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The spatial mapping and prediction of groundwater quality (GWQ) is important for sustainable groundwater management, but several research gaps remain unexplored, including the inaccuracy of spatial interpolation, limited consideration of the geological environment and human activity effects, limitation to specific pollutants, and unsystematic indicator selection. This study utilized the entropy-weighted water quality index (EWQI), the LightGBM model, the pressure-state-response (PSR) framework and SHapley Additive exPlanations (SHAP) analysis to address the above research gaps. The normalized importance (NI) shows that NO3− (0.208), Mg2+ (0.143), SO42− (0.110), Cr6+ (0.109) and Na+ (0.095) should be prioritized as parameters for remediation, and the skewness EWQI distribution indicates that although most sampled locations have acceptable GWQ, a few areas suffer from severely poor GWQ. The PSR framework identifies 13 indicators from geological environments and human activities for the SMP of GWQ. Despite high AUROCs (0.9074, 0.8981, 0.8885, 0.9043) across four random training and testing sets, it was surprising that significant spatial uncertainty was observed, with Pearson correlation coefficients (PCCs) from 0.5365 to 0.8066. We addressed this issue by using the spatial-grid average probabilities of four maps. Additionally, population and nighttime light are key indicators, while net recharge, land use and cover (LULC), and the degree of urbanization have the lowest importance. SHAP analysis highlights both positive and negative impacts of human activities on GWQ, identifying point-source pollution as the main cause of the poor GWQ in the study area. Due to the limited research on this field, future studies should focus on six key aspects: multi-method GWQ assessment, quantitative relationships between indicators and GWQ, comparisons of various spatial mapping and prediction models, the application of the PSR framework for indicator selection, the development of methods to reduce spatial uncertainty, and the use of explainable machine learning techniques in groundwater management. [ABSTRACT FROM AUTHOR]

Abstract Expectation of a future stimulus increases the preparedness to act once it actually appears and results in reduced latency of the appropriate motor response. Real world events are uncertain both spatially and/or temporally but this uncertainty could itself be expected. In the presence of both expected spatial and temporal uncertainty, which one should be prioritized by the motor system could depend on the context. Therefore, we investigated the relative weight of expected spatial and temporal uncertainty during the preparation of a saccadic eye movement. A reaction time task was used with a variable foreperiod between a warning and an imperative visual stimuli. Expected temporal and/or spatial uncertainty associated with the stimulus was cued. We found that before imperative stimulus onset, pupil dilation increased with expected temporal uncertainty but was unaltered by spatial uncertainty. After imperative stimulus onset, both types of expected uncertainty affected saccade latency. Maximum eye velocity was modulated by expected spatial uncertainty only. In conclusion, expected temporal and spatial uncertainty do not have the same impact on preparation and execution of a motor response. There could be a prioritization of the relevant information as a function of the evolving expected uncertainty context during the task.

The rapid development of information and communications technology has turned individuals into sensors, fostering the growth of human-generated geospatial big data. In disaster management, geospatial big data, mainly social media data, have opened new avenues for observing human responses to disasters in near real-time. Previous research relies on geographical information in geotags, content, and user profiles to locate social media messages. However, less than 1% of users geotag their messages, leaving geolocating users through user profiles or message content addresses increasingly crucial. This paper evaluates and visualizes the margin of error incurred when using user profiles or message-mentioned addresses to geolocate social media data for disaster research. Using Twitter data during the 2017 Hurricane Harvey as an example, this research assessed the inconsistencies in predicting users' locations in various administrative units during each disaster phase using three geolocating strategies. The results reveal that the similarities between geotags, and user profile locations decrease from 94.07% to 64.56%, 43.9%, 31.82%, 27.05%, and 26.7% as the geographical scale changes from country to state, county, block group, 1-kilometer, and 30-meter levels. These similarities are overall higher than the agreements between locations derived from geotags and tweet content. The geolocation consistencies among the three methods remain stable across disaster phases. The impacts of uncertainties in geolocating Twitter data for disaster management applications were further unraveled. The findings offer valuable insights into the trade-off between spatial scale and geolocation accuracy and inform the selection of appropriate scales when applying different geolocating strategies in future social media-based investigations. [ABSTRACT FROM AUTHOR]

The ability of the dolphin auditory system to recognize and classify noise signals according to certain invariant characteristics under the influence of noise interference and in conditions of spatial uncertainty of the simultaneous presentation of positive and negative signals was investigated. Bottlenose dolphins trained to differentiate such signals had to solve this problem in conditions simulating real sea conditions, when the perception of a useful noise signal occurs against a background of similar signals and against a noise interference background. First, noise signals were sequentially presented to the animal against a background of white masking noise. Subsequently, the dolphin had to identify a signal of a positive class from several simultaneously sounding sound sources. The animal's performance was assessed at several specified noise interference levels. In this case, the actual noise interference was both white noise and simultaneously sounding negative signals. It has been shown that the efficiency and noise immunity of the dolphin's auditory system depends on the degree of alternativeness of the spatial uncertainty of the simultaneous presentation of signals. [ABSTRACT FROM AUTHOR]

Aim: Many biogeographic analyses require some form of automated state assignment to tips of phylogenetic trees, reflecting a species presence or absence in a particular area, e.g., a biome. As datasets get exponentially larger, such procedures may increasingly induce errors (here called tip‐state‐error), but the specific algorithmic cause and consequence on downstream estimation of dispersal and extinction rates remains poorly known. We aim to improve automated tip‐scoring methods in the context of the alpine biome by leveraging elevation information. We document the profound effect of tip‐state‐errors on Dispersal‐Extirpation‐Cladogenesis (DEC) models. Location: The European Alpine Arc. Taxon: Three thousand three hundred seventeen vascular plant species, emphasizing six focal genera: Campanula, Carex, Festuca, Ranunculus, Saxifraga, and Viola. Methods: We use GBIF data to classify whether species occur above the upper climatic treeline using a newly developed algorithm ElevDistr or a gridded landscape model of thermal belts, under various filtering thresholds. We compared classification performance using the Flora Alpina as validation data. To determine if tip‐state‐error biases the dispersal and extirpation rate estimation, we fit DEC models for selected clades using tip‐states from different classification models. Results: ElevDistr is less error prone than other approaches. Filtering thresholds lower the false positive rate but increase the false negative rate. Inflated false positive rates bias the dispersal rate estimation upward, while inflated false negative rates lead to upward bias in extirpation rate estimation. Main Conclusions: Even moderate tip‐state‐error may lead to profound systematic bias in dispersal and extinction rate estimation if an unbalanced ratio between false positive and false negative rates occurs. Therefore, careful validation is imperative, though ElevDistr alleviates this problem in the context of the alpine environment. Overall, our results suggest contrasting rates of alpine biome shifts across the studied genera and have major implications for studies addressing the likelihood of niche evolution versus geographic dispersal. [ABSTRACT FROM AUTHOR]

The prime-probe version of the Stroop task has been predominantly used to demonstrate the context-specific proportion congruency (CSPC) effect. In this version, the location of the color is not known until its presentation, creating a spatial uncertainty for the color dimension. We propose that spatial uncertainty plays an important role in observing the CSPC effect. In this study, we investigated the role of spatial uncertainty with two experiments. In Experiment 1 (N = 53), we used a spatially separated version of the Stroop task having spatial uncertainty on the color dimension, and observed a significant CSPC effect. For Experiment 2, we conducted a preregistered prime-probe CSPC experiment with a considerably large sample (N = 128), eliminating the uncertainty of only the color dimension in one condition and both the color and the word dimensions in the other. Results showed that the CSPC effect was not observed in the first condition, while it was very small yet significant in the second condition. The Bayesian approach confirmed frequentist analyses of Experiment 1 and the first condition of Experiment 2. However, in the second condition of Experiment 2, there was no evidence regarding the existence of the CSPC effect. These findings support our claim that the spatial uncertainty of the color dimension, inherent in the prime-probe version Stroop task, contributed to the CSPC effect. [ABSTRACT FROM AUTHOR]

• APAA performance is analyzed through a stochastic multi-physics model. • The random multi-physics responses can be accurately calculated. • Huge amount of computational cost is saved compared with the MCS method. A random field based thermal-structural-electromagnetic (RF-TSE) coupling model is proposed to deal with the stochastic analysis of the Active Phased Array Antenna (APAA) performance considering material spatial uncertainty. Firstly, the spatial uncertainty of APAA material parameters is characterized by the random field model. Then, the material spatial uncertainty is embedded into the thermal-structural coupling analysis of APAA and the random displacement information of APAA units is calculated. To avoid supplemental analytical error of the APAA performance, the obtained deformation results are directly introduced into the antenna radiation pattern function without any additional assumptions. Finally, the stochastic spatial phase error caused by deformation is further calculated and utilized to obtain the statistical information of the APAA performance based on the power pattern function. The validity of the proposed RF-TSE model is fully investigated and the analytical results show that it can obtain very accurate statistical information of APAA electromagnetic (EM) performance and the computational efficiency can also be greatly improved compared with the Monte-Carlo simulation (MCS) approach. [ABSTRACT FROM AUTHOR]

With the emergence of the coronavirus disease 2019 (COVID-19) pandemic in Portugal, a geostatistical tool was developed to model the spatial distribution of COVID-19 risk to support decision-making and policymakers. Based on a block direct sequential simulation algorithm, the model provides detailed disease risk estimates and associated spatial uncertainty. However, uncertainty is difficult to visualize with the estimated risk, and is usually overlooked as a tool to support decision-making. Ignoring uncertainty can be misleading in evaluating risk, since the amount of uncertainty varies throughout the spatial domain. The EpiGeostats R package was developed to solve this problem, since it integrates the geostatistical model and visualization tools to deliver a single map summarizing disease risk and spatial uncertainty. This paper briefly describes the methodology and package functions implemented for interfacing with the tools in question. The use of EpiGeostats is illustrated by applying it to real data from COVID-19 incidence rates on mainland Portugal. EpiGeostats is a powerful tool for supporting decision-making in the context of epidemics, since it combines a well-established geostatistical model for disease risk mapping with simple and intuitive ways of visualizing results, which prevent fine-scale inference in regions with high-risk uncertainty. The package may be used for similar problems such as mortality risk, or applied to other fields such as ecology or environmental epidemiology. [ABSTRACT FROM AUTHOR]

Understanding land-use dynamics and patterns and how they respond to management scenarios helps to develop sustainable land use policies. This study simulated future land uses in Brantas River Basin (BRB), East Java, Indonesia using Land Change Modeler (LCM) under policy scenarios. Following identification of twelve important biophysical and socio-economic spatial drivers, the LCM accurately simulated land-use changes over the period 1995–2015, with validation against the 1995 land use map giving an overall accuracy of 85–88 %. Under a business-as-usual scenario, the LCM predicted that during the period 2015 to 2035 continuing deforestation may leave forest cover accounting for only 4 % of the total BRB area, and cause declines in dryland from 45 % to 40 % and in rice-field farming from 24 % to 22 %. The results of that scenario also indicate the prospect of massive urban development, increasing from 18 % of the BRB area in 2015 to 30 % in 2035, with increasing threats to food security and water resources. Both percentage changes and a mean-weighted fractal dimension index suggest increased risks of forest fragmentation and urban aggregation. A spatial planning-influenced scenario, which aims to reduce forest loss and support watershed protection, is predicted to lead to 5.5 % forest cover, 37 % dryland, 29 % rice-field farming and 24 % urban area in 2035. Sensitivity analysis of the LCM showed that the model results were more sensitive to drivers, spatial resolution, and policy scenarios than to uncertainty in model parameters. It is concluded that despite some limitations, the LCM successfully provided'' insights into policy impacts on land-uses in BRB, and the roles of forest and rice-field protection in spatial planning are essential in controlling urban development for watershed sustainability.

The spatial mapping and prediction of groundwater quality (GWQ) is important for sustainable groundwater management, but several research gaps remain unexplored, including the inaccuracy of spatial interpolation, limited consideration of the geological environment and human activity effects, limitation to specific pollutants, and unsystematic indicator selection. This study utilized the entropy-weighted water quality index (EWQI), the LightGBM model, the pressure-state-response (PSR) framework and SHapley Additive exPlanations (SHAP) analysis to address the above research gaps. The normalized importance (NI) shows that NO3− (0.208), Mg2+ (0.143), SO42− (0.110), Cr6+ (0.109) and Na+ (0.095) should be prioritized as parameters for remediation, and the skewness EWQI distribution indicates that although most sampled locations have acceptable GWQ, a few areas suffer from severely poor GWQ. The PSR framework identifies 13 indicators from geological environments and human activities for the SMP of GWQ. Despite high AUROCs (0.9074, 0.8981, 0.8885, 0.9043) across four random training and testing sets, it was surprising that significant spatial uncertainty was observed, with Pearson correlation coefficients (PCCs) from 0.5365 to 0.8066. We addressed this issue by using the spatial-grid average probabilities of four maps. Additionally, population and nighttime light are key indicators, while net recharge, land use and cover (LULC), and the degree of urbanization have the lowest importance. SHAP analysis highlights both positive and negative impacts of human activities on GWQ, identifying point-source pollution as the main cause of the poor GWQ in the study area. Due to the limited research on this field, future studies should focus on six key aspects: multi-method GWQ assessment, quantitative relationships between indicators and GWQ, comparisons of various spatial mapping and prediction models, the application of the PSR framework for indicator selection, the development of methods to reduce spatial uncertainty, and the use of explainable machine learning techniques in groundwater management.

Agriculture is considered one of the primary elements for socioeconomic stability in most parts of Sudan. Consequently, the irrigation water should be properly managed to achieve sustainable crop yield and soil fertility. This research aims to predict the irrigation indices of sodium adsorption ratio (SAR), sodium percentage (Na%), permeability index (PI), and potential salinity (PS) using innovative machine learning (ML) techniques, including K-nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and Gaussian process regression (GPR). Thirty-seven groundwater samples are collected and analyzed for twelve physiochemical parameters (TDS, pH, EC, TH, Ca+2, Mg+2, Na+, HCO3−, Cl, SO4−2, and NO3−) to assess the hydrochemical characteristics of groundwater and its suitability for irrigation purposes. The primary investigation indicated that the samples are dominated by Ca-Mg-HCO3 and Na-HCO3 water types resulted from groundwater recharge and ion exchange reactions. The observed irrigation indices of SAR, Na%, PI, and PS showed average values of 7, 42.5%, 64.7%, and 0.5, respectively. The ML modeling is based on the ion’s concentration as input and the observed values of the indices as output. The data is divided into two sets for training (70%) and validation (30%), and the models are validated using a 10-fold cross-validation technique. The models are tested with three statistical criteria, including mean square error (MSE), root means square error (RMSE), and correlation coefficient (R2). The SVR algorithm showed the best performance in predicting the irrigation indices, with the lowest RMSE value of 1.45 for SAR. The RMSE values for the other indices, Na%, PI, and PS, were 6.70, 7.10, and 0.55, respectively. The models were applied to digital predictive data in the Nile River area of Khartoum state, and the uncertainty of the maps was estimated by running the models 10 times iteratively. The standard deviation maps were generated to assess the model’s sensitivity to the data, and the uncertainty of the model can be used to identify areas where a denser sampling is needed to improve the accuracy of the irrigation indices estimates.

Objective Numerous studies support that simple visual search tests may not be sufficient to differentiate children with and without attention-deficit/hyperactivity disorder (ADHD), especially for older school-aged children. This study aimed to explore whether the high spatial uncertainty visual search tasks can effectively discriminate older school-aged children with ADHD from their typically developing (TD) peers. Method In a randomized, two-period crossover design, 122 school-aged children (61 ADHD and 61 TD subjects), aged 10–12 years old, were measured using comparable visual search tasks with structured and unstructured layouts. Results First, the discriminant effectiveness of unstructured visual search tasks, which are associated with high-level spatial uncertainty, is superior to structured ones. Second, combining accuracy and speed into a Q score is a more sensitive measure than accuracy or time calculated alone in visual search tasks. A more in-depth ROC analysis showed that all variables could accurately identify ADHD from their TD peers under unstructured visual search tasks, with the index of the Q score performing best (AUR = 0.956). Third, the development of detectability, which represents the ability to distinguish between target and non-target, is approaching maturity in 10–12-year-old children with ADHD. However, these children showed severe deficits in dealing with disorganized distractors when performing visual search tasks with high-level spatial uncertainty. Conclusions The findings of this study support that older school-aged children with ADHD demonstrate less efficient search performance than their TD peers in complex/difficult visual search tasks, especially under higher spatial uncertainty. [ABSTRACT FROM AUTHOR]

Currently, aboveground biomass (AGB) estimation primarily relies on an area-based approach (ABA), particularly at a large scale. With the advancement of individual tree detection techniques and the availability of multi-platform remotely sensed data, the individual tree-based approach (ITA) provides the potential of accurate and nondestructive fine AGB mapping. However, the performances of the two approaches on upscaling AGB from individual tree to stand level have not been thoroughly investigated. This study conducted the nondestructive AGB estimation using both ITA and ABA and compared the AGB estimates and their spatial uncertainties from an individual tree- to stand-level based on multi-platform LiDAR and Landsat 8 OLI imagery, taking Larix olgensis that is one of the most notable afforestation tree species in northeastern China as an example. Results showed that the point cloud segmentation (PCS) outperformed CHM-based individual tree crown delineation algorithms and obtained the highest accuracy of individual tree AGB estimate (R2 = 0.97, RMSE = 28.58 kg, rRMSE = 21.13%) for the dense larch plantations (about 1265 trees/ha). The plot-level AGB estimate aggregated by all detected trees and its uncertainty based on Monte Carlo simulation were 158.16 and 3.64 Mg·ha−1, respectively. The average pixel-level AGB of larch plantations in Maorshan Forest Farm estimated by ITA and ABA were similar (129.66 v.s 144.38 Mg·ha−1). ITA outperformed ABA in terms of pixel-level AGB accuracy and spatial uncertainty of pixel-level and stand-level AGB estimates. The overestimation of low AGB values, typical in ABA, was effectively eliminated by the ITA. The upscaling frameworks proposed in this study for AGB estimation and spatial uncertainty quantification based on ITA and ABA could be extended to other plantations or uncomplex forests. This study contributes to the accurate quantification of AGB and understanding of the uncertainties in the carbon stock of forest ecosystems at multi-scales.

Deep neural network algorithms have achieved impressive performance in object detection. Real-time evaluation of perception uncertainty from deep neural network algorithms is indispensable for safe driving in autonomous vehicles. More research is required to determine how to assess the effectiveness and uncertainty of perception findings in real-time.This paper proposes a novel real-time evaluation method combining multi-source perception fusion and deep ensemble. The effectiveness of single-frame perception results is evaluated in real-time. Then, the spatial uncertainty of the detected objects and influencing factors are analyzed. Finally, the accuracy of spatial uncertainty is validated with the ground truth in the KITTI dataset. The research results show that the evaluation of perception effectiveness can reach 92% accuracy, and a positive correlation with the ground truth is found for both the uncertainty and the error. The spatial uncertainty is related to the distance and occlusion degree of detected objects. [ABSTRACT FROM AUTHOR]

Translational rockslides are an important disaster that endangers the safety of mountain towns. The dip slopes with weak interlayers are the areas prone to translational rockslides. The regional translational rockslide susceptibility assessment should consider the sliding mechanism and spatial distribution uncertainty analysis of the weak interlayer. Taking Tiefeng Town of Wanzhou District as the study area, based on the detailed investigation of the material, structure and spatial distribution of weak interlayers, this paper analysed the evolution mechanism of shale and mudstone developing into sliding surfaces under the action of primary deposition, tectonic deformation and supergene transformation and summarized the deformation and failure mechanism of translational rockslides. Considering the spatial distribution uncertainty of the weak interlayer, a calculation model of the vertical distribution of the weak interlayer and the contribution of the weak interlayer to slip control in the effective control depth are proposed. The key factors that characterize the sliding structure, including the type of weak interlayer and the contribution degree of the weak interlayer, are selected as susceptibility assessment factors. In addition, four elements, topography, slope structure, hydrogeology and human activities, are considered. The susceptibility of translational rockslides in the study area was assessed with slope units adopting the analytic hierarchy process. The investigation and assessment results show that the mud interlayers of the Jurassic Zhenzhuchong Formation and the shale layer of the Ziliujing Formation are the main potential slip surfaces of the translational rockslide in the study area. The extremely high susceptibility area and high susceptibility area accounted for 9.7% and 25.8%, respectively. The distribution of the underlying weak interlayer and the excavation of the slope units are the main factors affecting the susceptibility to landslide disasters. Human activities such as house building and road excavation are the main triggering factors of translational rockslides.Compared with the susceptibility assessment results without considering the factors of the weak interlayer, the results of the method proposed in this paper are more consistent with the facts.

Purpose: To evaluate the spatial accuracy of radiation centers (RCs) of Elekta stereotactic cones while changing the cone sizes during intra-fraction treatment of multiple brain metastases (MBM). Materials and methods: In this study, nine cones of varying sizes (5, 10, 12.5, 15, 17.5, 20, 25, 30, and 35 mm) were used. The ball bearing (BB) was used as the target. The positions of BBs and RCs obtained using electronic portal imaging device (EPID) were transformed to a coordinate system X/Y/Z that was fixed with respect to the treatment room. Results: The positions of BBs when using cones sized between 12.5 and 35 mm were relatively stable. The maximum deviations between BBs and RCs were as follows: 1.11 mm in negative X and 1.30 mm in positive X, 0.87 mm in negative Y and 0.69 mm in positive Y, and 0.06 mm in negative Z and 0.58 mm in positive Z. The maximum offset between BBs and RCs gradually increased as the cone size decreased in the X and Y directions. The maximum offsets were 2.30, 1.56, and 0.64 mm in the X, Y, and Z directions, respectively, for the 12.5-mm cone. The maximum distance between the RCs was 0.43 mm when using the G135T270 beam between the 17.5-mm and 12.5-mm cones. Conclusion: The treatment of MBM with Elekta stereotactic cones demonstrated good spatial uncertainty. However, we need to pay particular attention to treatment using two or more cones in clinical practice. More external expansion margins are required in such cases.

Characterization of the response of systems with governing parameters that exhibit both uncertainties and spatial dependencies can become quite challenging. In these cases, the accuracy of conventional probabilistic methods to quantify the uncertainty may be strongly affected by the availability of data. In such a scenario, fuzzy fields become an efficient tool for solving problems that exhibit uncertainty with a spatial component. Nevertheless, the propagation of the uncertainty associated with input parameters characterized as fuzzy fields towards the output response of a model can be quite demanding from a numerical point of view. Therefore, this paper proposes an efficient numerical strategy for forward uncertainty quantification under fuzzy fields. This strategy is geared towards the analysis of steady-state, linear systems. To reduce the numerical cost associated with uncertainty propagation, full system analyses are replaced by a reduced-order model. This reduced-order model projects the equilibrium equations into a small-dimensional space constructed from a single analysis of the system plus sensitivity analysis. The associated basis is enriched to ensure the quality of the approximate response and numerical cost reduction. Case studies of heat transfer and seepage analysis show that with the presented strategy, it is possible to accurately estimate the fuzzy responses with reduced numerical effort. • Implements a Fuzzy Field approach to address spatial uncertainties. • Proposes a Reduced-Order Model for efficient uncertainty propagation. • Develops adaptive basis enrichment for accurate fuzzy responses. • Validates efficiency and accuracy through heat flow and seepage studies. [ABSTRACT FROM AUTHOR]

• The spatially uncertain parameters are described by the interval field model with upper and lower bounds. • A nonlinear interval finite element method is proposed for elastic–plastic analysis of structures with spatially uncertain parameters. • The upper and lower bounds of the responses obtained by the proposed method could envelop the exact response bounds, and the range of exact bounds can be determined by the outer solutions obtained by the proposed method and the inner solutions. This paper proposes a nonlinear interval finite element method for elastic–plastic analysis of structures with spatially uncertain parameters. The spatially uncertain parameters are described by the interval field, and the variation bounds of the elastic–plastic structural responses can be calculated effectively. Quantified by the interval field, the spatially uncertain parameters are represented by the interval Karhunen–Loève (K-L) expansion, based on which the nonlinear interval finite element equilibrium equation is formulated. An interval iterative method is then presented to solve the equilibrium equation and obtain an outer solution of the variation bounds of structural responses such as displacement. In this method, the Newton-Raphson iterative method is used to transform the nonlinear problem into a linear one, and then the interval iterative method is introduced to solve the interval linear equations. Three numerical examples are employed to illustrate the feasibility and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract The International Geomagnetic Reference Field (IGRF) is a multi-institute model of the Earth’s magnetic field, compactly described by sets of up to 195 spherical harmonic (Gauss) coefficients to degree and order 13, which allows the continuous evaluation of the field at any location and time on or above the surface. It is developed from satellite and ground-based magnetometer data and describes the large-scale variation of the magnetic field in space and time under quiet conditions. While much effort has been made on improving the forecast of the secular variation of the field over the 5-year intervals between release and renewal, less emphasis has been placed on understanding the spatial errors from a user point of view. In this study, we estimate the large-scale time-invariant spatial uncertainty of the IGRF based on the globally averaged misfit of the model to ground-based measurements at repeat stations and observatories between 1980 and 2021. As the ground measurements are reduced to quiet-time values, the external field is minimized for the purposes of this study. We find the 68.3% confidence interval is 87 nT in the North (X) component, 73 nT in the East (Y) component and 114 nT in vertical (Z) component. Due to the Laplacian distribution of the residuals, the standard deviations are larger at 144, 136 and 293 nT, respectively. Graphical Abstract

Mapping of geochemical anomalies is crucial to exploration and environmental geochemistry. The complex geochemical landscape, multiple geological sources and various secondary surficial processes impose a certain degree of spatial uncertainty in mapping of geochemical anomalies. Quantifying this uncertainty is significant for improving the efficiency of environmental monitoring or mineral prospecting. In this paper, sequential indicator simulation (SISIM) was used to assess local and spatial uncertainties of geochemical anomalies, and the concentration–area (C–A) fractal model was employed to determine the geochemical threshold prior to SISIM analysis. To illustrate the uncertainty of Ag geochemical anomalies, Ag concentration data of 1880 soil samples collected from NE of the Dong Ujimqin Banner district of Inner Mongolia, North China, was used in this study. Based on a set of simulation realizations of Ag concentrations, it was concluded that areas with low local (i.e. single-location) uncertainty of Ag concentrations entail low risk for mineral exploration. However, the spatial uncertainty for multi-locations showed that the joint probability statistics were stricter than local uncertainty. Therefore, combining local probability and spatial joint probability for delineating geochemical anomalies of Ag is more acceptable and reliable. The hybrid approach using the C–A fractal model and SISIM provides a new way to delineate anomalous areas by considering the uncertainty of spatial distributions of geochemical elements. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/topic/collections/applications-of-innovations-in-geochemical-data-analysis [ABSTRACT FROM AUTHOR]

Herbarium records provide a broad spatial and temporal range with which to investigate plant responses to environmental change. Research on plant phenology and its sensitivity to climate has advanced with the increasing availability of digitized herbarium specimens, but limitations of specimen‐derived data can undermine the inferences derived from such research. One issue that has received little attention is collection site uncertainty (i.e. error distance), a measure of confidence in the location from which a specimen was collected. We conducted comparative analyses of phenoclimatic models to determine whether spatial deviations of 2, 5, 15 or 25 km between recorded and simulated collection sites, as well as the error distance reported in digitized records, affect estimates of the phenological sensitivity of flowering time to annual temperature and precipitation in a widespread annual California wildflower. In this approach, we considered both spatial and interannual variation in climatic conditions. Simulated site displacements led to increasingly weak estimates of phenological sensitivity to temperature and precipitation anomalies with increasing distances. However, we found no significant effect of reported error distance magnitude on estimates of phenological sensitivity to climate normals or anomalies. These findings suggest that the spatial uncertainty of collection sites among specimens of widely collected plant species may not adversely affect estimates of phenological sensitivity to climate, even though real discrepancies and georeferencing inaccuracy can negatively impact such estimates. Collection site uncertainty merits further attention as a potential source of noise in herbarium data, especially for research on how plant traits respond to spatial and interannual climatic variation. [ABSTRACT FROM AUTHOR]

Recent improvements in the spatial, temporal, and spectral resolution of satellite images necessitate (semi-)automated classification and information extraction approaches. Therefore, we developed an integrated fuzzy object-based image analysis and deep learning (FOBIA-DL) approach for monitoring the land use/cover (LULC) and respective changes and compared it to three machine learning (ML) algorithms, namely the support vector machine (SVM), random forest (RF), and classification and regression tree (CART). We investigated LULC impacts on drought by analyzing Landsat satellite images from 1990 to 2020 for the Urmia Lake area in northern Iran. In the FOBIA-DL approach, following the initial segmentation steps, object features were identified for each LULC class. We then derived their respective attributes using fuzzy membership functions and deep convolutional neural networks (DCNNs), a deep learning method. The Fuzzy Synthetic Evaluation and Dempster-Shafer Theory (FSE-DST) also applied to validate and carryout the spatial uncertainties. Our results indicate that the FOBIA-DL, with an accuracy of 90.1% to 96.4% and a spatial certainty of 0.93 to 0.97, outperformed the other approaches, closely followed by the SVM. Our results also showed that the integration of Fuzzy-OBIA and DCNNs could improve the strength and robustness of the OBIA’s decision rules, while the FSE-DST approach notably improved the spatial accuracy of the object-based classification maps. While object-based image analysis (OBIA) is already considered a paradigm shift in GIScience, the integration of OBIA with fuzzy and deep learning creates more flexibility and robust OBIA decision rules for image analysis and classification. This research integrated popular data-driven approaches and developed a novel methodology for image classification and spatial accuracy assessment. From the environmental perspective, the results of this research support lake restoration initiatives by decision-makers and authorities in applications such as drought mitigation, land use management and precision agriculture programs.

The estimation of rainfall depth-duration-frequency (DDF) curves is necessary for the design of several water systems and protection works. These curves are typically estimated from observed locations, but due to different sources of uncertainties, the risk may be underestimated. Therefore, it becomes crucial to quantify the uncertainty ranges of such curves. For this purpose, the propagation of different uncertainty sources in the regionalisation of the DDF curves for Germany is investigated. Annual extremes are extracted at each location for different durations (from 5mins up to 7days), and local extreme value analysis is performed according to Koutsoyiannis et al. (1998). Following this analysis, five parameters are obtained for each station, from which four are interpolated using external drift kriging, while one is kept constant over the whole region. Finally, quantiles are derived for each location, duration and given return period. Through a non-parametric bootstrap and geostatistical spatial simulations, the uncertainty is estimated in terms of precision (width of 95 % confidence interval) and accuracy (expected error) for three different components of the regionalisation: i) local estimation of parameters, ii) variogram estimation and iii) spatial estimation of parameters. First two methods were tested for their suitability in generating multiple equiprobable spatial simulations: sequential Gaussian simulations (SGS) and simulated annealing (SA) simulations. Between the two, SGS proved to be more accurate and was chosen for the uncertainty estimation from spatial simulations. Next, 100 realisations were run at each component of the regionalisation procedure to investigate their impact on the final regionalisation of parameters and DDFs curves, and later combined simulations were performed to propagate the uncertainty from the main components to the final DDFs curves. It was found that spatial estimation is the major uncertainty component in the chosen regionalisation procedure, followed by the local estimation of rainfall extremes. In particular, the variogram uncertainty had very little effect in the overall estimation of DDFs curves. We conclude that the best way to estimate the total uncertainty consisted of a combination between local resampling and spatial simulations, which resulted in more precise estimation at long observation locations, and a decline in precision at un-observed locations according to the distance and density of the observations in the vicinity. Through this combination, the total uncertainty was simulated by 10,000 runs in Germany, and indicated, that depending on the location and duration level, tolerance ranges from ±10–30 % for low return periods (lower than 10 years), and from ±15–60 % for high return periods (higher than 10 years) should be expected, with the very short durations (5min) being more uncertain than long durations. [ABSTRACT FROM AUTHOR]

In a study of debris flow susceptibility on the European continent, an analysis of the impact between known location and a location accuracy offset for 99 debris flows demonstrates the impact of uncertainty in defining appropriate predisposing factors and consequent analysis for areas of susceptibility. The dominant predisposing environmental factors, as determined through Maximum Entropy modeling, are presented and analyzed with respect to the values found at debris flow event points versus a buffered distance of locational uncertainty around each point. Maximum Entropy susceptibility models are developed utilizing the original debris flow inventory of points, randomly generated points, and two models utilizing a subset of points with an uncertainty of 5 km, 1 km, and a model utilizing only points with a known location of "exact". The AUCs are 0.891, 0.893, 0.896, 0.921, and 0.93, respectively. The "exact" model, with the highest AUC, is ignored in final analyses due to the small number of points and localized distribution, and hence susceptibility results are likely non-representational of the continent. Each model is analyzed with respect to the AUC, highest contributing factors, factor classes, susceptibility impact, and comparisons of the susceptibility distributions and susceptibility value differences. Based on model comparisons, geographic extent, and the context of this study, the models utilizing points with a location uncertainty of less than or equal to 5 km best represent debris flow susceptibility for the continent of Europe. A novel representation of the uncertainty is expressed and included in a final susceptibility map, as an overlay of standard deviation and mean of susceptibility values for the two best models, providing additional insight for subsequent action. [ABSTRACT FROM AUTHOR]

Deep neural network algorithms have achieved impressive performance in object detection. Real-time evaluation of perception uncertainty from deep neural network algorithms is indispensable for safe driving in autonomous vehicles. More research is required to determine how to assess the effectiveness and uncertainty of perception findings in real-time.This paper proposes a novel real-time evaluation method combining multi-source perception fusion and deep ensemble. The effectiveness of single-frame perception results is evaluated in real-time. Then, the spatial uncertainty of the detected objects and influencing factors are analyzed. Finally, the accuracy of spatial uncertainty is validated with the ground truth in the KITTI dataset. The research results show that the evaluation of perception effectiveness can reach 92% accuracy, and a positive correlation with the ground truth is found for both the uncertainty and the error. The spatial uncertainty is related to the distance and occlusion degree of detected objects.

Eigenvalues of the dielectric-filled waveguide are of great importance to its transmission characteristic analysis and optimization design, which could be easily affected by spatially uncertain dielectric parameters. For the sake of quantifying their influence on eigenvalues of the dielectric-filled waveguide and overcoming the limitation of less samples, an interval vector finite element method (IVFEM) is proposed to acquire the lower and upper bounds of the eigenvalues with spatial uncertainty of the medium parameters Firstly, the uncertain dielectric material properties are described by the interval field model and the corresponding interval Karhunen-Loève (K-L) approximate method. Secondly, by inserting the interval uncertainties into the constitutive relationship of the standard generalized eigenvalue equations of the dielectric-filled waveguide, an interval standard generalized eigenvalue equation is then formulated. At last, the lower and upper bounds of the eigenvalues are calculated according to the first-order perturbation method, which can be used to estimate the transmission properties of the waveguide efficiently. Three kinds of the dielectric-filled waveguides are analyzed by the proposed IVFEM and verified by Monte Carlo simulation method. [ABSTRACT FROM AUTHOR]

The International Geomagnetic Reference Field (IGRF) is a multi-institute model of the Earth's magnetic field, compactly described by sets of up to 195 spherical harmonic (Gauss) coefficients to degree and order 13, which allows the continuous evaluation of the field at any location and time on or above the surface. It is developed from satellite and ground-based magnetometer data and describes the large-scale variation of the magnetic field in space and time under quiet conditions. While much effort has been made on improving the forecast of the secular variation of the field over the 5-year intervals between release and renewal, less emphasis has been placed on understanding the spatial errors from a user point of view. In this study, we estimate the large-scale time-invariant spatial uncertainty of the IGRF based on the globally averaged misfit of the model to ground-based measurements at repeat stations and observatories between 1980 and 2021. As the ground measurements are reduced to quiet-time values, the external field is minimized for the purposes of this study. We find the 68.3% confidence interval is 87 nT in the North (X) component, 73 nT in the East (Y) component and 114 nT in vertical (Z) component. Due to the Laplacian distribution of the residuals, the standard deviations are larger at 144, 136 and 293 nT, respectively. [ABSTRACT FROM AUTHOR]

The application of geostatistical analyses of site investigation data for tunneling projects has gained significant attention in the literature. While the proposed techniques are useful for modeling the spatial variability and uncertainty in subsurface conditions, the extension of these techniques to the mitigation of critical tunneling risks in soils is limited. This paper demonstrates how a geostatistical approach can be used to characterize cutter tool wear and cutterhead clogging, two critical risks associated with mechanized tunneling in soils. A case study using data from the Seattle Northgate Link tunnel project is presented and validated by actual construction data reported. The results demonstrate that the proposed approach provides significantly more accurate prediction of the necessary mitigation measures than the conventional deterministic approach, providing significant estimated cost savings with a 40%–42% reduction in estimated tool replacements and a 17% reduction in estimated conditioning volume for this project. [ABSTRACT FROM AUTHOR]

Vision is typically dominant over audition or touch in human adults. One classic example is the Colavita visual dominance effect: The presentation of a tone sometimes goes undetected when it is paired with a flash even though it is well detected when presented alone. We investigated whether the Colavita visual dominance effect is modulated by stimulus uncertainty in the temporal and spatial domains. In a simple discrimination task, participants were asked to press a predesignated key when detecting a flash, another key when detecting a tone, and both keys when detecting both a flash and a tone. Temporal uncertainty was increased by introducing temporal jitter between trials (Experiment 1), and spatial uncertainty was increased by shifting the flash to different locations (Experiment 2). The Colavita visual dominance effect was reduced when temporal uncertainty was increased, while it remained similar when spatial uncertainty was increased. We therefore demonstrate a novel consideration, where increasing temporal uncertainty balances the competition between vision and audition, suggesting that people's degree of sensory dominance is malleable. Our result therefore highlights the concept that intersensory competition is susceptible to the temporal predictability of the stimulus, which provides critical insights into the design of effective warning systems in the field of ergonomics. [ABSTRACT FROM AUTHOR]

Managers of offshore wind farms make strategic decisions based on information about site wind speeds and significant wave heights (SWH) available from numerical weather predictions (NWP) or local in-situ measurements. However, the coarse resolution with which such information are available, both in space and time, introduces a high degree of uncertainty into the decision process which in turn may result in higher costs during different stages of offshore wind farm life. The current work investigates how space-borne data describing wind speeds and SWH might be used to quantify spatial uncertainties and support decisions during the design and operation of offshore wind sites. Results have revealed that due to high spatial variance of wind speed, the estimated wind power can differ from that provided by an offshore met mast up to 11%. The methodology proposed for SWH has shown how data collected from distinct satellites can be efficiently interpolated (maximum absolute error observed around 1 m) to generate high-resolute spatial information of sea water surface, regardless of satellite trajectory distributions. The work has provided insights on how the propagation of measurement uncertainty through the wind farm area can affect both management costs and wind energy production over the plant life-cycle.

Observations and collections of organisms form the basis of our understanding of Earth's biodiversity and are an indispensable resource for global change studies. Geographic information is key, serving as the link between organisms and the environments they reside in. However, the geographic information associated with these records is often inaccurate, thus limiting their efficacy for research. While some tools for identifying erroneous coordinates have been developed, a prominent but less discussed source of inaccuracies arises from the use of gridded survey systems in many regions of the world. Here we present GridDER, a tool for identifying biodiversity records that have been designated locations based on widely used grid systems. Our tool also estimates the degree of environmental heterogeneity associated with grid systems, allowing users to make informed decisions about how to use such occurrence data in their research. We show that a significant proportion (∼13.5%; 261 million) of records on GBIF, the largest aggregator of natural history collection data, have potentially been assigned the centroid coordinates of grid cells of varying size, and demonstrate that our tool can reliably identify such records and quantify the associated uncertainties. GridDER can serve as a tool to not only screen for gridded points, but to quantify the geographic and environmental uncertainties associated with these records, which can be used to inform models and analyses that utilize these data, including those pertaining to global change. • A large amount of records on GBIF are potentially gridded data. • We developed an open-source software for detecting and evaluating gridded coordinates. • Our tool can also estimate the spatial and environmental uncertainties associated with the gridded coordinates. [ABSTRACT FROM AUTHOR]

In a study of debris flow susceptibility on the European continent, an analysis of the impact between known location and a location accuracy offset for 99 debris flows demonstrates the impact of uncertainty in defining appropriate predisposing factors and consequent analysis for areas of susceptibility. The dominant predisposing environmental factors, as determined through Maximum Entropy modeling, are presented and analyzed with respect to the values found at debris flow event points versus a buffered distance of locational uncertainty around each point. Maximum Entropy susceptibility models are developed utilizing the original debris flow inventory of points, randomly generated points, and two models utilizing a subset of points with an uncertainty of 5 km, 1 km, and a model utilizing only points with a known location of “exact”. The AUCs are 0.891, 0.893, 0.896, 0.921, and 0.93, respectively. The “exact” model, with the highest AUC, is ignored in final analyses due to the small number of points and localized distribution, and hence susceptibility results are likely non-representational of the continent. Each model is analyzed with respect to the AUC, highest contributing factors, factor classes, susceptibility impact, and comparisons of the susceptibility distributions and susceptibility value differences. Based on model comparisons, geographic extent, and the context of this study, the models utilizing points with a location uncertainty of less than or equal to 5 km best represent debris flow susceptibility for the continent of Europe. A novel representation of the uncertainty is expressed and included in a final susceptibility map, as an overlay of standard deviation and mean of susceptibility values for the two best models, providing additional insight for subsequent action.

Eutrophication, water quality, and environmental status of lakes is a global issue that depends not only on external loadings from industrial, agricultural, and municipal sources but often also on internal loadings from lake sediments. In the latter case, in addition to the quality and quantity of nutrients stored in sediments, their relative content may be an important factor. In the example of Lake Balaton, we jointly modeled the spatial distribution of the nutrients nitrogen (N) and phosphorus (P) and their ratio (i.e., N:P) in the sediments of the lake and then provided spatial predictions at different scales (i.e., point, basin, and entire lake) with the associated uncertainty. Our aim was to illustrate the merits of applying multivariate geostatistics when spatial modeling of more than one variable is targeted at various scales in water ecosystems. Variography confirmed that there is a spatial interdependence between the nutrients. The results revealed that multivariate geostatistics allows this interdependence to be taken into account and exploited to provide coherent and accurate spatial models. Additionally, stochastic realizations, reproducing the joint spatial variability, can be generated that allow providing spatially aggregated predictions with the associated uncertainty at various scales. Our study highlighted that it is worthy of applying multivariate geostatistics in case spatial modeling of two or more variables, which jointly vary in space, is targeted in water ecosystems.

Regional mapping of landslide susceptibility aims to identify zones of potential instability across geological settings. Given their predictive capabilities, physically based, deterministic models are useful tools for landslide triggering studies at regional scale. However, they rely on detailed input parameters that are rarely available for large areas. To address these limitations, this work proposes a computational framework to incorporate the spatial uncertainty of input data into physically based, landslide hazard zonation models through the use of regional scale random fields (RSRF). For this purpose, input parameters are treated as spatially correlated random variables with assigned statistical attributes, while a vectorization strategy is used to reduce the computational cost of large-scale stochastic analyses. Deterministic simulations based on a hydro-mechanical model are then performed for multiple Monte Carlo realizations to compute maps of failure probability (pf). The methodology was applied to a well-documented series of rainfall-induced shallow landslides in a volcanic site for which field measurements were available to constrain the statistical variability of the hydraulic conductivity and treat this parameter as an RSRF. To analyze the results, four classes of landslide susceptibility characterized by different pf thresholds were used. Such classes were mapped over the study zone and throughout the storm event, allowing a direct comparison with the spatio-temporal evidence of landslide triggering. The results indicate that (i) uncertainty analyses neglecting the role of spatial correlation may lead to non-conservative estimates of landslide susceptibility and (ii) there is an interval of spatial correlation distance that optimizes the performance of the model, thus providing an indirect estimate of the heterogeneity of the site. Such results highlight the benefits of accounting for the uncertainty of the soil properties in regional-scale models and offer a new predictive stochastic framework to assess the implications of future rainfall scenarios over large areas. [ABSTRACT FROM AUTHOR]

In this paper, an electromagnetic stochastic finite element method is presented to calculate the statistical moments of electromagnetic problems with spatially uncertain dielectric parameters. First, the random field model of the dielectric material is represented by the Karhunen–Loève expansion and inserted into the scalar Helmholtz wave equations. The stochastic equilibrium equations are thus constructed according to the node-based finite element method. Second, the first-order and second-order perturbation stochastic finite element methods are developed for calculating the statistical moments of electromagnetic responses. Finally, the feasibility and validity of the proposed method are verified by three numerical examples. [ABSTRACT FROM AUTHOR]

Summary: This paper discusses the concepts of auto‐ and cross‐interdependence in interval field finite element analysis. In classic interval analysis, independent intervals are used to construct hyper‐rectangular input spaces that correspond to the bounded uncertainty that is present on some model parameters. This is a direct result from the inability of modeling interdependence. Such assumption of complete independence might prove in some cases to be highly over‐conservative. A first example is the modeling of spatial uncertainty, where the interdependence is governed by allowable spatial gradients of field realizations. Secondly, interdependence can also occur in case uncertainty in several structural quantities has the same root cause (eg, the manufacturing process). Recent work by the authors introduced concepts for modeling dependence between intervals in a spatial and multivariate context. However, it is unclear how an analyst has to deal with multiple quantities that have a spatial uncertainty component and are furthermore interdependent. This paper presents an approach to link multiple interval fields using recently introduced convex hull pair constructions and inverse distance weighting interpolation. Two case studies to illustrate the new methodology are included, proving the flexibility of the methodology in the modeling of auto‐ and cross‐interdependence between multiple interval scalars and/or interval fields. [ABSTRACT FROM AUTHOR]

Purpose: In the absence of a 6D couch and/or assuming considerable intrafractional patient motion, rotational errors could affect target coverage and OAR‐sparing especially in multiple metastases VMAT‐SRS cranial cases, which often involve the concurrent irradiation of off‐axis targets. This work aims to study the dosimetric impact of rotational errors in such applications, under a comparative perspective between the single‐ and two‐isocenter treatment techniques. Methods: Ten patients (36 metastases) were included in this study. Challenging cases were only considered, with several targets lying in close proximity to OARs. Two multiarc VMAT plans per patient were prepared, involving one and two isocenters, serving as the reference plans. Different degrees of angular offsets at various orientations were introduced, simulating rotational errors. Resulting dose distributions were evaluated and compared using commonly employed dose‐volume and plan quality indices. Results: For single‐isocenter plans and 1⁰ rotations, plan quality indices, such as coverage, conformity index and D95%, deteriorated significantly (>5%) for distant targets from the isocenter (at> 4–6 cm). Contrarily, for two‐isocenter plans, target distances to nearest isocenter were always shorter (≤4 cm), and, consequently, 1⁰ errors were well‐tolerated. In the most extreme case considered (2⁰ around all axes) conformity index deteriorated by on‐average 7.2%/cm of distance to isocenter, if one isocenter is used, and 2.6%/cm, for plans involving two isocenters. The effect is, however, strongly associated with target volume. Regarding OARs, for single‐isocenter plans, significant increase (up to 63%) in Dmax and D0.02cc values was observed for any angle of rotation. Plans that could be considered clinically unacceptable were obtained even for the smallest angle considered, although rarer for the two‐isocenter planning approach. Conclusion: Limiting the lesion‐to‐isocenter distance to ≤4 cm by introducing additional isocenter(s) appears to partly mitigate severe target underdosage, especially for smaller target sizes. If OAR‐sparing is also a concern, more stringent rotational error tolerances apply. [ABSTRACT FROM AUTHOR]

During the manufacturing process of dielectric materials used in electromagnetic engineering, the electromagnetic parameters are often spatially uncertain due to the processing technology, environmental temperature, personal operations, etc. Traditionally, the random field model can be used to measure the spatial uncertainties, but its construction requires a large number of samples. On the contrary, the interval field model only needs the upper and lower bounds of the spatially uncertain parameters, which requires much less samples and furthermore is easy to understand and use for engineers. Therefore, in this paper, the interval field model is introduced to describe the spatial uncertainties of dielectric materials, and then an interval finite element method (IFEM) is proposed to calculate the upper and lower bounds of electromagnetic responses. Firstly, the interval field of the dielectric material is represented by the interval K-L expansion and inserted into the scalar Helmholtz wave equations, and thus the interval equilibrium equations are constructed according to the node-based finite element method. Secondly, a perturbation interval finite element method is developed for calculating the upper and lower bounds of electromagnetic responses such as the electric strength and magnetic strength. Finally, the effectiveness of the proposed method is verified by three numerical examples. [ABSTRACT FROM AUTHOR]