Parameters Of Prediction Model Research Articles

Construction and validation of parametric models to predict radium sorption in soils

Elucidating the factors affecting the transfer of naturally occurring radionuclides (NOR) between environmental compartments is a key part of the assessment of ecosystem's exposure to naturally occurring radionuclide materials (NORM). For that, the sorption and desorption solid-liquid distribution coefficients (Kd) of radium (Ra) were quantified in a collection of 31 soil samples with contrasting edaphic properties under controlled conditions in laboratory batch experiments. Ra sorption was demonstrated to be moderate to high, with Kd (Ra) values ranging from 102 to 103 L kg−1. Ra sorbed was mostly irreversible, as evidenced by desorption percentages lower than 2 %. An exploratory analysis with partial least squares (PLS) regression identified the soil properties that correlated with Kd (Ra) and discarded those that were not relevant for describing Kd variability. A dataset of the sorption Kd (Ra) values and associated soil properties was built from our own data and from the literature after performing an in-depth review of similar Ra sorption studies. For the first time, Kd (Ra) parametric prediction models were constructed using univariate linear regression (ULR) and multivariate linear regression (MLR). Ra sorption in soils was mostly explained by the soil properties directly or indirectly related to the available exchange sites, such as the levels of water-soluble and exchangeable Ca and Mg as well as the pH of the contact solution. The most promising models explained around 80 % of the Kd (Ra) data variance, only needing Kd (Ca + Mg) or additional soil descriptors such as pH, Mn content, and the specific surface area. The validation of the proposed models confirmed that Kd (Ra) can be predicted with only a few soil properties that can be characterised in routine analysis. Thus, the proposed models could be used to estimate the interaction of Ra in soils in risk assessment.

Test Method for Rapid Prediction of Steady-State Temperature of Outer Rings of Bearings under Grease Lubrication Conditions

Temperature has a great influence on the stability of bearing performance. For the study of bearing steady-state temperature, this paper proposes a test method to quickly predict the steady-state temperature of the outer ring of a bearing, which solves the problems in traditional theoretical calculations and simulation analysis methods such as the large number of calculations, complex models, and large errors. Firstly, a mathematical prediction model is established according to the bearing temperature-rise law; then, a bearing steady-state temperature detection device is designed; and finally, the prediction model parameters are solved according to the experimental results, and experimental verification is carried out. It is shown that the prediction model has high accuracy under different load and speed conditions, and the error between the predicted steady-state temperature and the tested steady-state temperature is less than 0.7 °C. This prediction method reduces the single test time of the same speed to 60 min, which greatly improves the efficiency of the temperature detection test. The steady-state temperature model has important theoretical significance in guiding the study of the limiting speed of bearings.

Analyzing meteorological parameters using Pearson correlation coefficient and implementing machine learning models for solar energy prediction in Kuching, Sarawak

Solar energy is one of the clean renewable energy sources that can offset the rising consumption of fossil fuels. However, the meteorological parameters, such as solar irradiance, ambient and solar module temperatures, relative humidity, etc., constantly change, and so does the solar power generation. Such variations cause instability in the power grid operation due to injecting an unpredicted amount of power. Hence, solar energy prediction models capable of learning from past weather data and predicting future energy generation are highly desired for grid operation and planning. The objective of this study is to determine the suitable meteorological parameters for the solar energy prediction model based on the Pearson correlation coefficient and to implement them in different machine learning models. It is found in this study that five meteorological parameters, namely Air temperature, cloud opacity, global tilted irradiance, relative humidity, and zenith angle, correlate highly with solar energy generation. Later, based on the correlations, four machine-learning models were implemented to predict the solar power for Kuching, Sarawak. The accuracy of the models is measured through standard matrices such as root mean square error, mean square error, mean absolute error, and R-squared value.

Cold spray processing of AA2024/Al2O3 coating on magnesium AZ31B alloy: Process parameters optimization, microstructure and adhesive strength performance of coating

The automotive and aerospace sectors are progressively employing the magnesium (Mg) alloy of the grade AZ31B because of its excellent castability, low density, and high ration of strength to weight. Nevertheless, the limited ability of AZ31B alloy to withstand corrosion limits their use in several fields of technology. In order to solve this problem, the AZ31B alloy is coated utilizing an AA2024/Al2O3 metal matrix composite (MMC) coating that is applied by the cold spray coating (CS) method. The primary goal of this work is the parametric optimization of CS process for maximizing adhesive strength of MMC-coated Mg-alloy substrate. Response surface methodology (RSM) is implemented to find the optimum CS parameters, including feed rate of powder – FRP (g/min), standoff distance of gun – SDG (mm) and processing temperature – TEMP (oC). The regression-based parametric adhesion strength prediction (ASP) model was formulated using the RSM and statistically validated using analysis of variance (ANOVA). Employing 3D surface of responses, the influence of CS parameters on the adhesion strength of an MMC-coating was assessed. The findings revealed that when the MMC-coating was cold sprayed on the Mg-alloy using FRP of 22 g/min, SDG of 12 mm, and TEMP of 520oC, the maximum adhesion strength of MMC-coating was 70 MPa (actual). Given less than 2% error at 95% confidence, the parametric ASP model correctly predicted the adhesion strength of the MMC-coating. The ANOVA findings showed that FRP (g/min) had significant effect on adhesive strength of MMC-coating followed by SDG (mm) and TEMP (oC). The MMC-coating applied using the RSM optimized CS parameters showed 70.73% superior adhesive strength owing to the lower porosity formation of 2 vol.% which offers greater interfacial area. The ASP equation was formulated using the “best fitting line” approach and validated using ANOVA for predicting the adhesive strength (MPa) from the porosity formation (vol.%) in the MMC-coating.

Ultra-early prediction of the process parameters of coal chemical production

Most accidents in a chemical process are caused by abnormal or deviations of the process parameters, and the existing research is focused on short-term prediction. When the early warning time is advanced, many false and missing alarms will occur in the system, which will cause certain problems for on-site personnel; how to ensure the accuracy of early warning as much as possible while the early warning time is a technical problem requiring an urgent solution. In the present work, a bidirectional long short-term memory network (BiLSTM) model was established according to the temporal variation characteristics of process parameters, and the Whale optimization algorithm (WOA) was used to optimize the model's hyperparameters automatically. The predicted value was further constructed as a Modified Inverted Normal Loss Function (MINLF), and the probability of abnormal fluctuations of process parameters was calculated using the residual time theory. Finally, the WOA-BiLSTM-MINLF process parameter prediction model with inherent risk and trend risk was established, and the fluctuation process of the process parameters was transformed into dynamic risk values. The results show that the prediction model alarms 16 min ahead of distributed control systems (DCS), which can reserve enough time for operators to take safety protection measures in advance and prevent accidents.

Genomic prediction of blood biomarkers of metabolic disorders in Holstein cattle using parametric and nonparametric models

BackgroundMetabolic disturbances adversely impact productive and reproductive performance of dairy cattle due to changes in endocrine status and immune function, which increase the risk of disease. This may occur in the post-partum phase, but also throughout lactation, with sub-clinical symptoms. Recently, increased attention has been directed towards improved health and resilience in dairy cattle, and genomic selection (GS) could be a helpful tool for selecting animals that are more resilient to metabolic disturbances throughout lactation. Hence, we evaluated the genomic prediction of serum biomarkers levels for metabolic distress in 1353 Holsteins genotyped with the 100K single nucleotide polymorphism (SNP) chip assay. The GS was evaluated using parametric models best linear unbiased prediction (GBLUP), Bayesian B (BayesB), elastic net (ENET), and nonparametric models, gradient boosting machine (GBM) and stacking ensemble (Stack), which combines ENET and GBM approaches.ResultsThe results show that the Stack approach outperformed other methods with a relative difference (RD), calculated as an increment in prediction accuracy, of approximately 18.0% compared to GBLUP, 12.6% compared to BayesB, 8.7% compared to ENET, and 4.4% compared to GBM. The highest RD in prediction accuracy between other models with respect to GBLUP was observed for haptoglobin (hapto) from 17.7% for BayesB to 41.2% for Stack; for Zn from 9.8% (BayesB) to 29.3% (Stack); for ceruloplasmin (CuCp) from 9.3% (BayesB) to 27.9% (Stack); for ferric reducing antioxidant power (FRAP) from 8.0% (BayesB) to 40.0% (Stack); and for total protein (PROTt) from 5.7% (BayesB) to 22.9% (Stack). Using a subset of top SNPs (1.5k) selected from the GBM approach improved the accuracy for GBLUP from 1.8 to 76.5%. However, for the other models reductions in prediction accuracy of 4.8% for ENET (average of 10 traits), 5.9% for GBM (average of 21 traits), and 6.6% for Stack (average of 16 traits) were observed.ConclusionsOur results indicate that the Stack approach was more accurate in predicting metabolic disturbances than GBLUP, BayesB, ENET, and GBM and seemed to be competitive for predicting complex phenotypes with various degrees of mode of inheritance, i.e. additive and non-additive effects. Selecting markers based on GBM improved accuracy of GBLUP.

An adaptive distribution-matched recurrent network for wind power prediction using time-series distribution period division

Precise wind power prediction (WPP) can address the issue caused by large-scale wind power grid integration to the power system operation. Most WPP research focus on the randomness and high volatility problem of wind power but ignore the time-series distribution shift (TSDS) problem. To solve the TSDS issue, this study proposes a novel hybrid model that incorporates complementary ensemble empirical mode decomposition (CEEMD), time-series distribution period division (TSDPD) and adaptive distribution-matched GRU (ADMGRU). First, CEEMD is utilized to decompose the nonstationary data into in sub-sequence, reducing complexity and randomness. Second, TSDPD is employed to automatically identify the underlying temporal segments within wind power sequence by maximizing discrepancies in distribution information between two periods, determining the quantity and respective boundaries of periods. Finally, ADMGRU, comprising Pre-train and Boosting-based importance assessment components, learns prediction model accurately by dynamically matching distribution periods. The former component initializes predictive model parameters and the latter learns the importance of each hidden state to assigns corresponding weights to different distributions. Numerous comparative experiments demonstrate the CEEMD-TSDPD-ADMGRU hybrid model surpasses existing popular models. Especially in spring scenario, compared with other four advanced models, the maximum reduction in MAE and RMSE are 54.64 % and 73.33 %, respectively.

A novel spaceborne photon-counting laser altimeter denoising method based on parameter-adaptive density clustering

ABSTRACT To tackle the challenge of denoising spaceborne photon-counting laser altimeter point clouds with uneven noise density, this study proposes a denoising method based on adaptive parameter density clustering, which utilizes numerical simulations to achieve self-adaptation of key parameters (neighborhood radius Eps and minimum number of points MinPts ). First, taking the directional adaptive ellipse DBSCAN (DAE-DBSCAN) as an example, photons with different background photon count rates ( bckgrd _ rate ) are used to traverse Eps and MinPts to calculate their optimal values ( Eps and MinPts with the highest denoising accuracy). Then, a mathematical prediction model of bckgrd _ rate , Eps and MinPts was established. The actual background photon count rates were introduced into the key parameter prediction model to obtain the optimal Eps and MinPts . Finally, a denoising experiment was conducted using the simulated photons and the ATLAS data. The results show that the proposed method had higher accuracy than the constant parameter denoising method, with an F >0.95. Even for photons of complex mountainous terrain with a high background photon count rate, the denoising accuracy was still higher than 0.9. The proposed method improves the denoising accuracy of photons with different noise densities by adapting density clustering parameters.

Non-destructive Prediction of Quality Parameters in Alphonso Mangoes Using Near-infrared Spectroscopy: A Comprehensive Study on Physicochemical Characteristics and Ripening Dynamics

This study focuses on the application of Near-Infrared Spectroscopy (NIRS) to predict key quality parameters in Alphonso mangoes, namely Total Soluble Solids (TSS), Titratable Acidity (TA), pulp firmness, and carotenoid content. Alphonso mangoes, known as the "king of fruits," are a significant tropical fruit, with India being the leading global producer. Quality control is crucial for fruit export, and the mango supply chain involves various procedures. Physiological transformations occur in mangoes from production to consumption, affecting their internal quality. The challenges in assessing internal quality factors such as soluble solids, dry matter, firmness, starch, and sugar content hinder the exportation of mangoes. The ripening process in mangoes involves metabolic changes leading to modifications in physical, mechanical, and chemical attributes. Non-destructive identification of internal defects in Alphonso mangoes is an underexplored field, with limited research on internal quality factors. The study aims to enhance understanding of light penetration into fruit tissues using numerical simulation techniques. NIR spectroscopy's significance in post-harvest technology is highlighted, and the study develops prediction models for quality parameters using Partial Least Squares (PLS) regression. The calibration models are optimized with spectral pre-processing techniques, and the Savitzky–Golay first derivative method yields favorable results. The results indicate changes in TSS, carotenoid content, firmness, and acidity during storage, providing insights into mango ripening dynamics. The Unscrambler software is employed for multivariate analysis, emphasizing the importance of the 600–1000 nm wavelength range. The study contributes to the development of portable instruments for rapid and accurate quality prediction of Alphonso mangoes in the supply chain.

Prediction of the Modal Parameters of The Spindle System in High-speed Dry Hobbing Machine with Thermal Effects

High-speed dry hobbing is an advanced green machining technology for gears. However, thermal issues in the machine tools become serious under the high cutting speed and dry cutting conditions, which result in a nonlinear evolution of the machine tool dynamics, and finally affect the hobbing stability. In view of this, introducing the hob spindle system as the object, this paper intends to investigate the influence of time-varying temperatures on the modal parameters based on the modal tests. Then, a temperature-dependent modal parameters prediction model based on the multi-output Gaussian process regression (MOGPR) is proposed. The experimental results indicated that the modal parameters of the spindle system vary obviously with changing thermal states. The natural frequencies for the first and third modes increased with the temperature rise, while the variation of the natural frequency for the second mode was insignificant. The damping ratios for the first three modes showed a decreasing trend with the temperature rise. The constructed MOGPR model has the capability to effectively predict the modal parameters with the time-varying thermal effects, which can provide a support to build the temperature-dependent hobbing stability boundaries.

Speed-based analysis and prediction of fatigue life for the crankshaft via genetic algorithm

To improve the accuracy of crankshaft fatigue life prediction, a new method for predicting the fatigue life of crankshafts was proposed, considering the influence of rotational speed on fatigue life. This study investigates the relationship between engine speed and crankshaft fatigue life, and the engine speed will obviously affect the fatigue life of the crankshaft. Considering the influence of different engine speeds, a crankshaft fatigue life prediction model considering the engine speed is proposed. The method first obtains the crankshaft force time domain data under different loads through multi-body dynamics simulation, and analyzes the relationship between different engine speeds and crankpins force. The crankshaft fatigue life under different loads of the engine is calculated based on fatigue damage accumulation theory. Genetic algorithm is used to find the optimization of the relevant parameters of the crankshaft fatigue life prediction model considering the engine speed and determine the values of the parameters. The fatigue life and remaining fatigue life of the crankshaft is calculated using the proposed crankshaft fatigue life prediction model according to the engine speed signal. The proposed model in this study demonstrates a higher level of accuracy, as evidenced by the result showing a prediction error of 3.45%.

How to select oil price prediction models — The effect of statistical and financial performance metrics and sentiment scores

Predicting crude oil prices is an important yet challenging forecasting problem due to various influencing quantitative and qualitative factors. To address the growing number of potential prediction models and model parameters to consider during model selection, we highlight the need to systematically compare alternative prediction models and variables while taking the specific context of their application into account. Specifically, we provide a novel perspective on oil price prediction models by comparing a variety of different forecasting models and considering both their statistical and financial performance. In addition to common statistical measures, to assess the usefulness in a practical setting we evaluate the potential financial impact of the predictions in a simulation of a simple trading strategy. We show that the ranking of different approaches depends on the selected evaluation metric and that small differences between models in one evaluation metric can translate into large differences in another metric. For instance, forecasts that are not considered statistically different can lead to substantially different financial performance when the forecasts are used in a trading strategy. Finally, we show that including qualitative information in the prediction models through sentiment analysis can yield both statistical and financial performance improvements.

Machine learning prediction models for ground motion parameters and seismic damage assessment of buildings at a regional scale

This study examines the feasibility of using a machine learning approach for rapid damage assessment of reinforced concrete (RC) buildings after the earthquake. Since the real-world damaged datasets are lacking, have limited access, or are imbalanced, a simulation dataset is prepared by conducting a nonlinear time history analysis. Different machine learning (ML) models are trained considering the structural parameters and ground motion characteristics to predict the RC building damage into five categories: null, slight, moderate, heavy, and collapse. The random forest classifier (RFC) has achieved a higher prediction accuracy on testing and real-world damaged datasets. The structural parameters can be extracted using different means such as Google Earth, Open Street Map, unmanned aerial vehicles, etc. However, recording the ground motion at a closer distance requires the installation of a dense array of sensors which requires a higher cost. For places with no earthquake recording station/device, it is difficult to have ground motion characteristics. For that different ML-based regressor models are developed utilizing past-earthquake information to predict ground motion parameters such as peak ground acceleration and peak ground velocity. The random forest regressor (RFR) achieved better results than other regression models on testing and validation datasets. Furthermore, compared with the results of similar research works, a better result is obtained using RFC and RFR on validation datasets. In the end, these models are utilized to predict the damage categories of RC buildings at Saitama University and Okubo Danchi, Saitama, Japan after an earthquake. This damage information is crucial for government agencies or decision-makers to respond systematically in post-disaster situations.

Research on Parameter Inversion of Coal Mining Subsidence Prediction Model Based on Improved Whale Optimization Algorithm

Rapid coal mining results in a series of mining subsidence damages. Predicting surface movement and deformation accurately is essential to reducing mining damage. The accurate determination of parameters for a mining subsidence prediction model is crucial for accurately predicting mining subsidence. In this research, with the incorporation of the Sobol sequence and Lévy flight strategy, we propose an improved whale optimization algorithm (IWOA), thereby enhancing its global optimization capability and mitigating local optimization issues. Our simulation experiment results demonstrate that the IWOA achieved a root mean square error and relative error of less than 0.42 and 0.27%, respectively, indicating its superior accuracy compared to a basic algorithm. The IWOA inversion model also exhibits superior performance compared to a basic algorithm in mitigating gross error interference, Gaussian noise interference, and missing observation point interference. Additionally, it demonstrates enhanced global search capabilities. The IWOA was employed to perform parameter inversion for the working face 1414(1) in Guqiao Coal Mine. The root mean square error of the inversion results did not exceed 6.03, while the subsidence coefficient q, tangent of the main influence angle tanβ, horizontal movement coefficient b, and mining influence propagation angle θ were all below 0.32. The average value of the fitted root mean square error for the subsidence value’s fitted root mean square error and horizontal movement value’s fitted root mean square error of the IWOA was 91.51 mm, which satisfies the accuracy requirements for general engineering applications.

Machine learning-assisted FTIR spectra to predict freeze-drying curve of food

With the demand for improving freeze-drying (FD) process efficiency and protecting product characteristics, intelligent and robust analysis of process parameters for drawing effective FD curves has become the development direction of modern FD process. In this study, a prompt and applicable prediction model of FD parameters was designed by FTIR associated with chemometrics. By using spectral preprocessing and principal component analysis, 34 feature wavenumbers were extracted as input variables for modeling to quantify FD parameters. Among the 18 parameter prediction models, artificial neural network was adopted as the optimum model for the temperature and time of pre-freezing and desorption stages (R2 = 0.91, 0.83, 0.92, 0.84, RMSE = 0.12, 0.13, 0.08, 0.10), and random forest was confirmed as the best model for the parameters of sublimation stage (R2 = 0.88, 0.77, RMSE = 0.13, 0.16). According to the model prediction, random samples were selected for verification that the experimental results were close to 96% agreement with the model output.

Uncertainty quantification in multiaxial fatigue life prediction using Bayesian neural networks

In practical engineering scenarios, unforeseen failures may result in significant costs and risks. Assessing the uncertainty related to multiaxial fatigue life prediction is crucial for engineering applications. However, establishing a reliable multiaxial fatigue life prediction model remains challenging due to uncertainties in physical models, material properties, and measurement data. This paper proposes an effective Bayesian Neural Network (BNN) model for quantifying the uncertainty in multiaxial fatigue life prediction. Fatigue parameters in the multiaxial fatigue life prediction model are used as inputs for the BNN. The BNN method utilizes No-U-turn Sampler (NUTS) and Automatic Differentiation Variational Inference (ADVI) to address the inference problem in the model and perform life predictions for unknown non-proportional loading paths and different materials. Validation using experimental data from six different materials confirms the effectiveness of the BNN model. Experiments show that the performance of BNN models is beneficial in most cases compared to classical ML models, assessed through different error standards, statistical tests, Taylor diagrams, uncertainty analysis, and scatter index analysis. The proposed method provides a clear understanding of the uncertainty in multiaxial fatigue life prediction, offering a promising approach for simulating fatigue life under multiaxial loading in engineering applications.

Development of a Predictive Model for the Indirect Tensile Strength Load–Displacement Curve for Asphalt Mixtures

The indirect tensile (IDT) strength test is one of the common laboratory tests conducted to characterize asphalt mixtures. The IDT load–displacement curve is used to calculate various indicators to evaluate the cracking performance of asphalt mixtures. This study evaluated the effect of the mix composition on the IDT load–displacement curve. Asphalt mixtures with different characteristics, including binder content, binder grade, and mix gradation, were prepared and tested. The study developed statistical regression models to predict the entire IDT load–displacement curve using parameters that describe the mix composition and properties, including the binder complex shear modulus and phase angle, aggregate gradation, and binder content. The results demonstrated that the predicted model parameters were found to correlate well with those calculated from the experimental IDT load–displacement curves. All predicted cracking performance indicators calculated from the predicted IDT load–displacement curve had a strong correlation with those calculated from the measured IDT load–displacement curve. The proposed model can be used as a tool to check the preliminary cracking performance of the asphalt mixtures during the mix design stage. In addition, the mix composition can be adjusted to improve the cracking performance of the designed mix (e.g., adjust binder grade, binder content, gradation, etc.).

Numerical thermal assessment and theoretical analysis of horizontal-hole interlock composite insulation blocks

In this paper, the thermal performance of 54 horizontal-hole interlocking composite insulation blocks was numerically analyzed based on the steady-state heat transfer model, the regulations were found, and prediction models for the thermal parameters of the blocks were established based on the parallel network thermal resistance model. The influence of block types and block materials on the thermal performance was studied, and it was found that the thermal performance of H-shaped interlocking insulation blocks was better than that of crossed-shaped ones, and the lower the thermal conductivity of block materials, the better the thermal performance of blocks, among which the reduction of thermal conductivity of concrete materials improved the thermal performance of blocks the most. The simulated values of thermal conductivity of H-shaped interlocking insulation blocks and thermal conductivity under the parallel network thermal resistance model were highly linearly correlated with correlation coefficients as high as 0.998 and 0.999. This finding enables the prediction models of thermal parameters of H-shaped interlocking insulation blocks to be established with high accuracy and provides an effective guide for the energy-saving design of interlocking insulation blocks wall.

Study on Rock Mechanics Parameter Prediction Method Based on DTW Similarity and Machine-Learning Algorithms

Rock mechanics parameters are crucial factors for predicting rock behavior in oil and gas reservoirs, optimizing extraction strategies, and ensuring drilling safety. In this study, we propose a random forest (RF)-convolutional neural network (CNN)-long-term short-term memory network (LSTM) fusion model based on the dynamic time warping (DTW) algorithm to construct intelligent prediction models for elastic modulus, Poisson’s ratio, and compressive strength using real-time drilling engineering data. An autoencoder with a sliding window is employed to automatically identify abnormal points or segments in the calculated values of elastic modulus, Poisson’s ratio, and compressive strength obtained from drilled wells. These abnormal values are then corrected using a backpropagation (BP) neural network. Compared to single CNN-LSTM or single RF models, the RF-CNN-LSTM fusion model performs better. It achieves this by effectively combining the strengths of different algorithms in predicting outcomes. The accuracy of the RF-CNN-LSTM fusion model is over 94% when compared to the actual values. Furthermore, the analysis of the relative importance of input parameters reveals that weight on bit (WOB), temperature, displacement, equivalent circulation density (ECD), and mud density are the primary input features for predicting elastic modulus. For predicting Poisson’s ratio, the main input features include WOB, mud density, ECD, temperature, pumping pressure, displacement, and rate of penetration (ROP). Similarly, for predicting compressive strength, the main input features consist of WOB, temperature, displacement, ECD, and mud density. The research findings demonstrate that the rock mechanics parameter prediction models based on the RF-CNN-LSTM algorithm using DTW exhibit high computational accuracy in the B oil field of China. These results are significant for gaining a deeper understanding of the variations in rock mechanics parameters and optimizing drilling decisions.

Modeling basal area yield using simultaneous equation systems incorporating uncertainty estimators

Abstract Over the last three decades, many growth and yield systems developed for the southeast USA have incorporated methods to create a compatible basal area (BA) prediction and projection equation. This technique allows practitioners to calibrate BA models using both measurements at a given arbitrary age, as well as the increment in BA when time series panel data are available. As a result, model parameters for either prediction or projection alternatives are compatible. One caveat of this methodology is that pairs of observations used to project forward have the same weight as observations from a single measurement age, regardless of the projection time interval. To address this problem, we introduce a variance–covariance structure giving different weights to predictions with variable intervals. To test this approach, prediction and projection equations were fitted simultaneously using an ad hoc matrix structure. We tested three different error structures in fitting models with (i) homoscedastic errors described by a single parameter (Method 1); (ii) heteroscedastic errors described with a weighting factor ${w}_t$ (Method 2); and (iii) errors including both prediction ($\overset{\smile }{\varepsilon }$) and projection errors ($\tilde{\varepsilon}$) in the weighting factor ${w}_t$ (Method 3). A rotation-age dataset covering nine sites, each including four blocks with four silvicultural treatments per block, was used for model calibration and validation, including explicit terms for each treatment. Fitting using an error structure which incorporated the combined error term ($\overset{\smile }{\varepsilon }$ and $\tilde{\varepsilon}$) into the weighting factor ${w}_t$ (Method 3), generated better results according to the root mean square error with respect to the other two methods evaluated. Also, the system of equations that incorporated silvicultural treatments as dummy variables generated lower root mean square error (RMSE) and Akaike’s index values (AIC) in all methods. Our results show a substantial improvement over the current prediction-projection approach, resulting in consistent estimators for BA.