Bias Error Research Articles

Using Artificial Intelligence for Personalized Cancer Treatment

The rapid evolution of artificial intelligence and the tools it has created gives rise to significant opportunities for precision oncology and promises a brighter future for the development of the sector. Precision oncology is a modern and advancing branch of cancer treatment and it is a renovated approach which specializes on specific treatment plans rather than “one-size-fits”; with advanced technology including but not limited to genomics, proteomics and bioinformatics, precision oncology focuses on optimizing therapeutic efficacy by targeting the specific molecular and cellular characteristics of a patient's cancer. This review paper explores the sophisticated and complex potential roles of artificial intelligence in cancer treatment, emphasizing on its impact in personalized treatment plans, immunotherapy and diagnostic accuracy. The immense database that AI has access to enables medical professionals to analyze imaging results and detect similarities of symptoms, genetic patterns, offering enhanced prognostic and diagnostic capabilities by eliminating human error and increasing cost effectiveness. This paper also dives into applications such as immune checkpoint inhibitors, digital pathology and demonstrations of the remarkable capability of artificial intelligence in multi-omics integration. Nonetheless, the advent of such advanced artificial intelligence tools, although promising, have not yet become the “gold standard” in oncology due to many rising agitations regarding reliability, since treatment decisions made by opaque AI algorithms are prone to error due to potential biases in AI systems and design errors. By critically examining these points, this paper evaluates both the advantages and disadvantages of the future implementation of artificial intelligence in the field of precision oncology, aiming to devise optimal integration in clinical practice.

DUS Topp–Leone-G Family of Distributions: Baseline Extension, Properties, Estimation, Simulation and Useful Applications

This study introduces the DUS Topp–Leone family of distributions, a novel extension of the Topp–Leone distribution enhanced by the DUS transformer. We derive the cumulative distribution function (CDF) and probability density function (PDF), demonstrating the distribution’s flexibility in modeling various lifetime phenomena. The DUS-TL exponential distribution was studied as a sub-model, with analytical and graphical evidence revealing that it exhibits a unique unimodal shape, along with fat-tail characteristics, making it suitable for time-to-event data analysis. We evaluate parameter estimation methods, revealing that non-Bayesian approaches, particularly Maximum Likelihood and Least Squares, outperform Bayesian techniques in terms of bias and root mean square error. Additionally, the distribution effectively models datasets with varying skewness and kurtosis values, as illustrated by its application to total factor productivity data across African countries and the mortality rate of people who injected drugs. Overall, the DUS Topp–Leone family represents a significant advancement in statistical modeling, offering robust tools for researchers in diverse fields.

Parameter Identification in Triple-Diode Photovoltaic Modules Using Hybrid Optimization Algorithms

Identifying the parameters of a triple-diode electrical circuit structure in PV modules is a critical issue, and it has been regarded as an important research area. Accordingly, in this study, a differential evolution algorithm (DEA) is hybridized with an electromagnetism-like algorithm (EMA) in the mutation stage to enhance the reliability and efficiency of the DEA. A new formula is presented to adapt the control parameters (mutation factor and crossover rate) of the DEA. Seven different experimental data sets are used to improve the performance of the proposed differential evolution with an integrated mutation per iteration algorithm (DEIMA). The results of the proposed PV modeling method are evaluated with other state-of-the-art approaches. According to different statistical criteria, the DEIMA demonstrates superiority in terms of root mean square error and main bias error by at least 5.4% and 10%, respectively, as compared to other methods. Furthermore, the DEIMA has an average execution time of 27.69 s, which is less than that of the other methods.

Predicting Green Water Footprint of Sugarcane Crop Using Multi-Source Data-Based and Hybrid Machine Learning Algorithms in White Nile State, Sudan

Water scarcity and climate change present substantial obstacles for Sudan, resulting in extensive migration. This study seeks to evaluate the effectiveness of machine learning models in forecasting the green water footprint (GWFP) of sugarcane in the context of climate change. By analyzing various input variables such as climatic conditions, agricultural data, and remote sensing metrics, the research investigates their effects on the sugarcane cultivation period from 2001 to 2020. A total of seven models, including random forest (RF), extreme gradient boosting (XGBoost), and support vector regressor (SVR), in addition to hybrid combinations like RF-XGB, RF-SVR, XGB-SVR, and RF-XGB-SVR, were applied across five scenarios (Sc) which includes different combinations of variables used in the study. The most significant mean bias error (MBE) was recorded in RF with Sc3 (remote sensing parameters), at 5.14 m3 ton−1, followed closely by RF-SVR at 5.05 m3 ton−1, while the minimum MBE was 0.03 m3 ton−1 in RF-SVR with Sc1 (all parameters). SVR exhibited the highest R2 values throughout all scenarios. Notably, the R2 values for dual hybrid models surpassed those of triple hybrid models. The highest Nash–Sutcliffe efficiency (NSE) value of 0.98 was noted in Sc2 (climatic parameters) and XGB-SVR, whereas the lowest NSE of 0.09 was linked to SVR in Sc3. The root mean square error (RMSE) varied across different ML models and scenarios, with Sc3 displaying the weakest performance regarding remote sensing parameters (EVI, NDVI, SAVI, and NDWI). Effective precipitation exerted the most considerable influence on GWFP, contributing 81.67%, followed by relative humidity (RH) at 7.5% and Tmax at 5.24%. The study concludes that individual models were as proficient as, or occasionally surpassed, double and triple hybrid models in predicting GWFP for sugarcane. Moreover, remote sensing indices demonstrated minimal positive influence on GWFP prediction, with Sc3 producing the lowest statistical outcomes across all models. Consequently, the study advocates for the use of hybrid models to mitigate the error term in the prediction of sugarcane GWFP.

A probabilistic integration of LSTM and Gaussian Process Regression for uncertainty-aware reservoir water level predictions

ABSTRACT Reservoir-level forecasting while being crucial for optimal operation, is challenged by complex physical processes and changing climate conditions. Machine learning approaches offer deterministic predictions but often neglect system physics and uncertainty. This article presents a probabilistic data-driven approach combining Long Short-Term Memory (LSTM) and Gaussian Process Regression (GPR) to provide both point forecasts and uncertainty estimates. The hybrid model leverages LSTM’s fitting capabilities with GPR’s robust Bayesian frameworks for uncertainty estimation in nonlinear problems, offering accurate predictions without extensive high-fidelity modeling, and avoiding frequent training and parameter optimization. Evaluation with real reservoir data from India shows the model’s superiority over the vanilla LSTM for both univariate and multivariate scenarios. The proposed model achieved a Nash Sutcliffe efficiency of 0.97 to 0.98, a mean biased error of -0.5634 to -1.0314 for 10-day forecasts, and a continuous ranked probability score of 5.80 and 1.87 for the Bhakra and Pong reservoirs, respectively.

Differential Positioning with Bluetooth Low Energy (BLE) Beacons for UAS Indoor Operations: Analysis and Results

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique.

Unbiased Method to Determine Articular Cartilage Thickness Using a Three-Dimensional Model Derived from Laser Scanning: Demonstration on the Distal Femur

Measuring articular cartilage thickness from 3D models developed from laser scans has the potential to offer high accuracy. However, this potential has not been fulfilled, since generating these models requires that the cartilage be removed, and previous methods of removal have led to systematic errors (i.e., bias) due to changes in the overall dimensions of the underlying bone. The objectives were to present a new method for removing articular cartilage, quantify the bias error, and demonstrate the method on the distal (i.e., 0° flexion) and posterior (i.e., 90° flexion) articular surfaces of example human femurs. The method consisted of creating a 3D articular cartilage model from high-accuracy (i.e., precision = 0.087 mm) laser scans before and after cartilage removal using dermestid beetles to remove the cartilage. Fiducial markers were used to minimize errors in registering surfaces generated from the two laser scans. To demonstrate the method, the cartilage thickness was computed in distal and posterior subregions of each femoral condyle for three example cadaveric specimens. The use of dermestid beetles did not introduce measurable bias, and the previously reported precision achieved in 3D cartilage models with the laser scanner was 0.13 mm. For the different subregions, the cartilage thickness ranged from 1.5 mm to 2.0 mm. A method of imaging by means of laser scanning, cartilage removal by means of dermestid beetles, and 3D model registration by means of fiducial markers ensured that cartilage thickness on the articular surface of the long bones of the knee was determined with negligible bias and a precision of 0.13 mm. With this method, the potential to measure cartilage thickness with high accuracy based on 3D models developed from laser scans can be fully realized.

EFFICIENCY OF LOGARITHMIC RATIO CUM LOGARITHMIC PRODUCT ESTIMATORS IN DOUBLE SAMPLING USING IMPUTATION TECHNIQUES

In literature, there are many estimators of finite population mean, some of which are superior to the others. In practical situations, all the information on sample units may not be available due to non-response in sample surveys. Thus, our objective in this study is to get more precise estimators of the finite population mean of the study variable under two-phase sampling in case of missing data. Three new logarithmic ratio cum logarithmic product type imputation methods and corresponding point estimators have been introduced and observed to be better under two-phase sampling, which adds contribution to the field of imputation techniques. The bias and mean square errors of the proposed estimators are calculated in terms of population parameters. The performance of the proposed estimators is compared theoretically and empirically as well with existing traditional estimators.

New Modified Efficient Variance Estimators for the Estimation of Population Variance in Survey Sampling

For efficient and precise estimate of population variance, we have proposed some new ratio type variance estimators in the current research work by incorporating the linear combination of conventional and non-conventional parameters of auxiliary information. Bias and mean square error have been computed up to the first order of approximation. Numerical demonstration has been carried out to test the efficiency of new proposed estimators against existing estimators.

Small sample adjustment for inference without assuming orthogonality in a mixed model for repeated measures analysis

ABSTRACT The mixed model for repeated measures (MMRM) analysis is sometimes used as a primary statistical analysis for a longitudinal randomized clinical trial. When the MMRM analysis is implemented in ordinary statistical software, the standard error of the treatment effect is estimated by assuming orthogonality between the fixed effects and covariance parameters, based on the characteristics of the normal distribution. However, orthogonality does not hold unless the normality assumption of the error distribution holds, and/or the missing data are derived from the missing completely at random structure. Therefore, assuming orthogonality in the MMRM analysis is not preferable. However, without the assumption of orthogonality, the small-sample bias in the standard error of the treatment effect is significant. Nonetheless, there is no method to improve small-sample performance. Furthermore, there is no software that can easily implement inferences on treatment effects without assuming orthogonality. Hence, we propose two small-sample adjustment methods inflating standard errors that are reasonable in ideal situations and achieve empirical conservatism even in general situations. We also provide an R package to implement these inference processes. The simulation results show that one of the proposed small-sample adjustment methods performs particularly well in terms of underestimation bias of standard errors; consequently, the proposed method is recommended. When using the MMRM analysis, our proposed method is recommended if the sample size is not large and between-group heteroscedasticity is expected.

SsVERDICT: Self-supervised VERDICT-MRI for enhanced prostate tumor characterization.

Demonstrating and assessing self-supervised machine-learning fitting of the VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumors) model for prostate cancer. We derive a self-supervised neural network for fitting VERDICT (ssVERDICT) that estimates parameter maps without training data. We compare the performance of ssVERDICT to two established baseline methods for fitting diffusion MRI models: conventional nonlinear least squares and supervised deep learning. We do this quantitatively on simulated data by comparing the Pearson's correlation coefficient, mean-squared error, bias, and variance with respect to the simulated ground truth. We also calculate in vivo parameter maps on a cohort of 20 prostate cancer patients and compare the methods' performance in discriminating benign from cancerous tissue via Wilcoxon's signed-rank test. In simulations, ssVERDICT outperforms the baseline methods (nonlinear least squares and supervised deep learning) in estimating all the parameters from the VERDICT prostate model in terms of Pearson's correlation coefficient, bias, and mean-squared error. In vivo, ssVERDICT shows stronger lesion conspicuity across all parameter maps, and improves discrimination between benign and cancerous tissue over the baseline methods. ssVERDICT significantly outperforms state-of-the-art methods for VERDICT model fitting and shows, for the first time, fitting of a detailed multicompartment biophysical diffusion MRI model with machine learning without the requirement of explicit training labels.

Validation of 3D thermal simulations of the Double C Block, a novel composite masonry unit, using in-situ U-value measurements

Energy efficient building envelopes, particularly innovative prefabricated building components, can significantly contribute to lower the building industry energy-related emissions. In this research the composite masonry unit Double C Block (DCB) is quantitatively tested by means of three-dimensional thermal simulation validated by in-situ U-value measurements carried out in both summer and winter conditions.The simulations were enhanced by the experimental measurement of the dry thermo-physical material properties (specific heat capacity, thermal conductivity, and density) in laboratory conditions for both concrete and polyurethane. The samples were extracted from real-scale DCB prototypes. Furthermore, site-specific boundary conditions of Malta — representative a case study of a Mediterranean climate − were utilised in the simulations. Both laboratory and in-situ assessments of thermal transmittance, or U-value, had their uncertainty specified as part of the calculations.The validation exercise was performed by means of hourly recorded surface temperature and the selected indices were the root mean square error and mean absolute error. Results showed that these indices were within the recommended accuracy thresholds during both winter and summer assessments. For the hourly-based heat flux measurements, the mean bias error and coefficient of variation of the root mean square error were found lower than values reported in literature, thus supporting the robustness of the overall simulation outputs. Hence the 3D CVM simulation was experimentally validated by in-situ assessments.The convergence value of the experimental U-value provided respectively 1.51 ± 0.20 W/m2K (winter) and 1.44 ± 0.19 W/m2K (summer). Then, the validated 3D CVM model was adapted to calculate the theoretical U-value of DCB, using monthly values of recorded surface temperatures obtained by the test cells (in both seasons). The predicted thermal transmittance was of 1.47 W/m2K, in both conditions, and this almost matches the above-mentioned experimental assessments and thresholds established in previous works.

Improving PM10 sensor accuracy in urban areas through calibration in Timișoara

Low-cost particulate matter sensors (LCS) are vital for improving the spatial and temporal resolution of air quality data, supplementing sparsely placed official monitoring stations. Despite their benefits, LCS readings can be biased due to the physical properties of aerosol particles and device limitations. An optimization model is essential to enhance LCS data accuracy. This paper presents a calibration study of the LCS network of Timișoara, Romania. The calibration began by selecting LCS devices near National Air Quality Monitoring Network (NAQMN) stations and developing parametric models, choosing the best for broader application. Plantower, Sensirion, and Honeywell sensors showed comparable accuracy. Calibration involved clusters within a 750 m radius around NAQMN stations. Models incorporating RH corrections and multiple linear regression (MLR) were fitted. The best model was validated against data from unseen sensors, leading to mean bias errors (MBE) within 9-17% and RMSEs of 33-35%, within sensor uncertainty margins. Applied to the city-wide LCS network, the model identified several stations regularly exceeding the EU daily PM10 threshold, unnoticed by NAQMN stations due to their limited coverage. The study highlights the necessity of granular monitoring to accurately capture urban air quality variations.

A minute-resolution downscaling algorithm for high-resolution global irradiance time series

Numerous studies have demonstrated the significant impact of the resolution of solar irradiation data on the outcomes of hourly production models. Accurate integration of photovoltaic (PV) systems sometimes demands a high-resolution global horizontal irradiance (GHI) time series to capture the rapid fluctuations in PV power output induced by swift irradiance changes. Most of the available databases provide data at hourly resolution, leading to a lack of accuracy in PV simulations. Those existing hourly averages of global horizontal irradiance in open sources fail to represent this volatility adequately, especially when PV systems are coupled with fast ramp rate technologies. In the present work, an easy-to-use algorithm is implemented to synthesize high-resolution GHI time series from hourly averaged and clear sky irradiance datasets. By employing Linear interpolation, a technique that helps to achieve the desired time resolution and afterward computing critical factors, the algorithm identifies periods characterized by short-term weather phenomena, thus creating a high-resolution time series that accurately represents these dynamics. Avoiding the probabilistic components and machine learning techniques conserves computational power and reduces calculation time, but this comes at the cost of reduced fidelity in reproducing the results. Improving accuracy in PV simulations is not always directly related to reproducing real phenomena, but enhancing the amount of information contained in the data is sufficient. This study’s approach enhances user-friendliness and facilitates seamless integration into existing energy modeling frameworks, aiming for representation with sub-hourly time steps. The algorithm’s effectiveness is demonstrated by applying the model to hourly averaged data to revert them to a one-minute time step, and finally comparing the synthetically produced one-minute GHI data to the original measured data. The comparative analysis between synthesized and measured data demonstrated a strong agreement, with normalized mean bias error (MBE) values ranging between 1.8% and 9.6% and normalized root mean square error (NRMSE) values between 2.7% and 16.1%, depending on weather conditions. Additionally, the coefficient of determination (R2) consistently remained above 0.64. Successful algorithm validation makes our algorithm suitable for use in meteorological datasets and locations, with similar climatic characteristics.

Development of a Two-Step EOF Statistical Postprocessing Algorithm to Identify Patterns of Systematic Error and Variance within GEFSv12 Reforecasts

Abstract Analysis of seasonally varying signals of model error biases, as well as the diagnosis of space–time patterns of synoptic error, here is accomplished through the development of a statistical postprocessing algorithm based on time-extended empirical orthogonal functions (EEOFs) built on patterns of variance. This work analyzes GEFSv12 200-hPa geopotential height (Z200) model error against ERA5 reanalysis data. The mean-square error variance between GEFSv12 reforecast and ERA5 grows rapidly after day 7 of a forecast and continues to increase through the end of the 16-day forecast period. At lead time 16, the largest variance occurs in mid- to high-latitude oceanic storm tracks. Variance is highest during hemispheric winter when baroclinic energy is abundant. The seasonal cycle of error shows largest anomalies in the high latitudes during hemispheric winter. After running the titular two-step, space–time EEOF algorithm, a standardized spatial eigenspectrum shows that eigenvalues increase at longer lead times after decreasing in the medium range, demonstrating that the algorithm extracts large-scale signals of systematic error in the long range. Leading wintertime space–time eigenmode pairs include high-latitude blocking structures and Rossby wave trains. Results suggest that the large-scale systematic errors in GEFSv12 Z200 initiate in part from inaccurate representations of phase speeds of atmospheric waves in the polar jet.

A Global Evaluation of Radar‐Derived Digital Elevation Models: SRTM, NASADEM, and GLO‐30

AbstractThis study evaluates global radar‐derived digital elevation models (DEMs), namely the Shuttle Radar Topography Mission (SRTM), NASADEM and GLO‐30 DEMs. We evaluate their accuracy over bare‐earth terrain and characterize elevation biases induced by forests using global Lidar measurements from the Ice, Cloud, and Land Elevation Satellite (ICESat)'s Geoscience Laser Altimeter System (GLAS), the Global Ecosystem Dynamics Investigation (GEDI) and the ICESat‐2 Advanced Topographic Laser Altimeter System (ATLAS) instruments collected on locally flat terrain. Our analysis is based on error statistics calculated for each DEM tile, which are then summarized as global error percentiles, providing a regional characterization of DEM quality. We find NASADEM to be a significant improvement upon the SRTM V3. Over bare ground areas, the mean elevation bias and root mean square error (RMSE) improved from 0.68 to 2.50 m respectively to 0.00 and 1.5 m as compared to ICESat/GLAS. GLO‐30 is more accurate with bare ground elevation bias and RMSE were below 0.05 and 0.55 m. Similar improvements were observed when compared to GEDI and ICESat‐2 measurements. The DEM biases associated with the presence of vegetation vary linearly with canopy height, and more closely follow the percentile of Lidar Relative Height (RH50). Other factors such as canopy density, radar frequency and Lidar technology also contribute to observed elevation biases. This global analysis highlights the potential of various technologies for mapping of Earth's topography, and the need for more advanced remote sensing observations that can resolve vegetation structure and sub‐canopy ground elevation.

Regional frequency analysis of extreme wind in Pakistan using robust estimation methods

The quantile estimates of extreme wind speed are needed for various areas of interest using regional frequency analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meters per second (m/s). RFA of annual maximum wind speed (AMWS) using L-moments is performed utilizing annual maximum wind speed data from sixteen sites (16) in Pakistan’s Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions is chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, the GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing relative bias (RB) and relative root mean square error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

New multivariate models for the estimation of earth’s albedo and its variation with meteorological parameters over Kano, Nigeria

The study of what happens to sunlight as it goes through the atmosphere is vital to many fields of science because over time, reflection, scattering, and absorption in the atmosphere have changed the amount of solar radiation that reaches the Earth's surface. This study examined the variation in albedo over Kano, situated in the Sahelian climatic zone of Nigeria. The study's data, which covered the years 1984–2022, totaled thirty-nine, was obtained from the National Aeronautics and Space Administration (NASA). Utilizing monthly average daily measured meteorological data on global solar radiation, wind speed, mean temperature, surface pressure, and relative humidity, eleven models of three categories based on two, three, and four variable regression models were developed. Five validation indicators were used to verify the regression models: the t-test, coefficient of determination (R2), mean percentage error (MPE), root mean square error (RMSE), and mean bias error (MBE). The developed models that were considered most appropriate for estimating surface albedo were ranked; the three-variable regression model ALB7, which included the wind speed, relative humidity, and mean temperature, outperformed the previously employed generalized method from literature in estimating surface albedo over Kano. The variation of measured albedo with meteorological parameters depicts a direct relationship for clearness index and wind speed; an inverse relationship was observed for surface pressure. Mean temperature and surface pressure exhibit an inverse relationship during the wet season and vice versa for the dry season.

Evaluation of WRF model performance with different microphysics schemes for extreme rainfall prediction in Lagos, Nigeria: Implications for urban flood risk management

In Nigeria, particularly in urban areas like Lagos, flooding is a frequent natural hazard. In 2011, Lagos experienced one of its worst floods resulting in significant economic losses and displacement of people. In recent years, Lagos has continued to grapple with flooding challenges, with an equally significant flood episode occurring in 2021. This study focuses on predicting floods and forecasting extremely heavy rainfall in West Africa's equatorial zone using the Weather Research and Forecasting (WRF) model, particularly in humid tropical environments like Lagos. The study discusses the need to review existing flood models and adopt alternative flood models to address the limitations of flood prediction. As potential causes of these rainfall episodes, the interconnections between synoptic systems such as tropical easterly waves, southwesterly winds related to the West African Monsoon, and local topography and oceanic conditions are investigated. Three key metrics: root mean square error (RMSE), mean bias (MB), and mean absolute error (MAE) are used to assess the effectiveness of the computational model. Results indicate that the WRF model, specifically when using the Thompson parameterisation, can estimate the amount of rainfall accumulated over a 24-h period. This suggests that the model can predict the size of daily precipitation during intense rain events. The Thompson scheme shows better performance compared to the WSM6 scheme while evaluating the stations and episodes. During the rainfall episode on July 10, 2011, Thompson's spatial rainfall predictions were better than WSM6, resulting in a decrease in root mean square error (RMSE) of 15–31% depending on the area. Simulations of the July 2021 episode also show better performance, with a decrease in RMSE of 11–25% when comparing Thompson to WSM6 scheme. The Thompson scheme’s improved ability is directly linked to a more accurate depiction of the microphysical mechanisms that control the rainfall formation. By explicitly simulating the dynamics of ice crystals and graupel, it is possible to accurately replicate the processes of orographic lifting and moist convection that are responsible for driving intense monsoon precipitation. In addition, Thompson scheme shows a reduced degree of systemic bias in comparison to WSM6, with a 75% reduction in the average bias in rainfall accumulation over the research area. The combination of the advanced Thompson microphysics method and WRF's atmospheric dynamics shows a high level of accuracy in predicting intense rainfall and the risk of floods in this area with diverse tropical topography.

Statistical inference and data analysis for inverted Kumaraswamy distribution based on maximum ranked set sampling with unequal samples

A very useful modification to ranked set sampling (RSS) that allows a larger set size without significantly increasing ranking errors is the maximum ranked set sampling with unequal samples (MRSSU) approach. This article covers the parameter estimation of the inverted Kumaraswamy distribution using MRSSU and RSS designs. The maximum likelihood and Bayesian estimation techniques are considered. The regarded Bayesian estimation technique is determined in the case of non-informative and informative priors represented by Jeffreys and gamma priors, respectively. Squared error and minimum expected are the two loss functions that are employed. We presented a simulation study to evaluate the performance of the recommended estimations using root mean squared error and relative bias. The Bayes point estimates were computed using the Metropolis–Hastings algorithm. Additional conclusions have been made based on actual geological data regarding the intervals between Kiama Blowhole’s 64 consecutive eruptions. Based on the same number of measured units, the results of simulation and real data analysis showed that MRSSU estimators performed much better than their RSS counterparts.