Accurate Method For Assessment Research Articles

Diagnosis of the accuracy of land cover classification using bootstrap resampling

ABSTRACT Since the 1970s, there has been a long history of assessing the accuracy of remotely sensed land cover types. Various accuracy assessment methods have been proposed, including the method of bootstrap resampling. However, the potential of the bootstrap resampling method to diagnose the accuracy of remote sensing monitoring of land cover has not been fully explored. Here, we used the land cover map classified in the Gannan Tibetan Autonomous Region as an example and investigated the impact of two parameters in the bootstrap resampling method on the assessment of land cover classification accuracy: the number of validation points and the number of bootstrap resampling times. Varying these two parameters can help diagnose the accuracy of remote sensing classifications of land cover. The main conclusions are as follows. First, the number of validation points required to achieve a stabilized accuracy estimate varies among land cover types. It indicates not only the level of homogeneity of the land cover type but also the level of difficulty associated with classifying it. Second, it is not necessary to have a very large number of bootstrap resampling times to estimate the classification accuracy – 50 was sufficient. In summary, we recommend that various numbers of validation points be employed in bootstrap to objectively and comprehensively assess the overall, producer’s, and user’s accuracies of remote sensing classifications of land cover.

Assessing the Influence of Vehicular Traffic-Associated Atmospheric Pollutants on Pulmonary Function Using Spirometry and Impulse Oscillometry in Healthy Participants: Insights from Bogotá, 2020–2021

Air pollution, particularly from particulate matter (PM2.5) and black carbon (eBC), has been implicated in airway pathologies. This study aims to assess the relationship between exposure to these pollutants and respiratory function in various populations, including healthy individuals, while seeking an accurate assessment method. A cross-sectional study was conducted in Bogotá, evaluating respiratory function in the users of bicycles, minivans, and buses through spirometry and impulse oscillometry. Measurements were taken along two main avenues, assessing the PM2.5 and eBC concentrations. The results reveal higher pollutant levels on AVE KR 9, correlating with changes in oscillometry values post-travel. Cyclists exhibited differing pre- and post-travel values compared to bus and minivan users, suggesting aerobic exercise mitigates pollutant impacts. However, no statistically significant spirometry or impulse oscillometry variations were observed among routes or modes. Public transport and minivan users showed greater PM2.5 and eBC exposure, yet no significant changes associated with environmental contaminants were found in respiratory function values. These findings underscore the importance of further research on pollutant effects and respiratory health in urban environments, particularly concerning different transport modes.

Evaluating Residual Strength of RCC Tanks Affected by ASR Using NDT Methods

This case study focuses on the groundwater level tanks (GLR) located in Netra Village, 35 km from Jodhpur, on Nagour Road. The investigation revealed that people and livestock in the area were grappling with water scarcity issues. Non-Destructive Testing (NDT) methods were employed to assess the residual strength of three tanks in the region. The results showed that Tank-1 had a residual strength of 35.41%, Tank-2 had 63.08%, and Tank-3 had 53.8%. Alkali-silica reaction (ASR) poses a significant threat to the structural integrity of concrete tanks, necessitating accurate and non-destructive methods for residual strength assessment. This research paper delves into the effectiveness of NDT methods, specifically rebound hammer and Ultrasonic Pulse Velocity (UPV), in evaluating the residual strength of ASR-affected tanks. Through an extensive review of literature, case studies, and experimental data, this study aims to shed light on the practical application of rebound hammer and UPV for ASR assessment in tank structures. The paper discusses the principles, advantages, and limitations of each NDT method, emphasizing their ability to detect ASR-induced damage and predict the remaining structural capacity of tanks. Moreover, the research addresses the challenges associated with implementing rebound hammer and UPV techniques in ASR-affected environments, offering recommendations for enhancing their reliability and accuracy. By harnessing the combined capabilities of rebound hammer and UPV, engineers and asset managers can make well-informed decisions regarding the maintenance, repair, and retrofitting of ASR-affected tanks, ensuring their long-term safety and functionality in critical infrastructure applications.

Spatial–Temporal Analysis-Based Video Quality Assessment: A Two-Stream Convolutional Network Approach

In system processing, video inevitably suffers from distortion, which leads to quality degradation and affects the user experience. Therefore, it is of great importance to design an accurate and effective objective video quality assessment (VQA) method. In this paper, by considering the multi-dimensional characteristics for video and visual perceptual mechanism, a two-stream convolutional network for VQA is proposed based on spatial–temporal analysis, named TSCNN-VQA. Specifically, for feature extraction, TSCNN-VQA first extracts spatial and temporal features by two different convolutional neural network branches, respectively. After that, the spatial–temporal joint feature fusion is constructed to obtain the joint spatial–temporal features. Meanwhile, the TSCNN-VQA also integrates an attention module to guarantee that the process conforms to the mechanism that the visual system perceives video information. Finally, the overall quality is obtained by non-linear regression. The experimental results in both the LIVE and CSIQ VQA datasets show that the performance indicators obtained by TSCNN-VQA are higher than those of existing VQA methods, which demonstrates that TSCNN-VQA can accurately evaluate video quality and has better consistency with the human visual system.

Developing a smart green supplier risk assessment system integrating natural language processing and life cycle assessment based on AHP framework: An empirical study

Global carbon reduction drives the transformations of corporations, emphasizing carbon and environmental impact within supply chains. A critical challenge in risk management is converting unstructured data into quantifiable information, and ruining the conversion often leads to ineffective risk assessments. While artificial intelligence is increasingly used in supply chain risk management, previous studies seldom integrate text analysis for risk extraction and lack accurate risk assessment methods. This study pioneers an intelligent green supplier risk assessment system integrating natural language processing (NLP) and life cycle assessment (LCA) based on an analytic hierarchy process (AHP) framework. The proposed approach utilizes NLP keyword extraction to identify and assess risk and uses LCA to uncover environmental risks, minimizing negative impacts. The text classification model attains 81.7 % accuracy, ensuring a trustworthy risk assessment system. The proposed method could significantly improve risk analysis efficiency, foster sustainable procurement, and elevate enterprise competitiveness in sustainable business practices.

The distribution of global tidal marshes from Earth observation data

AbstractAimTidal marsh ecosystems are heavily impacted by human activities, highlighting a pressing need to address gaps in our knowledge of their distribution. To better understand the global distribution and changes in tidal marsh extent, and identify opportunities for their conservation and restoration, it is critical to develop a spatial knowledge base of their global occurrence. Here, we develop a globally consistent tidal marsh distribution map for the year 2020 at 10‐m resolution.LocationGlobal.Time period2020.Major taxa studiedTidal marshes.MethodsTo map the location of the world's tidal marshes at 10‐m resolution, we applied a random forest classification model to Earth observation data from the year 2020. We trained the classification model with a reference dataset developed to support distribution mapping of coastal ecosystems, and predicted the spatial distribution of tidal marshes between 60° N and 60° S. We validated the tidal marsh map using standard accuracy assessment methods, with our final map having an overall accuracy score of 0.85.ResultsWe estimate the global extent of tidal marshes in 2020 to be 52,880 km2 (95% CI: 32,030 to 59,780 km2) distributed across 120 countries and territories. Tidal marsh distribution is centred in temperate and Arctic regions, with nearly half of the global extent of tidal marshes occurring in the temperate Northern Atlantic (45%) region. At the national scale, over a third of the global extent (18,510 km2; CI: 11,200–20,900) occurs within the USA.Main conclusionsOur analysis provides the most detailed spatial data on global tidal marsh distribution to date and shows that tidal marshes occur in more countries and across a greater proportion of the world's coastline than previous mapping studies. Our map fills a major knowledge gap regarding the distribution of the world's coastal ecosystems and provides the baseline needed for measuring changes in tidal marsh extent and estimating their value in terms of ecosystem services.

BDHE-Net: A Novel Building Damage Heterogeneity Enhancement Network for Accurate and Efficient Post-Earthquake Assessment Using Aerial and Remote Sensing Data

Accurate and efficient post-earthquake building damage assessment methods enable key building damage information to be obtained more quickly after an earthquake, providing strong support for rescue and reconstruction efforts. Although many methods have been proposed, most have limited effect on accurately extracting severely damaged and collapsed buildings, and they cannot meet the needs of emergency response and rescue operations. Therefore, in this paper, we develop a novel building damage heterogeneity enhancement network for pixel-level building damage classification of post-earthquake unmanned aerial vehicle (UAV) and remote sensing data. The proposed BDHE-Net includes the following three modules: a data augmentation module (DAM), a building damage attention module (BDAM), and a multilevel feature adaptive fusion module (MFAF), which are used to alleviate the weight deviation of intact and slightly damaged categories during model training, pay attention to the heterogeneous characteristics of damaged buildings, and enhance the extraction of house integrity contour information at different resolutions of the image. In addition, a combined loss function is used to focus more attention on the small number of severely damaged and collapsed classes. The proposed model was tested on remote sensing and UAV images acquired from the Afghanistan and Baoxing earthquakes, and the combined loss function and the role of the three modules were studied. The results show that compared with the state-of-the-art methods, the proposed BDHE-Net achieves the best results, with an F1 score improvement of 6.19–8.22%. By integrating the DBA, BDAM, and MFAF modules and combining the loss functions, the model’s classification accuracy for severely damaged and collapsed categories can be improved.

Acaricide resistance status of livestock ticks from East and West Africa and in vivo efficacy of acaricides to control them

Through a collaborative effort across six Sub-Saharan African countries, using recognized international assessment techniques, 23 stocks of three tick species (Rhipicephalus microplus, Rhipicephalus appendiculatus and Amblyomma variegatum) of economic importance for rural small holder farming communities from East and West Africa were collected from cattle, and evaluated in in vitro larval packet tests (LPT). The results demonstrated medium to high resistance to chlorfenvinphos and amitraz across species. Rhipicephalus microplus demonstrated high level alpha-cypermethrin and cypermethrin resistance. Stocks of A. variegatum (West Africa) and R. appendiculatus (Uganda) demonstrated medium level ivermectin resistance.The four least susceptible stocks (East and West African R. microplus, A. variegatum and R. appendiculatus) were taken into in vivo controlled cattle studies where fipronil was found effective against West and East African R. microplus isolates although persistent efficacy failed to reach 90%. Cymiazole and cypermethrin, and ivermectin based acaricides were partially effective against R. microplus without persistent efficacy. Flumethrin spray-on killed A. variegatum within 72 h for up to 10 days posttreatment, however product application was directly to tick attachment sites, which may be impractical under field conditions. A flumethrin pour-on formulation on goats provided persistent efficacy against A. variegatum for up to one-month. Therapeutic control was achieved against R. appendiculatus through weekly spraying cattle with flumethrin, amitraz or combined cymiazole and cypermethrin. A fipronil pour-on product offered four-week residual control against R. appendiculatus (with slow onset of action).Few studies have assessed and directly compared acaricidal activity in vitro and in vivo. There was some discordance between efficacy indicated by LPT and in vivo results. This observation calls for more research into accurate and affordable assessment methods for acaricide resistance.No single active or product was effective against all three tick species, emphasising the need for the development of alternative integrated tick management solutions.

Comparison of treatment adherence among TB patients with and without COVID-19 in South India

AimThe present study aimed to compare the adherence to anti-TB treatment among post-COVID-19 pulmonary TB cases and TB patients without a history of COVID-19 in South India. MethodsA prospective, cross sectional study was conducted in all drug-sensitive pulmonary tuberculosis patients in National TB Elimination Program. The research investigates the impact of the COVID-19 pandemic on TB management, considering both subjective and objective measures of adherence. Data were collected using a validated instrument for subjective assessment and urine metabolite testing for objective evaluation. ResultsThe results reveal significant differences (p < 0.05) between subjective and objective adherence measures, emphasizing the need for accurate and comprehensive assessment methods. However, there is no statistically significant difference (p > 0.05) in adherence to anti-TB treatment among post-COVID-19 pulmonary TB cases and TB patients without a history of COVID-19 in South India. The reason for non-adherence in both groups were ATT side effects, loss of daily wages, forgetting to take medication and lazy to take medications. ConclusionThe study concluded that there is no statistically significant difference (p > 0.05) in adherence to anti-TB treatment among post-COVID-19 pulmonary TB cases and TB patients without a history of COVID-19.

Landslide Hazard Assessment for Wanzhou Considering the Correlation of Rainfall and Surface Deformation

The landslide hazard assessment plays a crucial role in landslide risk mitigation and land use planning. The result of landslide hazard assessment corrected by surface deformation, obtained through time-series InSAR, has usually proven to have good application capabilities. However, the issue lies in the uncertainty of InSAR results, where some deformations cannot be calculated, and some are not true deformations. This uncertainty of InSAR results will lead to errors in landslide hazard assessment. Here, we attempt to evaluate landslide hazards by considering combined rainfall and surface deformation. The main objective of this research was to mitigate the impact of bias and explore the accurate landslide hazard assessment method. A total of 201 landslides and 11 geo-environment factors were utilized for landslide susceptibility assessment by support vector machine (SVM) model in Wanzhou District, Three Gorges Reservoir Area (TGRA). The preliminary hazard is obtained by analyzing the statistical data of landslides and rainfall. Based on the SAR image data of Sentinel-1A satellites from September 2019 to October 2021, the SBAS-InSAR method was used to analyze surface deformation. The correlation between surface deformation and rainfall was analyzed, and the deformation factor variables were applied to landslide hazard assessment. The research results demonstrate that the error caused by the uncertainty of InSAR results can be effectively avoided by analyzing the relationship between rainfall and surface deformation. Our results can effectively adjust and correct the hazard results and eliminate the errors in the general hazard assessment. Our proposed method can be used to assess the landslide hazard in more detail and provide a reference for fine risk management and control.

A novel customizing knowledge graph evaluation method for incorporating user needs

Knowledge graphs are now widely used in various domains, including Question-and-answer systems, intelligent search and recommendation systems, and intelligent decision-making systems. However, knowledge graphs inevitably contain inaccurate and incomplete knowledge during the creation process, which leads to a reduction in the usefulness of knowledge graphs. Therefore, to assess the usefulness of knowledge graphs based on specific application requirements, quality assessment is particularly important. Among them, accuracy assessment, as a necessary dimension, reflects the degree of correctness of the triples. However, in the actual assessment process, the existing assessment methods do not consider the user’s needs and do not implement the concept of “Fitness for Use”. Meanwhile, it takes a lot of labor cost to evaluate the accuracy of large-scale knowledge graphs. Therefore, to ensure the accuracy of the assessment in a cost-saving way while meeting the needs of users, we propose and implement a novel accuracy assessment method that focuses on the requirements of users by designing an effective sampling method to obtain accurate assessment results that are more practical and instructive for users. Finally, the performance of our proposed method is evaluated by comparing it with the real accuracy rate, and the experimental results show that the accuracy rate obtained by the proposed method is very close to the real accuracy rate, and the sample size is minimized.

An integrated uncertainty analysis method for the risk assessment of hydrogen refueling stations

In order to assess more rigorously the risks of hydrogen refueling stations (HRSs) with consideration of uncertainties, an uncertainty analysis method integrating the Hydrogen Plus Other Alternative Fuels Risk Assessment Models (HyRAM+), Monte Carlo simulation, and Latin Hypercube Sampling (LHS) method is proposed in this paper. In order to discuss and demonstrate the potential ability of the proposed method, the leak risk of a 35 MPa dispenser at a HRS is assessed. The results indicate that the proposed method can effectively quantify the leak risk uncertainty of the dispenser at a HRS. These uncertainties considered have significant effects on the leak risks of the dispenser, and the risk contribution of jet fire is much larger than that from explosion. Moreover, the leak risk of the dispenser is underestimated by the HyRAM+, so it should be used by combining a reliable safety factor. Current work can provide a more accurate method for the risk assessment and performance-based design of HRSs.

Progress and Prospects of Elastography Techniques in the evaluation of fibrosis in chronic liver disease

The precise assessment of liver fibrosis degree is imperative for monitoring and managing chronic liver diseases. Traditionally, liver biopsy and quantitative blood biomarkers have been the mainstays for fibrosis assessment, albeit each with its inherent limitations. However, the emergence of elastography presents a promising non-invasive alternative, potentially supplanting or complementing traditional biomarkers. In contrast to conventional biomarkers, elastography offers several advantages. Firstly, it is non-invasive, sidestepping the risks and discomfort associated with liver biopsy. Secondly, this technique provides real-time and intuitive fibrosis assessments, characterized by user-friendly operation and high reproducibility. Thirdly, elastography excels in diagnosing moderate to severe fibrosis, vital for determining treatment strategies and monitoring disease progression. Notably, MRE stands out as the most accurate non-invasive method for fibrosis assessment, especially suitable for advanced fibrotic stages. This article provides an overview of the advancements in elastography as a viable quantitative biomarker for chronic liver disease fibrosis burden.

A physics-based PSO-BPNN model for civil aircraft noise assessment

With the increasingly severe problem of aircraft noise pollution around airports, it is urgent to explore accurate and effective aircraft noise assessment methods. This article proposes a physics-based PSO-BPNN model based on the European Civil Aviation Conference (ECAC) best practice model, backpropagation neural network (BPNN), and particle swarm optimization (PSO) to enrich the methodology system of aircraft noise assessment. The primary modeling process is as follows. Firstly, based on airport parameters, flight information, and other data, the theoretical noise levels of ground monitoring point are calculated using the ECAC dynamic model. Then, a dataset containing measured noise values, theoretical noise values, trajectory data, and meteorological data is constructed to train the physics-based BPNN model, in order to correct the theoretical noise level. Finally, the PSO algorithm is introduced to optimize the parameters of the BPNN model and the construction of the physics-based PSO-BPNN model is completed. By taking Hefei Xinqiao International Airport (HFE) as a research case, the experimental results show that the physics-based PSO-BPNN model, which combines best practice model and machine learning model, demonstrates better performance than the ECAC model and physics-based BPNN model because of its balance between stability and flexibility. In the validation set, the error of 74.77 % of the predicted results was within ±3 dB(A), and the coefficient of determination R2 between all predicted values and measured values reached 0.9450, which indicates the physics-based PSO-BPNN model a potential aircraft noise assessment solution.

Features extraction of point clouds based on Otsu’s algorithm

Currently, a point cloud extraction method based on geometric features requires the configuration of two essential parameters: the neighborhood radius within the point cloud and the criterion for feature threshold selection. This article addresses the issue of manual selection of feature thresholds and proposes a feature extraction method for 3D point clouds based on the Otsu algorithm. Firstly, the curvature value of each point cloud is calculated based on the r-neighborhood of the point cloud data. Secondly, the Otsu algorithm is improved by taking the curvature values as input for the maximum inter-class variance method. The optimal segmentation threshold is obtained based on the Otsu algorithm to divide the point cloud data into two parts. Point cloud data whose curvature is greater than or equal to the threshold is extracted as feature point data. In order to verify the reliability of the algorithm presented in this paper, a method for accuracy assessment of regular point cloud data is proposed. Additionally, comparative analysis was conducted on data with varying point cloud densities and on data contaminated with Gaussian white noise using multiple methods. Experimental results show that the proposed algorithm achieves good extraction results for data with 90% simplification rate and low noise.

Non-Destructive Near-Infrared Technology for Efficient Cannabinoid Analysis in Cannabis Inflorescences.

In the evolving field of cannabis research, scholars are exploring innovative methods to quantify cannabinoids rapidly and non-destructively. This study evaluates the effectiveness of a hand-held near-infrared (NIR) device for quantifying total cannabidiol (total CBD), total delta-9-tetrahydrocannabinol (total THC), and total cannabigerol (total CBG) in whole cannabis inflorescences. Employing pre-processing techniques, including standard normal variate (SNV) and Savitzky-Golay (SG) smoothing, we aim to optimize the portable NIR technology for rapid and non-destructive cannabinoid analysis. A partial least-squares regression (PLSR) model was utilized to predict cannabinoid concentration based on NIR spectra. The results indicated that SNV pre-processing exhibited superior performance in predicting total CBD concentration, yielding the lowest root mean square error of prediction (RMSEP) of 2.228 and the highest coefficient of determination for prediction (R2P) of 0.792. The ratio of performance to deviation (RPD) for total CBD was highest (2.195) with SNV. In contrast, raw data exhibited the least accurate predictions for total THC, with an R2P of 0.812, an RPD of 2.306, and an RMSEP of 1.651. Notably, total CBG prediction showed unique characteristics, with raw data yielding the highest R2P of 0.806. SNV pre-processing emerges as a robust method for precise total CBD quantification, offering valuable insights into the optimization of a hand-held NIR device for the rapid and non-destructive analysis of cannabinoid in whole inflorescence samples. These findings contribute to ongoing efforts in developing portable and efficient technologies for cannabinoid analysis, addressing the increasing demand for quick and accurate assessment methods in cannabis cultivation, pharmaceuticals, and regulatory compliance.

An analytical method for free vibrations of the fuel rod with non-uniform mass of small modular reactor

The intricate internal structure of fuel rods results in a non-uniform mass distribution, making it imperative to employ analytical methods for accurate assessment. The study utilizes Euler beam theory to derive the transverse vibration equation for beams with varying mass distribution. The approach involves transforming the non-uniform mass beam into a multi-segment beam with concentrated mass points. Modal function relationships between adjacent uniform segments are established based on continuous conditions at connection points. This transformation leads to the conversion of the variable coefficient differential equation into a nonlinear matrix equation. The Newton-Raphson method is then applied to calculate the characteristic equation and mode shapes, essential for determining natural frequencies. To validate precision, the results obtained are compared with those derived from the finite element method. Furthermore, the developed method is employed to assess the impact of gas plenum location and length on the natural frequency of fuel rods. The proposed methodology serves as a rapid design tool, particularly beneficial during the design phase of fuel rods with non-uniform mass distribution, aiding in configuring structural aspects effectively.

Goniometric accuracy and compensation methods for full-space laser positioning systems

The accurate large-scale positioning system is a novel full-space laser positioning system that has the advantages of automatic and multi-node parallel measurement and sub-millimeter positioning accuracy, and it has become an important component in the digital manufacturing of large equipment. Goniometric accuracy is particularly important for the evaluation of system function and accuracy improvement. An improved goniometric accuracy calibration method is proposed to assess the goniometric accuracy by using multiple measurement points to fit the mapping relationship between the goniometric measurement of the system and the goniometric measurement of the multi-tooth indexing table. The feasibility of the accuracy assessment method is verified by establishing a geometric model and performing modeling and simulation analysis. The method is verified using an all-space laser positioning system, and the dynamic performance of the base station is optimized to improve the overall goniometric accuracy. The maximum value of the goniometric error is 0.008″ and the goniometric accuracy is 0.026″ after fitting the curve by the mapping relation, demonstrating high stability, and the fluctuation of the goniometric error after optimization is improved from the original ±15″ to ±7.5″, verifying the effectiveness of the goniometric compensation method. This method solves the problem of coaxiality calibration in goniometric evaluation and provides the basis for the traceability of the goniometric accuracy.

Evaluation of soil pesticide leaching to groundwater using undisturbed lysimeter: development of the pesticide groundwater leaching scoring system (PLS).

Groundwater pesticide safety management is essential for providing consistently safe water for humans, but such management is limited globally. In this study, we developed an accurate and convenient exposure assessment method for the safety management of pesticides in groundwater by conducting a lysimeter experiment to evaluate the leaching of 11 pesticides into groundwater. During the experimental period, flutoalanil and oxadiazon had the highest cumulative leaching amounts, 603.7 and 83.5ng, respectively. Comparative analysis of existing groundwater exposure prediction indices, including the GUS, LEACH, modified LEACH, Hornsby index, and GLI showed no correlations with the measured data (p > 0.05). To enhance the accuracy of the assessment method, we used lysimeter data and principal component analysis to determine the main factors affecting groundwater leaching, and developed the "pesticide groundwater leaching scoring system" (PLS). The soil and water half-life, which had the greatest positive impact on groundwater leaching, was set as a 10-point indicator, whereas log P was set as a 1-point indicator. In contrast, solubility in water was determined as a 5-point indicator, and organic carbon partition coefficient and vapor pressure were determined as 2.5-point indicators owing to their negative relationship. The correlation coefficient was 0.670, indicating a significant correlation with the lysimeter data (p < 0.05). Using our scoring system, we ranked 376 pesticides. As an exposure assessment method developed using actual data, the PLS is expected to be applicable to groundwater safety management.

The Role of Wood Density Variation and Biomass Allocation in Accurate Forest Carbon Stock Estimation of European Beech (Fagus sylvatica L.) Mountain Forests

The European beech (Fagus sylvatica L.) is one of the most common tree species in Romania, with importance both economically and environmentally. Accurate methods of biomass assessment at the tree compartment level (i.e., stump, stem, branches, and leaves) are necessary for carbon stock estimation. Wood density (WD) is an important factor in determining biomass and, ultimately, the tree’s carbon content. The average tree density was found to be 578.6 kg/m3. For this study, WD was evaluated by the weighting method related to tree volume. Also, to investigate a practical approach to determining the weighted wood density (WWDst), models were run using density at the base of the tree (WDBase), density at breast height level using discs (WDDBH), the wood core density (WDic), and the diameter at breast height (DBH) as predictors. The biomass assessment was conducted using different model evaluations for WWDst as well as allometric equations using the destructive method. From the results, it was noted that using the WWDst, the total biomass was underestimated by −0.7% compared to the biomass measured in the field. For allometric equations that included DBH and tree height as independent variables, the explained variability was around 99.3% for total aboveground biomass (AGBtotal), while it was 97.9% for allometric function using just the DBH. Overall, the distribution of biomass across different compartments was as follows: 73.5% in stems, 23.8% in branches, 1.9% in stumps, and 1.3% in leaves. The study findings offer valuable insights into WD, biomass distribution among different components, and biomass allometric quantification in natural beech forest environments in mountainous areas.