Parameter Estimation Research Articles

Comparing sensitivities of groundwater head to variations in hydraulic parameters under boundary conditions of river stage rise and tidal variation

The sensitivity of hydraulic head responses to spatially distributed hydraulic parameters is essential for uncertainty analysis, inverse modeling, and parameter estimation and interpretation. This study formulates the Fréchet sensitivity kernel of hydraulic head responses to a suddenly rising boundary and a sinusoidal head fluctuation boundary to variation of spatially distributed hydraulic parameters in a semi-infinite, one-dimensional (1-D), confined aquifer, and it then derives analytical solutions. Different from previous studies that derived expressions for Fréchet kernels in the time domain for a 2-D pumping test, this study is the first to derive the closed-form Fréchet kernels in time and frequency domains for a semi-infinite, 1-D, confined aquifer. This study uses the Fréchet kernels to investigate the nature of singularities in the spatial sensitivity functions around the observation location and boundary. The information content revealed by observation of head change or head fluctuation amplitude at a given specified location and time (or frequency) under the above two boundary conditions is different. When comparing Fréchet sensitivity kernels across various times or periods, multi-frequency information, much like multi-time information, can be instrumental for hydrogeological parameter inversion. The explicit-form Fréchet sensitivity kernels also identify the optimal time or period for obtaining measurements.

Fast Bayesian Functional Regression for Non-Gaussian Spatial Data

Functional generalized linear models (FGLM) have been widely used to study the relations between non-Gaussian response and functional covariates. However, most existing works for FGLM assume independence among observations and therefore they are of limited applicability for correlated data. A particularly important example is spatial functional data, where we observe functions over spatial domains, such as the age population curve or temperature curve at each areal unit. In this paper, we extend FGLM by incorporating spatial random effects. Especially, we study the relationship between the non-Gaussian response variable and functional covariates that are spatially observed. However, such model has computational and inferential challenges. The high-dimensional spatial random effects cause the slow mixing of Markov chain Monte Carlo (MCMC) algorithms. Furthermore, spatial confounding can lead to bias in parameter estimates and inflate their variances. To address these issues, we propose an efficient Bayesian method using a sparse reparameterization of high-dimensional random effects. We also study the average coverage probabilities of the credible intervals of functional parameters. We apply our methods to simulated and real data examples, including malaria incidence data and US COVID-19 data. The proposed method is fast while providing accurate functional estimates.

Incubation Period and Serial Interval of Mpox in 2022 Global Outbreak Compared with Historical Estimates.

Understanding changes in the transmission dynamics of mpox requires comparing recent estimates of key epidemiologic parameters with historical data. We derived historical estimates for the incubation period and serial interval for mpox and contrasted them with pooled estimates from the 2022 outbreak. Our findings show the pooled mean infection-to-onset incubation period was 8.1 days for the 2022 outbreak and 8.2 days historically, indicating the incubation periods remained relatively consistent over time, despite a shift in the major mode of transmission. However, we estimated the onset-to-onset serial interval at 8.7 days using 2022 data, compared with 14.2 days using historical data. Although the reason for this shortening of the serial interval is unclear, it may be because of increased public health interventions or a shift in the mode of transmission. Recognizing such temporal shifts is essential for informed response strategies, and public health measures remain crucial for controlling mpox and similar future outbreaks.

The impact of international trade on economic growth in Somalia

The main objective of this study was to examine the impact of international trade on economic growth in Somalia. This objective was accomplished with the support of four distinct objectives. To investigate the impact of exports on economic growth in Somalia. To examine how imports impact economic growth in Somalia. To discover how the trade openness phenomenon effects economic growth in Somalia. To establish a solid foundation for public policy founded on an innovative international trade approach that considerably boosts the country's economic growth over the long term. In Somalia, international trade has become increasingly important, driven by imports surpassing exports, which underscores the primary objective of this study. Therefore, the study employed the least squares (OLS) method to analyze the determinants of international trade in Somalia from 1960 to 2022. Our analysis predominantly centered on explanatory variables such as exports, imports, and trade openness. According to the study's findings, exports and imports have a positive relationship and have a statistically significant impact on economic growth. This means that a one-unit increase in exports and imports leads to an increase in economic growth in Somalia. The exports and imports have revealed a number of characteristics that are positive for economic growth. Moreover, the study's findings indicate an adverse relationship between trade openness and economic growth. Trade openness has a probability that it is statistically significant with a negative coefficient within the parameter estimation model. A negative coefficient suggests that the dependent variable tends to decrease with an increase in the independent variable. In conclusion, the study's findings contributed to the policy recommendations, which should be taken into account by decision-makers.

Transgenerational effects of the maternal gestational environment on the post-natal performance of beef cattle: A reaction norm approach.

In tropical beef cattle production systems, animals are commonly raised on pastures, exposing them to potential stressors. The end of gestation typically overlaps with a dry period characterized by limited food availability. Late gestation is pivotal for fetal development, making it an ideal scenario for inter- and transgenerational effects of the maternal gestational environment. Intergenerational effects occur due to exposure during gestation, impacting the development of the embryo and its future germline. Transgenerational effects, however, extend beyond direct exposure to the subsequent generations. The objective of the present study was to verify these effects on the post-natal performance of zebu beef cattle. We extended the use of a reaction norm model to identify genetic variation in the animals' responses to transgenerational effects. The inter- and transgenerational effects were predominantly positive (-0.09% to 19.74%) for growth and reproductive traits, indicating improved animal performance on the phenotypic scale in more favourable maternal gestational environments. Additionally, these effects were more pronounced in the reproductive performance of females. On average, the ratio of direct additive genetic variances of the slope and intercept of the reaction norm ranged from 1.23% to 3.60% for direct and from 10.17% to 11.42% for maternal effects. Despite its relatively modest magnitude, this variation proved sufficient to prompt modifications in parameter estimates. The average percentage variation of direct heritability estimates ranged from 19.3% for scrotal circumference to 33.2% for yearling weight across the environmental descriptors evaluated. Genetic correlations between distant environments for the studied traits were generally high for direct effects and far from unity for maternal effects. Changes in EBV rankings of sires across different gestational environments were also observed. Due to the multifaceted nature of inter- and transgenerational effects of the maternal gestational environment on various traits of beef cattle raised under tropical pasture conditions, they should not be overlooked by producers and breeders. There were differences in the specific response of beef cattle to variations in the quality of the maternal gestational environment, which can be partially explained by transgenerational epigenetic inheritance. Adopting a reaction norm model to capture a portion of the additive variance induced by inter- or transgenerational effects could be an alternative for future research and animal genetic evaluations.

Joint Modeling of Gene-Environment Correlations and Interactions Using Polygenic Risk Scores in Case-Control Studies.

Polygenic risk scores (PRS) are rapidly emerging as a way to measure disease risk by aggregating multiple genetic variants. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in a wide variety of settings. We develop an efficient method for simultaneously modeling gene-environment correlations and interactions using PRS in case control studies. We use a logistic-normal regression modeling framework to specify the disease risk and PRS distribution in the underlying population and propose joint inference across the two models using the retrospective likelihood of the case-control data. Extensive simulation studies demonstrate the flexibility of the method in trading-off bias and efficiency for the estimation of various model parameters compared to the standard logistic regression or a case-only analysis for gene-environment interactions, or a control-only analysis for gene-environment correlations. Finally using simulated case-control data sets within the UK Biobank study, we demonstrate the power of our method for its ability to recover results from the full prospective cohort for the detection of an interaction between long-term oral contraceptive use and PRS on the risk of breast cancer. This method is computationally efficient and implemented in a user-friendly R package.

Optimal quantum metrology of two-photon absorption

Two-photon absorption (TPA) is a nonlinear optical process with wide-ranging applications from spectroscopy to super-resolution imaging. Despite this, the precise measurement and characterisation of TPA parameters are challenging due to their inherently weak nature. We study the potential of single-mode quantum light to enhance TPA parameter estimation through the quantum Fisher information (QFI). Discrete variable quantum states (defined to be a finite superposition of Fock states) are optimised to maximise the QFI for given absorption, revealing a quantum advantage compared to both the coherent state (classical) benchmark and the single-mode squeezed vacuum state. For fixed average energy n¯∈2N , the Fock state is shown to be optimal for large TPA parameters, while a superposition of vacuum and a particular Fock state is optimal for small absorption for all n¯ . This differs from single-photon absorption where the Fock state is always optimal. Notably, photon counting is demonstrated to offer optimal or nearly optimal performance compared to the QFI bound for all levels of TPA parameters for the optimised quantum probes, and their quantum advantage is shown to be robust to single-photon loss. Our findings provide insight into known limiting behaviours of Gaussian probes and their different FI scalings under photon counting ( ∝n¯2 for squeezed vacuum states versus n¯3 for coherent states). The squeezed state outperforms coherent states for small TPA parameters but underperforms in the intermediate regime, becoming comparable in the large absorption limit. This can be explained through fundamental differences between behaviours of even and odd number Fock states: the former’s QFI diverges in both large and small absorption limits, while the latter diverges only in the small absorption limit, dominating at intermediate scales.

A case study of using artificial neural networks to predict heavy metal pollution in Lake Iznik.

Artificial neural networks offer a viable route in assessing and understanding the presence and concentration of heavy metals that can cause dangerous complications in the wider context of water quality prediction for the sustainability of the ecosystem. In order to estimate the heavy metal concentrations in Iznik Lake, which is an important water source for the surrounding communities, characterization data were taken from five different water sources flowing into the lake between 2015 and 2021. These characterization results were evaluated with IBM SPSS Statistics 23 software, with the addition of the lake water quality system. For this purpose, seven distinct physicochemical parameters were measured and monitored in Karasu, Kırandere, Olukdere and Sölöz water sources flowing into the lake, to serve as input data. Concentration levels of 15 distinct heavy metals in Karsak Stream originating from the lake were as the output. Specifically, Sn for Karasu (0.999), Sb for Kırandere (1.000), Cr for Olukdere (1.000) and Pb and Se for Sölöz (0.995) indicate parameter estimation R2 coefficients close to 1.000. Sn stands out as the common heavy metal parameter with best estimation prospects. Given the importance of the independent variable in estimating heavy metal pollution, conductivity, COD, COD and temperature stood out as the most effective parameters for Karasu, Olukdere, Kırandere and Sölöz, respectively. The ANN model emerges as a good prediction tool that can be used effectively in determining the heavy metal pollution in the lake as part of the efforts to protect the water budget of Lake Iznik and to eliminate the existing pollution.

Two-phase data science framework for compensation of the friction force in CNC machines

ABSTRACT This study proposes a two-phase data science framework for the friction force and parameter estimation of the hysteresis effect segment in the servo-control systems of precision machines. The first phase uses an exponential-based friction force model to identify the model parameters by an autoregressive model and Z-transform. The second phase uses symbolic regression for the residual analysis to enhance the friction force estimation. An empirical study of three types of CNC machines under different working conditions is conducted to validate the two-phase data science framework and identify the change of the machining displacement considered to be a critical factor affecting friction force. The exponential-based model successfully eliminates the circular spike error caused by the friction force in the tapping center machine. The results indicate that the proposed two-phase framework improves mean absolute error by 5.6% on average in the tapping center, 6.5% in the milling machine and 7.6% in the turning and milling center, respectively.

Autoencoding Labeled Interpolator, Inferring Parameters from Image and Image from Parameters

The Event Horizon Telescope (EHT) provides an avenue to study black hole accretion flows on event-horizon scales. Fitting a semianalytical model to EHT observations requires the construction of synthetic images, which is computationally expensive. This study presents an image generation tool in the form of a generative machine-learning model, which extends the capabilities of a variational autoencoder. This tool can rapidly and continuously interpolate between a training set of images and can retrieve the defining parameters of those images. Trained on a set of synthetic black hole images, our tool showcases success in interpolating both black hole images and their associated physical parameters. By reducing the computational cost of generating an image, this tool facilitates parameter estimation and model validation for observations of black hole systems.

Signal-to-Noise Ratio in Estimating and Testing the Mediation Effect: Structural Equation Modeling versus Path Analysis with Weighted Composites.

Mediation analysis plays an important role in understanding causal processes in social and behavioral sciences. While path analysis with composite scores was criticized to yield biased parameter estimates when variables contain measurement errors, recent literature has pointed out that the population values of parameters of latent-variable models are determined by the subjectively assigned scales of the latent variables. Thus, conclusions in existing studies comparing structural equation modeling (SEM) and path analysis with weighted composites (PAWC) on the accuracy and precision of the estimates of the indirect effect in mediation analysis have little validity. Instead of comparing the size on estimates of the indirect effect between SEM and PAWC, this article compares parameter estimates by signal-to-noise ratio (SNR), which does not depend on the metrics of the latent variables once the anchors of the latent variables are determined. Results show that PAWC yields greater SNR than SEM in estimating and testing the indirect effect even when measurement errors exist. In particular, path analysis via factor scores almost always yields greater SNRs than SEM. Mediation analysis with equally weighted composites (EWCs) also more likely yields greater SNRs than SEM. Consequently, PAWC is statistically more efficient and more powerful than SEM in conducting mediation analysis in empirical research. The article also further studies conditions that cause SEM to have smaller SNRs, and results indicate that the advantage of PAWC becomes more obvious when there is a strong relationship between the predictor and the mediator, whereas the size of the prediction error in the mediator adversely affects the performance of the PAWC methodology. Results of a real-data example also support the conclusions.

On the optimality of the radical-pair quantum compass

Quantum sensing enables the ultimate precision attainable in parameter estimation. Circumstantial evidence suggests that certain organisms, most notably migratory songbirds, also harness quantum-enhanced magnetic field sensing via a radical-pair-based chemical compass for the precise detection of the weak geomagnetic field. However, what underpins the acuity of such a compass operating in a noisy biological setting, at physiological temperatures, remains an open question. Here, we address the fundamental limits of inferring geomagnetic field directions from radical-pair spin dynamics. Specifically, we compare the compass precision, as derived from the directional dependence of the radical-pair recombination yield, to the ultimate precision potentially realisable by a quantum measurement on the spin system under steady-state conditions. To this end, we probe the quantum Fisher information and associated Cramér–Rao bound in spin models of realistic complexity, accounting for complex inter-radical interactions, a multitude of hyperfine couplings, and asymmetric recombination kinetics, as characteristic for the magnetosensory protein cryptochrome. We compare several models implicated in cryptochrome magnetoreception and unveil their optimality through the precision of measurements ostensibly accessible to nature. Overall, the comparison provides insight into processes honed by nature to realise optimality whilst constrained to operating with mere reaction yields. Generally, the inference of compass orientation from recombination yields approaches optimality in the limits of complexity, yet levels off short of the theoretical optimal precision bounds by up to one or two orders of magnitude, thus underscoring the potential for improving on design principles inherent to natural systems.

Experimental Research on Shipborne SINS Rapid Mooring Alignment with Variance-Constraint Kalman Filter and GNSS Position Updates

Analytical coarse alignment and Kalman filter fine alignment based on zero-velocity are typically used to obtain initial attitude for inertial navigation systems (SINS) on a static base. However, in the shipboard mooring state, the static observation condition is corrupted. This paper presents a rapid alignment method for SINS on swaying bases. The proposed method begins with a coarse alignment technique in the inertial frame to obtain an initial rough attitude. Subsequently, a Kalman filter with position updates is employed to estimate the remaining misalignment error. To enhance the filter estimation performance, an appropriate lower boundary is set to the target states’ variances according to a carefully designed relative convergence index. The variance-constraint Kalman filter (VCKF) approach is proposed in this paper, and the shipborne experiments validate its effectiveness. The results demonstrate that the VCKF approach significantly reduces the time requirement for fine alignment to achieve the same accuracy on a swaying base, from 90 min in the classic Kalman filter to 30 min. Additionally, the parameter estimation performance in the Kalman filter is also improved, particularly in situations where unpredicted external interference is involved during fine alignment.

Study on the Robustness of an Atmospheric Scattering Model under Single Transmittance

When light propagates in a scattering medium such as haze, it is partially scattered and absorbed, resulting in a decrease in the intensity of the light emitted by the imaging target and an increase in the intensity of the scattered light. This phenomenon leads to a significant reduction in the quality of images taken in hazy environments. To describe the physical process of image degradation in haze, the atmospheric scattering model is proposed. However, the accuracy of the model applied to the usual fog image restoration is affected by many factors. In general, fog images, atmospheric light, and haze transmittances vary spatially, which makes it difficult to calculate the influence of the accuracy of parameters in the model on the recovery accuracy. In this paper, the atmospheric scattering model was applied to the restoration of hazed images with a single transmittance. We acquired hazed images with a single transmittance from 0.05 to 1 using indoor experiments. The dehazing stability of the atmospheric scattering model was investigated by adjusting the atmospheric light and transmittance parameters. For each transmittance, the relative recovery accuracy of atmospheric light and transmittance were calculated when they deviated from the optimal value of 0.1, respectively. The maximum parameter estimation deviations allowed us to obtain the best recovery accuracies of 90%, 80%, and 70%.

Quantitative Stylistic Analysis of Middle Chinese Texts Based on the Dissimilarity of Evolutive Core Word Usage

Stylistic analysis enables open-ended and exploratory observation of languages. To fill the gap in the quantitative analysis of the stylistic systems of Middle Chinese, we construct lexical features based on the evolutive core word usage and scheme a Bayesian method for feature parameters estimation. The lexical features are from the Swadesh list, each of which has different word forms along with the language evolution during the Middle Ages. We thus count the varied word of those entries along with the language evolution as the linguistic features. With the Bayesian formulation, the feature parameters are estimated to construct a high-dimensional random feature vector in order to obtain the pair-wise dissimilarity matrix of all the texts based on different distance measures. Finally, we perform the spectral embedding and clustering to visualize, categorize and analyze the linguistic styles of Middle Chinese texts. The quantitative result agrees with the existing qualitative conclusions and furthermore, betters our understanding of the linguistic styles of Middle Chinese from both the inter-category and intra-category aspects. It also helps unveil the special styles induced by the indirect language contact.

SBMLToolkit.jl: a Julia package for importing SBML into the SciML ecosystem.

Julia is a general purpose programming language that was designed for simplifying and accelerating numerical analysis and computational science. In particular the Scientific Machine Learning (SciML) ecosystem of Julia packages includes frameworks for high-performance symbolic-numeric computations. It allows users to automatically enhance high-level descriptions of their models with symbolic preprocessing and automatic sparsification and parallelization of computations. This enables performant solution of differential equations, efficient parameter estimation and methodologies for automated model discovery with neural differential equations and sparse identification of nonlinear dynamics. To give the systems biology community easy access to SciML, we developed SBMLToolkit.jl. SBMLToolkit.jl imports dynamic SBML models into the SciML ecosystem to accelerate model simulation and fitting of kinetic parameters. By providing computational systems biologists with easy access to the open-source Julia ecosystevnm, we hope to catalyze the development of further Julia tools in this domain and the growth of the Julia bioscience community. SBMLToolkit.jl is freely available under the MIT license. The source code is available at https://github.com/SciML/SBMLToolkit.jl.

Antidepressant Effect of Vincamine, Melatonin and their Combination in Experimental Rat Models of Depression

Background: Depression, a form of mood illness, is characterized by a lack of or diminished social life, melancholy, and symptoms linked to an uplifted or depressed mood. Vincamine and Melatonin combination may prove beneficial for treating depression because vincamine and melatonin have already been reported in the treatment of dementia. Dementia is a CNS disorder which is connected to the level of neurotransmitters at the synaptic cleft, which is similar to depression that is why it might be effective in the treatment of depression. Aim: The aim of this research is to conduct an experimental study on the antidepressant activity of Vincamine alone and along with Melatonin and comparison with standard antidepressant drugs. Method: In this study, all animals are trained for the experiment, and then animals are divided into groups according to the treatment protocol. Animals were placed for the rest for 1 week. Vincamine and melatonin were administered to the treated groups on days 8 and 21 for the behavioral analysis using different kinds of models like elevated plus maze, forced swim test, open field test and sucrose preference test. Animals were sacrificed at day 22 for the estimation of biochemical (SOD) and neurotransmitters (Serotonin, Norepinephrine and Dopamine). Results: After extensive investigation, it has been established that Vincamine, alone and in combination with Melatonin, is beneficial in treating depression. Positive results are obtained. The group treated with Vincamine taken per oral route ((10 mg/kg), Melatonin (10 mg/kg), Vincamine + Melatonin (10 mg/kg + 10 mg/kg) and std (10 mg/kg) treatment showed significant result which are followed as for all groups (a) p < 0.0001 vs NC; (b) p < 0.0001 vs disease control; (c) p < 0.05 vs Std (10 mg/kg); (d) p < 0.001V vs M (10 mg/kg); (e) p < 0.0090 vs V+M (10 mg/kg+10 mg/kg). It was demonstrated by a number of behavioral (elevated plus maze, forced swim test, open field test, and sucrose preference test) and biochemical parameters (estimation of neurotransmitters such as serotonin, epinephrine, and dopamine). Vincamine alone and in combination with Melatonin demonstrated positive results (p < 0.0001) in the evaluation of the SOD antioxidant defence system. Also, it significantly attenuated the alteration in locomotor activity, oxidative stress and neurotransmitters in rat brain. result: After extensive investigation, it has been established that Vincamine, alone and in combination with Melatonin, is beneficial in treating depression. Positive results are obtained. The group treated with Vincamine taken per oral route ((10mg/kg), Melatonin (10mg/kg), Vincamine + Melatonin (10mg/kg + 10mg/kg) and std (10mg/kg) treatment showed significant result which are followed as for all groups (a) p < 0.0001 vs NC; (b) p < 0.0001 vs disease control; (c) p < 0.05 vs Std (10 mg/kg); (d) p < 0.001V vs M (10 mg/kg); (e) p < 0.0090 vs V+M (10mg/kg+10mg/kg). It was demonstrated by a number of behavioral (elevated plus maze, forced swim test, open field test, and sucrose preference test) and biochemical parameters (estimation of neurotransmitters such as serotonin, epinephrine, and dopamine). Vincamine alone and in combination with Melatonin demonstrated positive results (p<0.0001) in the evaluation of the SOD antioxidant defence system. Also, significantly attenuated the alteration in locomotor activity, oxidative stress and neurotransmitters in rat brain. Conclusion: It has been concluded that vincamine alone and along with melatonin is effective for the treatment of depression on the basis of behavioural and biochemical parameters. That’s why it might be useful for the future prospect.

Making Predictions Using Poorly Identified Mathematical Models

Many commonly used mathematical models in the field of mathematical biology involve challenges of parameter non-identifiability. Practical non-identifiability, where the quality and quantity of data does not provide sufficiently precise parameter estimates is often encountered, even with relatively simple models. In particular, the situation where some parameters are identifiable and others are not is often encountered. In this work we apply a recent likelihood-based workflow, called Profile-Wise Analysis (PWA), to non-identifiable models for the first time. The PWA workflow addresses identifiability, parameter estimation, and prediction in a unified framework that is simple to implement and interpret. Previous implementations of the workflow have dealt with idealised identifiable problems only. In this study we illustrate how the PWA workflow can be applied to both structurally non-identifiable and practically non-identifiable models in the context of simple population growth models. Dealing with simple mathematical models allows us to present the PWA workflow in a didactic, self-contained document that can be studied together with relatively straightforward Julia code provided on GitHub. Working with simple mathematical models allows the PWA workflow prediction intervals to be compared with gold standard full likelihood prediction intervals. Together, our examples illustrate how the PWA workflow provides us with a systematic way of dealing with non-identifiability, especially compared to other approaches, such as seeking ad hoc parameter combinations, or simply setting parameter values to some arbitrary default value. Importantly, we show that the PWA workflow provides insight into the commonly-encountered situation where some parameters are identifiable and others are not, allowing us to explore how uncertainty in some parameters, and combinations of parameters, regardless of their identifiability status, influences model predictions in a way that is insightful and interpretable.

On duration effects in non-life insurance pricing

AbstractThe paper discusses duration effects on the consistency of mean parameter and dispersion parameter estimators in exponential dispersion families (EDFs) that are the standard models used for non-life insurance pricing. Focus is on the standard generalised linear model assumptions where both the mean and variance, conditional on duration, are linear functions in terms of duration. We derive simple convergence results that highlight consequences when the linear conditional moment assumptions are not satisfied. These results illustrate that: (i) the resulting mean estimators always have a relevant asymptotic interpretation in terms of the duration adjusted actuarially fair premium—a premium that only agrees with the standard actuarial premium using a duration equal to one, given that the expected value is linear in the duration; (ii) deviance based estimators of the dispersion parameter in an EDF should be avoided in favour of Pearson estimators; (iii) unless the linear moment assumptions are satisfied, consistency of dispersion and plug-in variance estimators can not be guaranteed and may result in spurious over-dispersion. The results provide explicit conditions on the underlying data generating process that will lead to spurious over-dispersion that can be used for model checking. This is illustrated based on real insurance data, where it is concluded that the linear moment assumptions are violated, which results in non-negligible spurious over-dispersion.

Individual tree segmentation from UAS Lidar data based on hierarchical filtering and clustering

ABSTRACT Accurate Individual Tree Segmentation (ITS) is fundamental to fine-scale forest structure and management studies. Light detection and ranging (Lidar) from Unoccupied Aerial Systems (UAS) has shown strengths in ITS and tree parameter estimation at stand and landscape scales. However, dense woodlands with tightly interspersed canopies and highly diverse tree species challenge the performance of ITS, and current research has not delved into the impact of mixed tree species and different leaf conditions on algorithm accuracy. Therefore, this study firstly evaluates the performance of open-source ITS methods, including both deep learning and non-deep learning algorithms, on data with mixed tree species and different leaf conditions, then proposes a hierarchical filtering and clustering (HFC) algorithm to mitigate the influence and improve the robustness. Hierarchical filtering consists of intensity filtering, Singular Value Decomposition (SVD) filtering, and Statistical Outlier Removal (SOR). Hierarchical clustering involves point-based clustering, cluster merging, and filtered point assignment. Through experiments on three distinct UAS Lidar datasets of forests with mixed tree species and different leaf conditions, HFC achieved the optimal segmentation results while maintaining high robustness. The variations of F1-score are 1–3 percentage points for mixed tree species and 1–2 percentage points for different leaf conditions across different datasets.