GMDH model

Abstract Weirs are one of the most common hydraulic structures used in water engineering projects. In this research, a group method of data handling (GMDH) was developed to estimate the energy dissipation of the flow passing over the labyrinth weirs with triangular and trapezoidal plans. To compare the performance of this model with other types of soft computing models, a multilayer perceptron neural network (MLPNN) was developed. The dimensionless parameters derived from dimensional analysis, including the relative upstream head (ho/P), the number of cycles (Ncy), the Froude number ( Fr), and the magnification ratio (Mr) were used as input variables. The error statistical indicators of the GMDH model in the training phase were R2 = 0.913, RMSE = 0.010, and in the testing phase were R2 = 0.829, RMSE = 0.015. The error statistical indicators of the MLPNN in the training phase were R2 = 0.957, RMSE = 0.007, and in the testing phase were R2 = 0.945, RMSE = 0.009. Examining the structure of the GMDH network shows that ho/P, Ncy, and Mr play more meaningful roles in the development network.

Air Kerma Calculation in Diagnostic Medical Imaging Devices Using Group Method of Data Handling Network

The air kerma, which is the amount of energy given off by a radioactive substance, is essential for medical specialists who use radiation to diagnose cancer problems. The amount of energy that a photon has when it hits something can be described as the air kerma (the amount of energy that was deposited in the air when the photon passed through it). Radiation beam intensity is represented by this value. Hospital X-ray equipment has to account for the heel effect, which means that the borders of the picture obtain a lesser radiation dosage than the center, and that air kerma is not symmetrical. The voltage of the X-ray machine can also affect the uniformity of the radiation. This work presents a model-based approach to predict air kerma at various locations inside the radiation field of medical imaging instruments, making use of just a small number of measurements. Group Method of Data Handling (GMDH) neural networks are suggested for this purpose. Firstly, a medical X-ray tube was modeled using Monte Carlo N Particle (MCNP) code simulation algorithm. X-ray tubes and detectors make up medical X-ray CT imaging systems. An X-ray tube's electron filament, thin wire, and metal target produce a picture of the electrons' target. A small rectangular electron source modeled electron filaments. An electron source target was a thin, 19,290 kg/m3 tungsten cube in a tubular hoover chamber. The electron source-object axis of the simulation object is 20° from the vertical. For most medical X-ray imaging applications, the kerma of the air was calculated at a variety of discrete locations within the conical X-ray beam, providing an accurate data set for network training. Various locations were taken into account in the aforementioned voltages inside the radiation field as the input of the GMDH network. For diagnostic radiology applications, the trained GMDH model could determine the air kerma at any location in the X-ray field of view and for a wide range of X-ray tube voltages with a Mean Relative Error (MRE) of less than 0.25%. This study yielded the following results: (1) The heel effect is included when calculating air kerma. (2) Computing the air kerma using an artificial neural network trained with minimal data. (3) An artificial neural network quickly and reliably calculated air kerma. (4) Figuring out the air kerma for the operating voltage of medical tubes. The high accuracy of the trained neural network in determining air kerma guarantees the usability of the presented method in operational conditions.

Accelerating the nonlinear analysis of hyper-elastic behavior by GMDH-assisted Newton-Raphson scheme

This paper presents an accelerated iterative scheme for nonlinear problems. Commonly, analysis of nonlinear behavior is conducted by the Newton-Raphson (NR) method. It is well-known that the number of iterations required depends on the deviation between the “starting point” and the converged solution. In practice, the solution of previous load step is taken as the “starting point”, while the converged solution of the current load step is not known beforehand. Therefore, difficulties or even non-convergence may occur. Recently, it is suggested that a neural network is employed to predict the solution of the current load step. This prediction is then used as the “starting point” for NR scheme. It is expected, that the true converged solution (of current step) is closer to the prediction by neural network than to the solution of previous load step. As a result, the scheme becomes faster due to less iterations. Obviously, any techniques for time-series forecasting can be used. Here, the Group Method of Data Handling (GMDH) is proposed. Loosely speaking, GMDH is a feedforward neural network without backpropagation. Practically, the GMDH-assisted NR scheme should not take longer time than conventional NR scheme. The advantage of GMDH is fast computation; however, the accuracy may be not as high as a network that has backpropagation. Therefore, careful consideration on the construction of GMDH network is needed. In the current work, the performance of GMDH-assisted NR scheme is investigated in analysis of hyper-elastic behavior, which involves both geometrical and material nonlinearity. A study on the influence of activation function on the accuracy is presented. Also, it is found that prediction for incremental displacement (between the current load step and the previous load step) could be better than prediction of displacement of the current load step.

Enhanced group method of data handling (GMDH) for permeability prediction based on the modified Levenberg Marquardt technique from well log data

Permeability is the key variable for reservoir characterization used for estimating the flow patterns and volume of hydrocarbons. Modern computer advancement has highlighted the use of machine learning approaches such as group method of data handling (GMDH) in predicting permeability. However, the widely employed GMDH has intrinsic problems in its application. Therefore, the objective of this study is to present an enhanced GMDH based modified Levenberg-Marquardt (LM) as an improved alternative to conventional GMDH in predicting permeability from well logs. The study used natural gamma-ray, standard resolution formation density, limited effective porosity, shale volume of rock, and thermal neutron porosity well logs as input variables. Results show that an enhanced method has a reasonable reduction in processing time with high accuracy. Compared to conventional GMDH and backpropagation neural networks (BPNN), the GMDH-LM used 30% less computation time and performed excellently during training with the least error values of 0.092 and 0.018 for RMSE and MAE. Likewise, good results were observed during testing, obtaining the least error values of 0.679 and 0.056 for RMSE and MAE respectively. The modified generalization performance of GMDH-LM makes it an improved form of GMDH and can be adopted as an improved alternative in predicting permeability.

Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil–water three phase flows

In this investigation, a fan-beam photon attenuation based system, including one X-ray tube and two sodium iodide crystal detectors, combined with group method of data handling (GMDH) neural network is proposed to recognize type of flow regime and predict gas-oil–water volume fractions of a three phase flow. One GMDH neural network was considered for recognizing flow patterns and two GMDH networks were implemented to predict the volume fractions. The recorded photon energy spectra from the two sodium iodide detectors were defined as the inputs of the three GMDH neural networks. The type of flow pattern and volume fractions were the output obtained from the first and the other two GMDH neural networks, respectively. Through the application of the proposed methodology, all of the flow patterns were recognized correctly except one single case. The volume fraction was also predicted with RMS error of less than 3.1.

Prediction of Disc Cutter Life During Shield Tunneling with AI via the Incorporation of a Genetic Algorithm into a GMDH-Type Neural Network

Abstract Disc cutter consumption is a critical problem that influences work performance during shield tunneling processes and directly affects the cutter change decision. This study proposes a new model to estimate the disc cutter life (Hf) by integrating a group method of data handling (GMDH)-type neural network (NN) with a genetic algorithm (GA). The efficiency and effectiveness of the GMDH network structure are optimized by the GA, which enables each neuron to search for its optimum connections set from the previous layer. With the proposed model, monitoring data including the shield performance database, disc cutter consumption, geological conditions, and operational parameters can be analyzed. To verify the performance of the proposed model, a case study in China is presented and a database is adopted to illustrate the excellence of the hybrid model. The results indicate that the hybrid model predicts disc cutter life with high accuracy. The sensitivity analysis reveals that the penetration rate (PR) has a significant influence on disc cutter life. The results of this study can be beneficial in both the planning and construction stages of shield tunneling.

Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

Over the past decade, energy forecasting applications not only on the grid side of electric power systems but also on the customer side for load and demand prediction purposes have become ubiquitous after the advancements in the smart grid technologies. Within this context, short-term electrical energy consumption forecasting is a requisite for energy management and planning of all buildings from households and residences in the small-scale to huge building complexes in the large-scale. Today’s popular machine learning algorithms in the literature are commonly used to forecast short-term building electrical energy consumption by generating an abstruse analytical expression between explanatory variables and response variables. In this study, gene expression programming (GEP) and group method of data handling (GMDH) networks are meticulously employed for creating genuine and easily understandable mathematical models among predictor variables and target variables and forecasting short-term electrical energy consumption, belonging to a large hospital complex situated in the Eastern Mediterranean. Consequently, acquired results yielded mean absolute percentage errors of 0.620% for GMDH networks and 0.641% for GEP models, which reveal that the forecasting process can be accomplished and formulated simultaneously via proposed algorithms without the need of applying feature selection methods.

Receiving More Accurate Predictions for Longitudinal Dispersion Coefficients in Water Pipelines: Training Group Method of Data Handling Using Extreme Learning Machine Conceptions

Longitudinal dispersion coefficient (LDC) is known as the most remarkable environmental variables which plays a key role in evaluation of pollution profiles in water pipelines. Even though, there is a wide range of numerical models to estimate coefficient of longitudinal dispersion, these mathematical techniques may often come in quite few inaccuracies due to complex mechanism of convection-diffusion processes in pollutant transition in water pipelines. In this research work, to obtain more accurate prediction of LDC, general structure of group method of data handling (GMDH) is modified by means of extreme learning machine (ELM) conceptions. In fact, with getting inspiration from ELM, a novel GMDH method, called GMDH network based on using extreme learning machine (GMDH-ELM), is proposed in which weighting coefficients of quadratic polynomials applied in conventional GMDH are no longer required to be updated either using back propagation technique or other evolutionary algorithms through training stage. In fact, an intermediate parameter is employed to establish a relationship between the input and output in each neuron of the GMDH model. In this way, a well-known and reliable dataset (233 experimental data) related to LDC in water network pipelines, as output vector, is applied to conduct training and testing phases. Through datasets, the Re number, the average longitudinal flow velocity, the friction factor of pipeline and the diameter of pipe are considered as inputs of the proposed approach. The results of GMDH-ELM model indicate a highly satisfying level of precision in both training and testing phases. Furthermore, feed forward structure of GMDH model was improved by particle swarm optimization (PSO) and gravitational search algorithm (GSA) to predict LDC. Through a sound judgment, a comparison is drawn between the performance of GMDH-ELM and other developed GMDH models. Moreover, several empirical equations existing in literature have been applied for comparisons. Overall, results of GMDH-ELM have permissible superiority over the other soft computing tools and conventional predictive models.

A novel efficient model for gas compressibility factor based on GMDH network

With the development of the low-power flowmeters, it is urgent and crucial to calculate the gas compression factor (Z-factor) in real time. However, the traditional estimation methods for Z-factor such as AGA8-92DC, needed a long calculation time and were difficult to be applied to the low-power embedded-based gas flowmeters. The other empirical correlations also had large errors. To solve this problem, we proposed a novel model for quick calculation of gas Z-factor based on Group Method of Data Handling(GMDH) network. The accuracy and reliability of this model were verified under different Tpr and Ppr. Compared with other empirical correlations, our model has the lowest root mean sum of squares of the errors (RMSE) of 0.0066 and mean absolute percentage error (MAPE) of 0.5615%, which is only 1/13–2/5 of MAPE calculated by the other correlations. The results show that our model has higher accuracy. Moreover, our model avoids lots of complex exponential and logarithmic operations, so it is especially useful for real-time Z-factor calculation of the low-power flowmeters

Analyzing nuclear reactor simulation data and uncertainty with the group method of data handling

Abstract Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems. In this study, multi-layer GMDH networks are used to perform uncertainty quantification (UQ) and sensitivity analysis (SA) of nuclear reactor simulations. GMDH is utilized as a surrogate/metamodel to replace high fidelity computer models with cheap-to-evaluate surrogate models, which facilitate UQ and SA tasks (e.g. variance decomposition, uncertainty propagation, etc.). GMDH performance is validated through two UQ applications in reactor simulations: (1) low dimensional input space (two-phase flow in a reactor channel), and (2) high dimensional space (8-group homogenized cross-sections). In both applications, GMDH networks show very good performance with small mean absolute and squared errors as well as high accuracy in capturing the target variance. GMDH is utilized afterward to perform UQ tasks such as variance decomposition through Sobol indices, and GMDH-based uncertainty propagation with large number of samples. GMDH performance is also compared to other surrogates including Gaussian processes and polynomial chaos expansions. The comparison shows that GMDH has competitive performance with the other methods for the low dimensional problem, and reliable performance for the high dimensional problem.

Group Method of Data Handling as a Tool to Determine Vertical Displacements

Surveying measurements carried out to determine displacements and deformations of existing civil structures and their surroundings provide information making it possible to represent their geometry in space and any changes it undergoes over time. Data acquired through geodetic monitoring can be modelled using artificial neural networks, capable of learning (adaptability) and quick operation and providing the possibility of visualisation by means of computer simulation. Neural networks, however, require specification of an optimal architecture by the user, as a result of which any resulting solutions are flawed by a difficult to identify error of method. Therefore, this article proposes an alternative approach in the form of the Group Method of Data Handling (GMDH) based on evolutionary algorithms. The article presents the fundamental assumptions for the GMDH and the principles of development and training of static neural networks with multiple inputs and one output. The GMDH network was used to develop a geometric model of vertical displacements determined on the basis of periodic measurements taken on civil structures.

Prediction of CO2 adsorption on different activated carbons by hybrid group method of data-handling networks and LSSVM

ABSTRACTCO2 is a ubiquitous species that has received much attention recently. The adsorption of CO2 by means of activated carbons is a well-tried technology that can be used on a large scale. Improvements of the prediction models with more accurate results and lower error are necessary for future development of the projects and the economic dispatch sector. The least square support vector machine, a relatively unexplored neural network known as group method of data handling (GMDH), were implemented to forecast the CO2 adsorption on different activated carbons. This work aims to provide new methods to predict the adsorption equilibrium of pure CO2 on a set of commercial activated carbons and to express it regarding textural properties such as Brunauer–Emmett–Teller (BET) surface area, total pore volume, and micropore volume. Results indicated that the utilized models are very accurate in predicting CO2 adsorption on different activated carbons. Comparison of the outcomes of the two models shows that the GMDH model is more accurate with R2 and mean squared error values of 0.8915 and 0.0001425, respectively.

Stress wave evaluation by accelerometer and acoustic emission sensor for thermally modified wood classification using three types of neural networks

Classification of thermally modified wood (TMW) allowing the distinction between different processing temperatures and the corresponding changes in wood properties is a crucial task in TMW grading. In this study, stress wave evaluation technique was used to classify the heat treatment level. Accordingly, an acoustic emission (AE) sensor and a pair of accelerometers captured stress waves generated by pendulum impact, and the data was used to classify the heat treatment level of thermally modified Western hemlock wood samples. Sensory features were extracted from time, frequency, and wavelet domain analysis. The extracted features were then used to train multilayer perceptron (MLP), group method of data handling (GMDH), and linear vector quantization (LVQ) neural networks for TMW classification. The results showed that while the features extracted from the accelerometers such as stress wave velocity and wood dynamic modulus of elasticity showed poor classification performance, acoustic emission sensory features were effective for classification of TMW. Wavelet domain features lead to better classification than those extracted from time and frequency domains. Feature fusion approach comprising the features from all the signal domains showed the best classification performance that was further improved by using a dimensionality reduction approach. The linear discriminant analysis was conducted on all acoustic emission features and resulted in 91.1% and 89.1% accuracy obtained from the LVQ and GMDH network, respectively. This performance was further increased to 98% and 97% using the LVQ and GMDH models when the input was combined with wood moisture content. The MLP neural network did not seem as suitable as the other two models. Neural network modeling using the captured stress wave data from an AE sensor could therefore be a promising nondestructive evaluation method for TMW classification.

The back propagation based on the modified group method of data-handling network for oilfield production forecasting

In this paper, a novel hybrid forecasting model combining modified group method of data handling (GMDH) and back propagation (BP) is introduced for time series oilfield production forecasting. The proposed model takes advantages of both the modified GMDH networks in effective parameter selection and the BP network in excellent nonlinear mapping and provides a robust simulation ability for oilfield production with higher precision. Various production parameters of an actual oilfield were utilized to analyze and test the annual output predicted by proposed model (modified GMDH-BP). The performance of the proposed model was compared with the multiple linear regression (MLR), GMDH, modified GMDH, BP, and the hybrid model combining group method of data handling and back propagation (GMDH-BP) using time series annual production data. The relative error, correlation coefficient (R), root mean square error, mean absolute percentage of error, and scatter index were utilized to investigate the performance of the presented models. The evaluation results indicate that the hybrid model provides more accurate production forecasts compared to other models and exhibits a robust simulation ability for capturing the nonlinear relation of complex production time series prediction of oilfield.

A New Input Selection Algorithm Using the Group Method of Data Handling and Bootstrap Method for Support Vector Regression Based Hourly Load Forecasting

Electric load forecasting is indispensable for the effective planning and operation of power systems. Various decisions related to power systems depend on the future behavior of loads. In this paper, we propose a new input selection procedure, which combines the group method of data handling (GMDH) and bootstrap method for support vector regression based hourly load forecasting. To construct the GMDH network, a learning dataset is divided into training and test datasets by bootstrapping. After constructing GMDH networks several times, the inputs that appeared frequently in the input layers of the completed networks were selected as the significant inputs. Filter methods based on linear correlation and mutual information (MI) were employed as comparison methods, and the performance of hybrids of the filter methods and the proposed method were also confirmed. In total, five input selection methods were compared. To verify the performance of the proposed method, hourly load data from South Korea was used and the results of one-hour, one-day and one-week-ahead forecasts were investigated. The experimental results demonstrated that the proposed method has higher prediction accuracy compared with the filter methods. Among the five methods, a hybrid of an MI-based filter with the proposed method shows best prediction performance.

GMDH-GEP to predict free span expansion rates below pipelines under waves

ABSTRACTIn this research, group method of data handling (GMDH) as a one of the self-organized approaches is utilized to predict three-dimensional free span expansion rates around pipeline due to waves. The GMDH network is developed using gene-expression programming (GEP) algorithm. In this way, GEP was performed in each neuron of GMDH instead of polynomial quadratic neuron. Effective parameters on the three-dimensional scour rates include sediment size, pipeline geometry, and wave characteristics upstream of pipeline. Four-dimensionless parameters are considered as input variables by means of dimensional analysis technique. Furthermore, scour rates along the pipeline, vertical scour rate, and additionally scour rates in the left and right of pipeline are determined as output parameters. Results of the proposed GMDH-GEP models for the training stages and testing ones are evaluated using various statistical indices. Performances of the GMDH-GEP models are compared with artificial neural network (ANN), GEP, GMDH, and traditional equations-based regression models. Moreover, sensitivity analysis and parametric study are conducted to perceive influences of different input parameters on the three-dimensional scour rates.

Modified GMDH networks for oilfield production prediction

ABSTRACTThe self-organizing Group Method of Data Handling (GMDH) functional network is effective in predicting oilfield production. During operation the division of data sample depending on artificial classification cannot lead to global optimum in great probability and the variables are probably eliminated early in the iterative process in traditional GMDH algorithm. Recent years, GMDH model has been improved through many artificial intelligent models, but few people take the optimization of the model structure into account. In this paper, different training and testing set grouping and the effects of variables transmission were studied. The modified GMDH algorithm was optimized using the original variables preservation method and the random sample method, which was applied to the oilfield production forecasting simulation. The results of the modified GMDH algorithm, the traditional GMDH algorithm, ANNs and the empirical equations for predicting annual oil production were compared. The simulative results indicated that the modified GMDH model was the best tool for data-fitting with lowest error (RMSE = 13.9440, MAPE = 0.1121 and SI = 0.0378) and highest accuracy (R = 0.9984).

Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: A comparison between GMDH, ANN, and MLR

Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs’ development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5–45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity (Ks), bulk (Db) and particle (Dp) densities, organic carbon (OC), wet-aggregate stability (WAS), electrical conductivity (EC), and soil antecedent (θi) and field saturated (θfs) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding Ks. According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including Ks resulted in more accurate (with E values of 0.673–0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356–0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed that Ks exclusion from input variables list caused around 30 percent decrease in PTFs accuracy for all applied procedures. However, it seems that Ks exclusion resulted in more practical PTFs especially in the case of GMDH network applying input variables which are less time consuming than Ks. In general, it is concluded that GMDH provides more accurate and reliable estimates of cumulative infiltration (a non-readily available characteristic of soil) with a minimum set of input variables (2–4 input variables) and can be promising strategy to model soil infiltration combining the advantages of ANN and MLR methodologies.