Bias Error Research Articles

Psychological impact on the quality-of-life in patients of psoriasis vulgaris: A systemic review

Living with a chronic condition like psoriasis can significantly affect the individual involved. Psoriasis patients have difficulties with their physical, psychological, occupational, and social functioning. These impairments can result in psychiatric morbidity depression and anxiety disorders. This systematic review (SR) assessed the impact of psychological factors in life of psoriasis patients. The literature search comprised Medline through PubMed, Web of Science, the Cochrane Library, Wiley Online Library, and additional sources such as Google Scholar, spanning the period from 2014 to 2023. Thorough examination of the most important electronic databases was conducted. Various study designs, including observational studies, trials, and commentaries, were incorporated to ensure a comprehensive examination of the research topic. The risk of bias was evaluated using established methodology. This SR comprised 13 studies, with the Cochrane Risk of Bias Tool employed to assess biases in randomized trials across five domains. The majority of the studies demonstrated a low risk (57.69%), indicating their reliability while those categorized as unclear (37.17%) exhibited some ambiguity without compromising the validity of results. Studies categorized as high risk (5.12%) suggested significant bias and potential errors. The findings indicate an association between psoriasis and various psychological factors, including depression, anxiety, suicidal ideation, impaired emotional functioning, negative body image, and self-perception. The significant impact of depression and anxiety on individuals with psoriasis underscores the need for greater awareness of its psychological aspects. Incorporating psychological interventions into care and management strategies is crucial for addressing this chronic condition and enhancing overall well-being, thus improving quality of life.

Development of improved estimators of finite population mean in simple random sampling with dual auxiliaries and its application to real world problems

In general, the incorporation of supplementary information reduces the Mean Square Error (MSE) and, consequently, enhances the precision of estimating a population parameter. This improvement relies on the appropriate application of a suitable function, with careful consideration. This study introduces two innovative families of estimators for the finite population mean, both of which exhibit superior performance in scenarios involving dual auxiliary information in simple random sampling. Expressions up to the first-order approximation, for bias, and Mean Square Error were derived, and the conditions under which these proposed families surpassed the existing estimators. Our evaluation involved the use of both real and simulated data to compute the Mean Square Error and Percent Relative Efficiency (PRE) of the estimators. A comparative analysis revealed that under the specified conditions, both proposed families yielded more precise results.

OPTIMISING SPRINT PERFORMANCE: CONCURRENT VALIDITY AND RELIABILITY OF 25HZ GNSS UNITS FOR SPEED PROFILING

Sprint performance profiling is crucial for understanding an athlete’s capabilities and identifying potential areas for improvement. While traditional gold standard systems provide valid and reliable measurements, they are often costly, laboratory-based, or impractical for field-based settings. New GNSS units (25Hz) with higher sampling rates may address some of these limitations. The purpose of the project was to evaluate the concurrent validity and inter-unit reliability of 25Hz GNSS units by examining their agreement with laser devices for velocity measurements and timing gates for interval times. Validity was assessed during a track and field training session. 30 participants performed between 3 and 6 sprints over 40m whilst being assessed simultaneously through all systems. Inter-unit reliability was assessed on a closed athletics track by placing 3 GNSS units on a motor vehicle and completing 60 accelerations from 0-60m. Low mean bias (<1%) and typical error less than <2% for all measurements demonstrate excellent agreement between GNSS and criterion devices. The units demonstrated good reliability for the 0-10m interval (ICC = 0.86), excellent reliability for all remaining 10m intervals to 60m (ICC = 0.91-0.99), full times 0-60m (ICC = 0.97) and maximal sprint velocity (ICC = 1.0). These GNSS units offer a more efficient and practical alternative to traditional measurement devices. For coaches, this technology offers a scalable method to concurrently assess the sprint performances of multiple athletes during training and competitions, enabling evidence-based decisions to guide athletic development.

Hybrid machine learning models for groundwater level prediction in a snow‐dominated region: An evaluation of EEMD, VMD and EWT decomposition techniques

Hybrid machine learning models for groundwater level prediction in a <scp>snow‐dominated</scp> region: An evaluation of <scp>EEMD</scp>, <scp>VMD</scp> and <scp>EWT</scp> decomposition techniques

Maximizing Building Energy Efficiency Through Zone-Based Conditions: An Advanced Dynamic Simulation Approach

Abstract The primary objective of this research is to maximize the building energy efficiency by identifying the zone-based conditions and employing an advanced energy simulation model. It is possible to achieve this by reducing periods of excessive heating and cooling by utilizing a calibrated energy model with 15-minute measured data. Further, by regulating the energy consumption of various functional zones corresponding to the work schedules, building's energy system timetables, and the sensible temperature comfort requirements, additional energy savings can be achieved. To show the effect of dynamic simulation on building energy consumption, periodic 15-min temperature data were collected in different zones to be used both in the calibration of the energy model and in the improvement of the current energy profile. Mean bias error and cumulative variation of the root mean squared error were chosen as a performance indicator. Utilizing measured data, over-heated and over-cooled periods were defined by manually identifying zone-based indoor comfort conditions to predict improvements in overall building energy performance. The potential energy savings that can be achieved by largely eliminating over-heating periods are calculated. In addition, energy needs of different zones were considered and zone-based scheduling and zone-based comfort conditions were applied with the implementation of demand-side management. As a result, 17% energy efficiency can be achieved with an automated heating system that controls the indoor temperatures and ensures that the temperature is always kept at the desired level. As a further improvement, 32% energy efficiency can be achieved by applying zone-based scheduling and comfort conditions.

Comparing the Clique Percolation algorithm to other overlapping community detection algorithms in psychological networks: A Monte Carlo simulation study

In psychological networks, one limitation of the most used community detection algorithms is that they can only assign each node (symptom) to a unique community, without being able to identify overlapping symptoms. The clique percolation (CP) is an algorithm that identifies overlapping symptoms but its performance has not been evaluated in psychological networks. In this study, we compare the CP with model parameters chosen based on fuzzy modularity (CPMod) with two other alternatives, the ratio of the two largest communities (CPRat), and entropy (CPEnt). We evaluate their performance to: (1) identify the correct number of latent factors (i.e., communities); and (2) identify the observed variables with substantive (and equally sized) cross-loadings (i.e., overlapping symptoms). We carried out simulations under 972 conditions (3x2x2x3x3x3x3): (1) data categories (continuous, polytomous and dichotomous); (2) number of factors (two and four); (3) number of observed variables per factor (four and eight); (4) factor correlations (0.0, 0.5, and 0.7); (5) size of primary factor loadings (0.40, 0.55, and 0.70); (6) proportion of observed variables with substantive cross-loadings (0.0%, 12.5%, and 25.0%); and (7) sample size (300, 500, and 1000). Performance was evaluated through the Omega index, Mean Bias Error (MBE), Mean Absolute Error (MAE), sensitivity, specificity, and mean number of isolated nodes. We also evaluated two other methods, Exploratory Factor Analysis and the Walktrap algorithm modified to consider overlap (EFA-Ov and Walk-Ov, respectively). The Walk-Ov displayed the best performance across most conditions and is the recommended option to identify communities with overlapping symptoms in psychological networks.

Stacked hybridization to enhance the performance of artificial neural networks (ANN) for prediction of water quality index in the Bagh river basin, India

Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO3−), Magnesium (Mg2+), Sulphate (SO42−), Calcium (Ca2+), and Potassium (K+). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE)) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R2 (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural resources.

Foreground removal with ILC methods for AliCPT-1

One of the main goals of most future CMB experiments is the precise measurement of CMB B-mode polarization, whose major obstacle is the Galactic foregrounds. In this paper, we evaluate the foreground cleaning performance of the variants of the ILC method on partial sky B-modes and analyze the main sources of biases on the BB power spectrum. Specially, we compare the NILC, the cILC (in three domains) and the cMILC methods for AliCPT-1 simulations. We find that the cILC methods implemented in harmonic space and needlet space are both competent to clean different models of foregrounds, which bias the tensor-to-scalar ratio about 0.008 at maximum, and constrain the tensor-to-scalar ratio to r < 0.043(95%CL) for the AliCPT-1 configuration. We also note that the deviation of the estimated noise bias from the actual one for ILC, dubbed the noise bias error (NBE) in this paper, might make significant effects on the power spectrum for a small footprint and low signal-to-noise ratio CMB experiment. We finally obtain its relation with respect to the noise residual which fits well with the simulated results.

Inspection Times and Optimal Censoring Scheme for Generalized InvertedExponential Distribution with Progressive type I Interval Censored Data

In this article, two issues related to the design of the experiment are investigated; namely the inspection times and optimal censoring. In the first issue, we study four different approaches to determine the inspection times, namely; pre-specified, equally spaced, optimally spaced, and equal probability using two optimality criteria. In the second issue, we identify the optimal censoring scheme by finding the expected numbers of removals or proportions that attain a specific optimality criterion using two optimality criteria considered. Numerical comparisons using the Monte Carlo simulations are provided of the inspection times, optimum control schemes issues for different parameters and different sample sizes. Statistical measures such as bias and root mean square error for both pre-specified and equal space methods are calculated and the inspection times for different values of the censoring percentage h are obtained.

EVALUATION OF NEW DEVELOPED MODELS FOR ESTIMATION OF DIFFUSE SOLAR RADIATION OVER IKEJA, LAGOS STATE, NIGERIA

This study investigates the estimation of diffuse solar radiation in Ikeja, Nigeria (6.58°N, 3.33°E, 39.40m above sea level). Employing a 22-year dataset (2001-2022) of monthly average climatic data from NASA website, nineteen new models were developed to predict diffuse solar radiation. These models incorporated various meteorological parameters including global solar radiation, wind speed, temperature, pressure, and relative humidity. A categorized approach was used, with models falling into five groups: modified Page, Liu and Jordan; clearness index with one additional variable; two-variable; three-variable; and a four-variable model. Evaluation using five statistical tests tools of Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t-test, and Coefficient of determination (R²) identified Equation 28h (the model relating diffuse radiation to clearness index and temperature) as the most suitable model out of all the models in each of the five categories for estimation of diffuse solar radiation for Ikeja. Hence, emphasizing the importance of considering both atmospheric clarity and temperature for accurate diffuse solar radiation estimation in this coastal location.

Application of Bayesian models in building information modeling of residential buildings

This paper lays forth a process for continuing Bayesian calibration of full-scale building energy simulation (BES) models by making use of data obtained from building information modeling (BIM) and building energy management systems (BEMS). According to a survey taken in China, it is possible that importing data from BIM and BEMS will dramatically cut down on the amount of time and effort required for the continuing calibration of BES models. After that, the continuous calibration approach that had been developed was examined with the use of a case study that was based on the actual calibration of a building. In China, Building Information Modeling (BIM) and data on the amount of electricity used each month over the course of the preceding three years were both incorporated into the case study. According to the findings, a non-continuous technique on the test dataset fared worse than a continuous Bayesian calibration method in terms of prediction accuracy and reduced uncertainty. The normalized mean biased error (NMBE) and the coefficient of variation of the root mean square error (CVRMSE) are both discussed in this research, and comparisons are drawn between the two.

Intelligent prediction model of a polymer fracture grouting effect based on a genetic algorithm-optimized back propagation neural network

Polymer grouting can effectively improve the stability of surrounding rock fractures. However, in practical construction, it is difficult to judge the degree of coupling between the slurry and the rock, and the effective grouting range after grouting. Therefore, early prediction of the effect of grouting on the surrounding rock is crucial. In this paper, a new artificial intelligence method is proposed to predict the polymer fracture grouting effect. The genetic algorithm optimized back propagation neural network (GA-BP) is employed to construct an intelligent prediction model. To acquire a substantial dataset for constructing the model, an easily assembled/disassembled test apparatus for polymer fracture grouting is designed. The maximum coupling degree of the fractures and slurry diffusion distance are chosen as the evaluation metrics for the grouting effectiveness. The influences of the fracture characteristic parameters and grouting volume on the grouting effect are investigated. Furthermore, a comprehensive analysis is conducted on the spatiotemporal diffusion characteristics and slurry-rock coupling mechanism of polymer grouting. Compared to traditional BP neural networks, and three other machine learning algorithms (decision trees, random forests and gradient boosting decision trees), the GA-BP model outperforms them in terms of R2 (coefficient of determination), MSE (mean squared error), MBE (mean bias error), MAE (mean absolute error) and RMSE (root mean squared error) in both the test and training sets. The GA algorithm significantly improves the accuracy and robustness of the prediction model. The optimized model demonstrates significant accuracy in predicting grouting results and assessing efficiency, providing a practical reference for grouting construction.

New Models for Estimation of Diffuse Solar Radiation for Warri, Nigeria

This study utilized a 22-year dataset (2001-2022) comprising monthly average daily global solar radiation, diffuse solar radiation, relative humidity, atmospheric pressure, wind speed, and mean temperature for Warri (Latitude 5.52 oN, Longitude 5.73 oE and 6.10 m above sea level). Nineteen models, categorized into modified Page, Liu, and Jordan models; clearness index and one variable models; two variable models; three variable models; and four variable models, were developed and statistically tested using the following validation indices of Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t-test, and coefficient of determination (R2). Among the models, the linear page model from the modified Page, Liu, and Jordan category, the model that relates the diffuse solar radiation with clearness index and mean temperature for clearness index and one variable models, the model that relates wind speed and atmospheric pressure for two variable models, and the model that relates wind speed, atmospheric pressure and relative humidity for three variable models were found more appropriate. Upon ranking the best-performing models from each category, the model that relates that diffuse solar radiation with clearness index and mean temperature performed best as the most suitable model for estimating diffuse solar radiation in Warri.

Estimation of monthly sunshine duration using satellite derived cloud data

Sunshine duration (SD) is one of the critical meteorological parameters used in different fields of application such as climate, renewable energy and agriculture. In this respect, determination and/or estimation of the temporal and spatial variability of SD is critical. Meteorological satellite data/products can be used for estimating SD and in constructing their maps due to their frequent observation of large areas at once. In this study, a multilayer perceptron type artificial neural network model was built to estimate the monthly mean SD for Türkiye using the EUMETSAT CM SAF (Satellite Application Facility on Climate Monitoring) CFC (Cloud Fractional Coverage) and CTY (Cloud Type) data, GMTED2010 (Global Multi-resolution Terrain Elevation Data) data, month number and daylength. The datasets of 45 stations, spanning nine years (2005–2013), were used for training the model and 12 stations for testing and validating the simulated values. We have compared the results of our model with the ground-measured values for the whole period under consideration and the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE) and the coefficient of determination (R2) were found as 0.7803 h, 0.6206 h, 0.1751 h and 0.9387, respectively. It has been shown that using the new generation cloud products such as CFC and CTY, elevation data such as GMTED2010 and daylength, it is possible to predict the SD for regions under the coverage of the satellite, in case no measurement is possible or may be unreliable, without needing any measured meteorological data.

Uncertainty in the mating strategy of honeybees causes bias and unreliability in the estimates of genetic parameters

BackgroundBreeding queens may be mated with drones that are produced by a single drone-producing queen (DPQ), or a group of sister-DPQs, but often only the dam of the DPQ(s) is reported in the pedigree. Furthermore, datasets may include colony phenotypes from DPQs that were open-mated at different locations, and thus to a heterogeneous drone population.MethodsSimulation was used to investigate the impact of the mating strategy and its modelling on the estimates of genetic parameters and genetic trends when the DPQs are treated in different ways in the statistical evaluation model. We quantified the bias and standard error of the estimates when breeding queens were mated to one DPQ or a group of DPQs, assuming that this information was known or not. We also investigated four alternative strategies to accommodate the phenotypes of open-mated DPQs in the genetic evaluation: excluding their phenotypes, adding a dummy pseudo-sire in the pedigree, or adding a non-genetic (fixed or random) effect to the statistical evaluation model to account for the origin of the mates.ResultsThe most precise estimates of genetic parameters and genetic trends were obtained when breeding queens were mated with drones of single DPQs that are correctly assigned in the pedigree. However, when they were mated with drones from one or a group of DPQs, and this information was not known, erroneous assumptions led to considerable bias in these estimates.Furthermore, genetic variances were considerably overestimated when phenotypes of colonies from open-mated DPQs were adjusted for their mates by adding a dummy pseudo-sire in the pedigree for each subpopulation of open-mating drones. On the contrary, correcting for the heterogeneous drone population by adding a non-genetic effect in the evaluation model produced unbiased estimates.ConclusionsKnowing only the dam of the DPQ(s) used in each mating may lead to erroneous assumptions on how DPQs were used and severely bias the estimates of genetic parameters and trends. Thus, we recommend keeping track of DPQs in the pedigree, and not only of the dams of DPQ(s). Records from DPQ colonies with queens open-mated to a heterogeneous drone population can be integrated by adding non-genetic effects to the statistical evaluation model.

Mendelian Randomization Analysis of the Causal Effect of Cigarette Smoking on Hospital Costs.

Knowledge of the impact of smoking on health care costs is important for establishing the external effects of smoking and for evaluating policies intended to modify this behavior. Conventional analysis of this association is difficult because of omitted variable bias, reverse causality, and measurement error. We approached these challenges using a Mendelian Randomization study design; genetic variants associated with smoking behaviors were used in instrumental variables models with inpatient hospital costs (calculated from electronic health records) as the outcome. We undertook genome-wide association studies to identify genetic variants associated with smoking initiation and a composite smoking index (reflecting cumulative health impacts of smoking) on up to 300 045 individuals (mean age: 57 years at baseline, range 39-72 years) in the UK Biobank. We followed individuals up for a mean of 6 years. Genetic liability to initiate smoking (ever vs. never smoking) was estimated to increase mean per-patient annual inpatient hospital costs by £477 (95% confidence interval (CI): £187 to £766). A one-unit change in genetic liability to the composite smoking index (range: 0-4.0) increased inpatient hospital costs by £204 (95% CI: £105 to £303) per unit increase in this index. There was some evidence that the composite smoking index causal models violated the instrumental variable assumptions, and all Mendelian Randomization models were estimated with considerable uncertainty. Models conditioning on risk tolerance were not robust to weak instrument bias. Our findings have implications for the potential cost-effectiveness of smoking interventions. We report the first Mendelian Randomization analysis of the causal effect of smoking on health care costs. Using two smoking phenotypes, we identified substantial impacts of smoking on inpatient hospital costs, although the causal models were associated with considerable uncertainty. These results could be used alongside other evidence on the impact of smoking to evaluate the cost-effectiveness of antismoking interventions and to understand the scale of externalities associated with this behavior.

Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T

PurposeFurther acceleration of DWI in diagnostic radiology is desired but challenging mainly due to low SNR in high b-value images and associated bias in quantitative ADC values. Deep learning-based reconstruction and denoising may provide a solution to address this challenge. MethodsThe effects of SNR reduction on ADC bias and variability were investigated using a commercial diffusion phantom and numerical simulations. In the phantom, performance of different reconstruction methods, including conventional parallel (SENSE) imaging, compressed sensing (C-SENSE), and compressed SENSE acceleration with an artificial intelligence deep learning-based technique (C-SENSE AI), was compared at different acceleration factors and flip angles using ROI-based analysis. ADC bias was assessed by Lin's Concordance correlation coefficient (CCC) followed by bootstrapping to calculate confidence intervals (CI). ADC random measurement error (RME) was assessed by the mean coefficient of variation (CV¯) and non-parametric statistical tests. ResultsThe simulations predicted increasingly negative bias and loss of precision towards lower SNR. These effects were confirmed in phantom measurements of increasing acceleration, for which CCC decreased from 0.947 to 0.279 and CV¯ increased from 0.043 to 0.439, and of decreasing flip angle, for which CCC decreased from 0.990 to 0.063 and CV¯ increased from 0.037 to 0.508. At high acceleration and low flip angle, C-SENSE AI reconstruction yielded best denoised ADC maps. For the lowest investigated flip angle, CCC = {0.630, 0.771 and 0.987} and CV¯={0.508, 0.426 and 0.254} were obtained for {SENSE, C-SENSE, C-SENSE AI}, the improvement by C-SENSE AI being significant as compared to the other methods (CV: p = 0.033 for C-SENSE AI vs. C-SENSE and p < 0.001 for C-SENSE AI vs. SENSE; CCC: non-overlapping CI between reconstruction methods). For the highest investigated acceleration factor, CCC = {0.479,0.926,0.960} and CV¯={0.519,0.119,0.118} were found, confirming the reduction of bias and RME by C-SENSE AI as compared to C-SENSE (by trend) and to SENSE (CV: p < 0.001; CCC: non-overlapping CI). ConclusionADC bias and random measurement error in DWI at low SNR, typically associated with scan acceleration, can be effectively reduced by deep-learning based C-SENSE AI reconstruction.

Electric Energy Metering Error Evaluation Method Based on Deep Learning

The measuring accuracy of the electric energy meter, voltage transformer and current transformer shows a dynamic state under the influence of its factors and external factors. The error of the voltage transformer and current transformer cannot be measured by traditional method. This paper establishes a multidimensional error analysis and fault diagnosis system for power metering based on Hadoop architecture and Spark memory calculation. The platform extracted the error signal from the measurement data and calculated the characteristic value of the error signal. Then, dependent cloud and dynamic time rules are used to estimate the transformer's and voltage transformer's continuity. Then, a half-step membership degree cloud generation algorithm is constructed to overcome the error bias randomness and fuzzy characteristics under the influence factors. Finally, the system uses the dynamic correction method to estimate the similarity of error timing and quantitative factors to realize the error calculation of the current transformer and voltage transformer. The power metering error processing system was built with the support of Hadoop and Spark. The timing increment is introduced in the process of data collection. Dependent cloud and dynamic time-repair methods can improve the accuracy of diagnosing errors in electric energy metering. The parallel optimization of big data platforms by belonging to the cloud and dynamic time-warping algorithm is verified.

A Spatiotemporal Assessment of the Precipitation Variability and Pattern and an Evaluation of the Predictive Reliability of Global Climate Models over Bihar

Climate change is significantly altering precipitation patterns, leading to spatiotemporal changes throughout the world. In particular, the increased frequency and intensity of extreme weather events, leading to heavy rainfall, floods, and droughts, have been a cause of concern. A comprehensive understanding of these changes in precipitation patterns on a regional scale is essential to enhance resilience against the adverse effects of climate change. The present study, focused on the state of Bihar in India, uses a long-term (1901–2020) gridded precipitation dataset to analyze the effect of climate change. Change point detection tests divide the time series into two epochs: 1901–1960 and 1961–2020, with 1960 as the change point year. Modified Mann–Kendall (MMK) and Sen’s slope estimator tests are used to identify trends in seasonal and annual time scales, while Centroidal Day (CD) analysis is performed to determine changes in temporal patterns of rainfall. The results show significant variability in seasonal rainfall, with the nature of pre-monsoon and post-monsoon observed to have flipped in second epoch. The daily rainfall intensity during the monsoon season has increased considerably, particularly in north Bihar, while the extreme rainfall has increased by 60.6 mm/day in the second epoch. The surface runoff increased by approximately 13.43% from 2001 to 2020. Further, 13 Global Climate Models (GCMs) evaluate future scenarios based on Shared Socioeconomic Pathways (SSP) 370 and SSP585. The suitability analysis of these GCMs, based on probability density function (PDF), monthly mean absolute error (MAE), root mean square error (RMSE) and percentage bias (P-Bias), suggests that EC-Earth3-Veg-LR, MIROC6, and MPI-ESM1-2-LR are the three best GCMs representative of rainfall in Bihar. A Bayesian model-averaged (BMA) multi-model ensemble reflects the variability expected in the future with the least uncertainty. The present study’s findings clarify the current state of variability, patterns and trends in precipitation, while suggesting the most appropriate GCMs for better decision-making and preparedness.

Weighted Differential Gradient Method for Filling Pits in Light Detection and Ranging (LiDAR) Canopy Height Model

The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called “pixels weighted differential gradient”, to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction.