Estimation Method Research Articles

Real-time flood forecasting and uncertainty analysis: a case study of the Krong H’nang hydropower reservoir

ABSTRACT Early and reliable flood forecasting is crucial for mitigating the impacts of floods and ensuring the safe operation of irrigation and hydroelectric facilities. Traditional methods often use a calibrated event-based rainfall-runoff model with a limited number of simulations, making it difficult to measure parameter uncertainties accurately. This paper introduces a new method for estimating the parameters of the HEC-HMS model and evaluating the uncertainties in real-time flood forecasting. First, the Latin Hypercube Sampling (LHS) procedure is used to efficiently sample the parameter values throughout the feasible parameter space. Next, thousands of parameter sets are simulated using a deterministic rainfall-runoff model, generating numerous initial solutions for the Shuffled Complex Evolution (SCE-UA) algorithm to search and evolve. Additional parameter sets are then generated as the search evolves, leading to new simulations until the algorithm reaches the convergence criteria. The optimal solutions are selected based on predefined multiple objective functions. Uncertainty of the forecast results is also computed using the Generalized Likelihood Uncertainty Estimation (GLUE) method and represented as confidence intervals. The method was applied to forecast flood hydrographs at the Krong H’nang hydropower reservoir in Vietnam, using data from 33 flood events from 2016 to 2021. The results demonstrate that the program can effectively forecast the flood hydrograph up to 4 hours in advance (the Kling-Gupta efficiency KGE > 0.8 and the absolute relative volume error |VE| < 10%). The observed values fall within the uncertainty range of Q5%-Q95%. Compared to the conventional method, which uses a fixed parameter set value, this approach is superior and therefore can assist policymakers and operators in more effectively managing reservoir operations.

A new Bayesian method for estimation of value at risk and conditional value at risk

AbstractValue at Risk (VaR) and Conditional Value at Risk (CVaR) have become the most popular measures of market risk in Financial and Insurance fields. However, the estimation of both risk measures is challenging, because it requires the knowledge of the tail of the distribution. Therefore, Extreme Value Theory initially seemed to be one of the best tools for this kind of problems, because using peaks-over-threshold method, we can assume the tail data approximately follow a Generalized Pareto distribution (GPD). The main objection to its use is that it only employs observations over the threshold, which are usually scarce. With the aim of improving the inference process, we propose a new Bayesian method that computes estimates built with all the information available. Informative prior Bayesian (IPB) method employs the existing relations between the parameters of the loss distribution and the parameters of the GPD that models the tail data to define informative priors in order to perform Metropolis–Hastings algorithm. We show how to apply IPB when the distribution of the observations is Exponential, stable or Gamma, to make inference and predictions. .Afterwards, we perform a thorough simulation study to compare the accuracy and precision of the estimates computed by IPB and the most employed methods to estimate VaR and CVaR. Results show that IPB provides the most accurate, precise and least biased estimates, especially when there are very few tail data. Finally, data from two real examples are analysed to show the practical application of the method.

Improved Methods for Vaccine Effectiveness Studies.

Evaluating the impact of public health investments in vaccination programs is crucial for ensuring their efficiency and effectiveness. Vaccine effectiveness (VE) studies, such as those using the test-negative design (TND), are commonly used to confirm the impact of vaccines and to guide future improvements. The TND, favored for its simplicity and cost efficiency, mitigates biases common in other epidemiological study designs. However, its validity can be compromised by inconsistent symptom definitions and retrospective data application. Here we summarize recent findings that highlight the need to address correlated vaccination behaviors when estimating VE, that suggest using negative control variables to reduce confounding, and that recommend accounting for prior infection history in VE studies to improve accuracy and reliability. These insights are important for refining VE estimation methods.

The Impact of Foreign Direct Investment, Trade Value on the Economic Growth of Greater Mekong Subregion (GMS)

In the study entitled “The impact of foreign direct investment, trade value on the economic growth of the Greater Mekong Subregion countries” which aims to study the changes of foreign direct investment, trade value on the economic growth of the Greater Mekong Subregion countries (GMS) from 2005–2022, and analyze the impact of foreign direct investment, trade value on the economic growth of the Greater Mekong Subregion countries (GMS), The data used in the study is geographical data. This study uses descriptive statistics to analyze changes and uses the estimation method of ordinary least squares (OLS) through a statistical program. Through the analysis of changes in foreign direct investment, trade value, and gross domestic product of GMS, it was found that, since GMS was established in 1992, the condition of foreign direct investment, trade value, and gross domestic product of GMS has been changing continuously due to the influence of Chinese investment that has come to invest in the region in terms of transportation infrastructure and electricity. In terms of trade, there are trade relations among the group of GMS countries, focusing on facilities for transportation and trade in the region. Cooperation in the region, contributing to economic growth and reducing poverty, as well as responding to the demand for products in the region, developing agriculture in urban areas, promoting GMS as a tourist source, and making the total domestic product of the countries in the region grow. A temporary drop in foreign direct investment, trade value, and gross domestic product of the GMS is due to the Mekong Delta member countries being affected by the financial crisis in Asia, political unrest, the financial crisis in China, and the spread of the COVID-19 disease. Analysis of the effect of foreign direct investment and trade value on the economic growth of the GMS countries by testing the relationship found that both the value of foreign direct investment and the value of international trade are related in the same direction to economic growth: if the value of foreign direct investment in the GMS countries increases by 1%, it will result in an increase in the total domestic product by 0.1225%, and if the value of trade in the GMS countries increases by 1%, it will result in an increase in the total domestic product by 0.7566% through statistical significance of 0.01.

Assessment of Meteorological Drought in a Changing Environment: An Example in the Upper Yangtze River

AbstractRecent studies have suggested that drought projections using Palmer drought severity index (PDSI) and standardized evapotranspiration precipitation index (SPEI) may overestimate drought severity. This overestimation occurs because the potential evapotranspiration (PET) calculations fail to consider the interactive effects of vegetation responses such as increased leaf area index (LAI) and constrained stomatal conductance, which are influenced by elevated atmospheric CO2 concentrations ([CO2]). To address this issue, our study replaced the traditional Penman‐Monteith (PM) equation with a recently proposed PET equation that includes the effects of changing [CO2] and LAI to assess droughts at monthly scale in the Upper Yangtze River basin, which experiences the vegetation greening. The findings indicated a consistent increasing trend in drought conditions with minimal discrepancy between the two equations over the historical period (1986–2017). This consistency arises because the water‐saving effects of increased [CO2] and the greening effects of rising LAI largely counterbalance each other. However, for the future period (2018–2100), projections using PM equation predicted an intensification of drought conditions. In contrast, the improved SPEI indicated no significant drought variations, and the improved PDSI suggested a wetting trend. This divergence can be attributed to the water‐saving effects increasingly outweighing the greening effects, as PET shows a decreasing sensitivity to LAI with LAI increasing, but maintains a near‐constant sensitivity to elevated [CO2]. Consequently, the indices based on PM equation tend to overestimate future drought severity. Overall, this study demonstrates that the new PET estimation method is more capable of responding to the changing environment.

A fusion algorithm of multidimensional element space mapping architecture for SOC estimation of lithium-ion batteries under dynamic operating conditions

Accurate estimation of the state of charge (SOC) is pivotal for ensuring the reliable operation of lithium-ion batteries in electric vehicles, particularly under complex real-world conditions. This paper introduces a novel SOC estimation method named Multidimensional Elemental Space Mapping Architecture (MESMA), which integrates the battery equivalent circuit model with a data-driven approach to tackle adaptability under intricate conditions and high-dimensional input data correlation. Initially, a least squares method incorporating a forgetting factor is employed to identify the battery circuit model parameters and analyze their correlation with SOC. Subsequently, a feature fusion algorithm is introduced to discern spatial variables highly correlated with SOC by leveraging both original voltage and current data and circuit model parameters as inputs to a Convolutional Neural Network (CNN) model, and an eight-input and one-output spatially mapped feature CNN model is established. Finally, the XGBoost model is harnessed for battery SOC estimation. The effectiveness and adaptability of MESMA are validated using experimental data from both public single condition and mixed condition datasets, including FUDS, Mixed-1, etc. The results unequivocally demonstrate that MESMA accurately estimates the SOC of lithium-ion batteries under varying operating conditions. Furthermore, it exhibits remarkable adaptability and stability across different temperatures, effectively capturing SOC trends.

Estimating the GPS time-varying differential code bias between C1 and P1 observations to provide a model product

Abstract Based on the time-varying characteristics of GPS satellite differential code biases (DCB) between C1 and P1 observations, a method for directly estimating its model coefficients is proposed to streamline processing and enhance product accuracy. Evaluation over three days compares estimated DCB (C1–P1) values and assesses real-time service (RTS) capabilities in Single Point Positioning (SPP). Three-day DCB (C1-P1) estimates match single-epoch time-varying DCB (C1-P1) series fitting accuracy. SPP results show significant improvement over Orbit Determination in Europe (CODE), reducing average root mean square error in three dimensions by 12 cm and by 4 cm compared to Chinese Academy of Sciences (CAS). Real-time DCB (C1-P1) service maintains stable and accurate performance, comparable to fitted values of single-epoch time-varying series. Real-time SPP application shows the method enhances positioning accuracy compared to CODE by up to 35%, confirming the feasibility and effectiveness of the proposed model estimation method.&amp;#xD;

Root zone in the Earth system

Abstract. The root zone is a vital part of the Earth system and a key element in hydrology, ecology, agronomy, and land surface processes. However, its definition varies across disciplines, creating barriers to interdisciplinary understanding. Moreover, characterizing the root zone is challenging due to a lack of consensus on definitions, estimation methods, and their merits and limitations. This opinion paper provides a holistic definition of the root zone from a hydrology perspective, including its moisture storage, deficit, and storage capacity. We demonstrate that the root zone plays a critical role in the biosphere, pedosphere, rhizosphere, lithosphere, atmosphere, and cryosphere of the Earth system. We underscore the limitations of the traditional reductionist approach in modelling this complex and dynamic zone and advocate for a shift towards a holistic, ecosystem-centred approach. We argue that a holistic approach offers a more systematic, simple, dynamic, scalable, and observable way to describe and predict the role of the root zone in Earth system science.

Penalized Sparse Covariance Regression with High Dimensional Covariates

Covariance regression offers an effective way to model the large covariance matrix with the auxiliary similarity matrices. In this work, we propose a sparse covariance regression (SCR) approach to handle the potentially high-dimensional predictors (i.e., similarity matrices). Specifically, we use the penalization method to identify the informative predictors and estimate their associated coefficients simultaneously. We first investigate the Lasso estimator and subsequently consider the folded concave penalized estimation methods (e.g., SCAD and MCP). However, the theoretical analysis of the existing penalization methods is primarily based on i.i.d. data, which is not directly applicable to our scenario. To address this difficulty, we establish the non-asymptotic error bounds by exploiting the spectral properties of the covariance matrix and similarity matrices. Then, we derive the estimation error bound for the Lasso estimator and establish the desirable oracle property of the folded concave penalized estimator. Extensive simulation studies are conducted to corroborate our theoretical results. We also illustrate the usefulness of the proposed method by applying it to a Chinese stock market dataset.

VP-net: an end-to-end deep learning network for elastic wave velocity prediction in human skin in vivo using optical coherence elastography.

Acne vulgaris, one of the most common skin conditions, affects up to 85% of late adolescents, currently no universally accepted assessment system. The biomechanical properties of skin provide valuable information for the assessment and management of skin conditions. Wave-based optical coherence elastography (OCE) quantitatively assesses these properties of tissues by analyzing induced elastic wave velocities. However, velocity estimation methods require significant expertise and lengthy image processing times, limiting the clinical translation of OCE technology. Recent advances in machine learning offer promising solutions to simplify velocity estimation process. In this study, we proposed a novel end-to-end deep-learning model, named velocity prediction network (VP-Net), aiming to accurately predict elastic wave velocity from raw OCE data of in vivo healthy and abnormal human skin. A total of 16,424 raw phase slices from 1% to 5% agar-based tissue-mimicking phantoms, 28,270 slices from in vivo human skin sites including the palm, forearm, back of the hand from 16 participants, and 580 slices of facial closed comedones were acquired to train, validate, and test VP-Net. VP-Net demonstrated highly accurate velocity prediction performance compared to other deep-learning-based methods, as evidenced by small evaluation metrics. Furthermore, VP-Net exhibited low model complexity and parameter requirements, enabling end-to-end velocity prediction from a single raw phase slice in 1.32ms, enhancing processing speed by a factor of ∼100 compared to a conventional wave velocity estimation method. Additionally, we employed gradient-weighted class activation maps to showcase VP-Net's proficiency in discerning wave propagation patterns from raw phase slices. VP-Net predicted wave velocities that were consistent with the ground truth velocities in agar phantom, two age groups (20s and 30s) of multiple human skin sites and closed comedones datasets. This study indicates that VP-Net could rapidly and accurately predict elastic wave velocities related to biomechanical properties of in vivo healthy and abnormal skin, offering potential clinical applications in characterizing skin aging, as well as assessing and managing the treatment of acne vulgaris.

Psychometric Properties of the World Health Organization Quality of Life Scale for Older Adults (WHO-QoL-Old) in a Mexican Population.

The present study aimed to contribute to analyzing the psychometric properties of the WHO Quality of Life Scale for Older Adults (WHO-QoL-Old) in a sample of older adults in Michoacán, Mexico. 111 older adults from Michoacán, Mexico, participated in the study. Confirmatory factor analysis (CFA) was conducted to test the fit of various models. Data analysis was performed using R Studio, considering the ordinal nature of the items in the model estimation method. Internal consistency was evaluated using the alpha coefficient (α) and McDonald's omega coefficient (ω). The CFA indicated that the six-correlated-factor model proposed theoretically showed a very good fit (χ2: 397.11, p < 0.001; CFI: 0.958; SRMR: 0.079; RMSEA: 0.079). The factors within the model demonstrated acceptable internal consistency, with an alpha coefficient ranging from 0.739 to 0.874 and an omega coefficient ranging from 0.748 to 0.882. It is concluded that the WHO-QoL-Old scale presents good psychometric properties for the Mexican older adult population.

Dimethyl sulfide (DMS) climatologies, fluxes, and trends – Part 2: Sea–air fluxes

Abstract. Dimethyl sulfide (DMS) contributes to cloud condensation nuclei (CCN) formation in the marine environment. DMS is ventilated from the ocean to the atmosphere, and, in most models, this flux is calculated using seawater DMS concentrations and a sea–air flux parameterization. Here, climatological seawater DMS concentrations from interpolation and parameterization techniques are passed through seven flux parameterizations to estimate the DMS flux. The seasonal means of calculated fluxes are compared to identify differences in absolute values and spatial distributions, which show large differences depending on the flux parameterization used. In situ flux observations were used to validate the estimated fluxes from all seven parameterizations. Even though we see a correlation between the estimated and observation values, all methods underestimate the fluxes in the higher range (&gt;20 µmol m−2 d−1) and overestimate the fluxes in the lower range (&lt;20 µmol m−2 d−1). The estimated uncertainty in DMS fluxes is driven by the uncertainty in seawater DMS concentrations in some regions but by the choice of flux parameterization in others. We show that the resultant flux is, hence, highly sensitive to both and suggest that there needs to be an improvement in the estimation methods of global seawater DMS concentration and sea–air fluxes for accurately modeling the effect of DMS on the atmosphere.

Uncertain Logistic Population Model with Allee Effect

By considering the influence of various uncertain noises, we established an uncertain Logistic population model with Allee effect, which was characterized by an uncertain differential equation. Firstly, we obtained the solution of the model, and discussed the stability of the equilibrium state. Secondly, the unknown parameters in the model were estimated by using the generalized moment estimation method under the framework of uncertainty theory. Finally, the parameter estimations of the model and the properties of the solution were explained by an example analysis.

DPC clustering algorithm based on K-nearest neighbors and kernel density estimation

Clustering by fast search and find of density peaks (DPC) is a density-based clustering algorithm. This algorithm does not require iteration and too many parameter settings, but it cannot identify cluster centers with small cluster density because the local structure of the data is not considered when calculating the local density. To address the above problems, a DPC clustering algorithm (KKDPC) based on K-reciprocal Neighbors (KN) and Kernel Density Estimation (KDE) is proposed. First, the number of reciprocal neighbors and local kernel density of data points are obtained by nearest neighbor and kernel density estimation methods ; second, the number of K reciprocal neighbors is added to the local kernel density to obtain a new local density; finally, the relative distance is obtained according to the local density of the data points, and the cluster center is selected and the non-center point is allocated by constructing a decision graph. Experiments are conducted using artificial and real data sets, and compared with seven clustering algorithms: DPC, DBSCAN, K-means, FCM, DPC-KNN, DPC-NN, and DPC-FWSN. The performance of the KKDPC clustering algorithm is verified by calculating the adjusted mutual information (AMI), adjusted Rand index (ARI), and normalized mutual information (NMI). The experimental results show that this method can more accurately identify the cluster center and effectively improve the clustering accuracy.

Bones at Home

Remote teaching during the COVID-19 pandemic led to a range of pedagogical challenges for anthropology laboratory courses. In biological anthropology courses such as Human Osteology, hands-on experience is essential to achieving learning outcomes, including basic bone and feature (i.e., landmark) identification, identification from fragmentary remains, and age and sex estimation. To address the need for training that includes object-based, tactile (haptic) learning in fields such as biological anthropology and archaeology, all Human Osteology students at Mount Royal University and the University of Manitoba took home plastic model skeletons. The purpose of this study was to evaluate how well remotely educated undergraduates (REUs) met human osteology learning objectives when supported by plastic model skeletons at home. We present the results of a survey designed to test core osteological skills obtained by REUs in comparison with undergraduates educated with in-person laboratory components (IPUs) and experts in the field (zero to four and five or more years of experience). REU scores did not differ significantly from those of IPU or Junior Experts with less than five years of experience. Students performed well in bone identification but were limited in their ability to apply common sex and age estimation methods and to identify incomplete elements. Our findings reinforce the importance of haptic learning and years of experience in human osteological learning. They support the use of take-home models as valuable resources in both remote and in-person undergraduate teaching. This work is a step toward more inclusive universal instructional design that can be applied across various anthropology laboratory courses.

Measuring Amazon Rainfall Intensity With Sound Recorders

AbstractGround weather observations are scarce in many parts of the globe, hampering effective climate monitoring and disaster management. In the Amazon basin, this occurs due to its remoteness and the challenging measurement of rainfall within the forest. Innovative rainfall estimation methods are thus requested to fill this gap. Here we present an approach to estimate rainfall based on sound measurements. We identified the best frequency range to estimate rainfall occurrence and intensity, trained classification and regression models with sound and rain gauge data collected in the Central Amazon during 9 months. By training a random forest classifier/regression model based on power spectrum values it was possible to identify and satisfactorily estimate hourly rainfall rates in two vegetation environments distinct from the training site, located 30 km from it. The proposed method is a promising approach for future weather monitoring in remote tropical areas.

Statistical Analysis and Several Estimation Methods of New Alpha Power-Transformed Pareto Model with Applications in Insurance

This article defines a new distribution using a novel alpha power-transformed method extension. The model obtained has three parameters and is quite effective in modeling skewed, complex, symmetric, and asymmetric datasets. The new approach has one additional parameter for the model. Certain distributional and mathematical properties are investigated, notably reliability, quartile, moments, skewness, kurtosis, and order statistics, and several approaches of estimation, notably the maximum likelihood, least square, weighted least square, maximum product spacing, Cramer-Von Mises, and Anderson Darling estimators of the model parameters were obtained. A Monte Carlo simulation study was conducted to evaluate the performance of the proposed techniques of estimation of the model parameters. The actuarial measures are computed for our recommended model. At the end of the paper, two insurance applications are illustrated to check the potential and utility of the suggested distribution. Evaluation using four selection criteria indicates that our recommended model is the most appropriate probability model for modeling insurance datasets.

A stableness of resistance model for nonresponse adjustment with callback data

Abstract Nonresponse arises frequently in surveys, and follow-ups are routinely made to increase the response rate. In order to monitor the follow-up process, callback data have been used in social sciences and survey studies for decades. In modern surveys, the availability of callback data is increasing because the response rate is decreasing, and follow-ups are essential to collect maximum information. Although callback data are helpful to reduce the bias in surveys, such data have not been widely used in statistical analysis until recently. We propose a stableness of resistance assumption for nonresponse adjustment with callback data. We establish the identification and the semiparametric efficiency theory under this assumption, and propose a suite of semiparametric estimation methods including doubly robust estimators, which generalize existing parametric approaches for callback data analysis. We apply the approach to a Consumer Expenditure Survey dataset. The results suggest an association between nonresponse and high housing expenditures.

Experimental study on the buffering mechanism of EPS bead-sand cushions under single and multiple impacts

As a main functional component of rock sheds in rockfall protection projects, traditional sand cushions have shortcomings such as heavy weight and weak buffering capacity. EPS bead-sand cushion can effectively solve these problems, but its buffering mechanism has not been fully revealed. In this study, a series of impact tests were carried out to investigate the performance of EPS bead-sand cushions with different EPS bead contents, and the evolutions of rockfall impact force, penetration depth, earth pressure, and slab vibration under single impact and multiple impacts were comparatively analyzed. The results show that with the addition of EPS beads, the maximum impact force, the peak earth pressure, and the vibration acceleration are significantly reduced. However, the cushion with high EPS bead content is at risk of being penetrated under high energy or multiple impacts, leading to excessive concentration of impact stresses. Furthermore, the EPS beads can alleviate the hardening of the sand cushion under impact through their deformation coordination, but excessive penetration should be prevented in the design of EPS bead-sand cushions. On this basis, combined with traditional sand cushion design theory, an estimation method for the maximum impact force applicable to EPS bead-sand cushion was proposed. The research results can provide a reference for the design and optimization of cushions in actual projects.

A New Lifetime Distribution and its Application to Cancer Data

Introduction: Recently, researchers have introduced new generated families of univariate lifetime distributions. These new generators are obtained by adding one or more extra shape parameters to the underlying distribution or compounding two distributions to get more flexibility in fitting data in different areas such as medical sciences, environmental sciences, and engineering. The addition of parameter(s) has been proven useful in exploring tail properties and for improving the goodness-of-fit of the family of the proposed distributions. Methods:A new Three-Parameter Weibull-Generalized Gamma distribution which provides more flexibility in modeling lifetime data is developed using a two-component mixture of Weibull distribution (with parameters θ and λ) and Generalised Gamma distribution (with parameters α=4,θ and λ). Some of its mathematical properties such as the density function, cumulative distribution function, survival function, hazard rate function, moment generating function, Renyi entropy and order statistics are obtained. The maximum likelihood estimation method was used in estimating the parameters of the proposed distribution and a simulation study is performed to examine the performance of the maximum likelihood estimators of the parameters. Results: Real life applications of the proposed distribution to two cancer datasets are presented and its fit was compared with the fit attained by some existing lifetime distributions to show how the Three-Parameter Weibull-Generalized Gamma distribution works in practice Conclusion: The results suggest that the proposed model performed better than its competitors and it’s a useful alternative to the existing models.