Low Uncertainty Research Articles

Computation of prediction intervals for forest aboveground biomass predictions using generalized linear models in a large-extent boreal forest region

Abstract Remotely sensed data have an important application for estimation of forest variables, e.g. height, volume, and aboveground biomass (AGB). The increased use of remotely sensed data implemented along with model-based inference has shown improved efficiency in prediction and mapping of such forest variables. In this study, plot-level airborne laser scanning data and Swedish National Forest Inventory field reference data were used to predict AGB using generalized linear models (GLMs) assuming Gamma and Tweedie distributions for the field observed AGB. The GLMs were selected considering the convenience of not correcting transformation bias as it is required in other regression models with transformed response variable. To overcome the challenge in providing reliable uncertainty estimates for the estimated forest variable map products at individual pixel-scale, we focused on computing 95% prediction intervals (PIs) for Gamma and Tweedie GLMs with a square root link function. The relative uncertainties were computed as the ratio between the half-width of the PIs and the predicted AGBs. The AGB-airborne laser scanning models were developed with root mean square error values of 22.6 Mgha−1 (26%) and 21.7 Mgha−1 (25%), respectively, for the Gamma and Tweedie GLMs. Two methods were applied to compute PIs for the Gamma GLM, one using the R package ‘ciTools’ and another derived through asymptotic theory. It was found that the 95% PIs computed using ‘ciTools’ had the most accurate coverage probability in comparison to the other method. An extended version of these PIs was also utilized for the Tweedie GLMs. The range of PIs associated with the prediction of AGB were narrower for lower predicted AGB values compared with the length of higher predicted AGB values. Comparing the two fitted models, the Gamma GLM showed lower relative uncertainties for the lower range of predicted AGBs, whereas the Tweedie GLM showed lower relative uncertainties for the higher range of predicted AGBs. Overall, the Tweedie GLM provided a better model fit for AGB predictions.

AN ASSESSMENT OF THE FELDSTEIN–HORIOKA PUZZLE FOR INDIA: A MARKOV REGIME SWITCHING APPROACH

The study aims to examine the Feldstein–Horioka puzzle in the Indian context during 2002Q2 to 2019Q4 to unearth nonlinear patterns in the data. Findings from an augmented Markov regime-switching model reveal two distinct, yet random regimes for correlations between savings and investment-GDP ratios, with higher correlation between the variables persisting for a longer duration. In the low correlation regime, trade-related factors, policy uncertainty and global shocks have a significant impact on the correlation. Fiscal shocks and trade costs explain high correlations in regime 2, while global shocks act to decrease the correlation in this regime. The high correlation regime dominates the study period and indicates that the Feldstein–Horioka puzzle of limited capital mobility persists in India. Economic agents appear to respond rapidly to changing domestic and global policy conditions, besides being affected by incomplete integration of goods markets, explaining the pattern of correlations.

Quantification of NOx sources contribution to ambient nitrate aerosol, uncertainty analysis and sensitivity analysis in a megacity

Dual isotopes of nitrogen and oxygen of NO3− are crucial tools for quantifying the formation pathways and precursor NOx sources contributing to atmospheric nitrate. However, further research is needed to reduce the uncertainty associated with NOx proportional contributions. The acquisition of nitrogen isotopic composition from NOx emission sources lacks regulation, and its impact on the accuracy of contribution results remains unexplored. This study identifies key influencing factors of source isotopic composition through statistical methods, based on a detailed summary of δ15N-NOx values from various sources. NOx emission sources are classified considering these factors, and representative means, standard deviations, and 95 % confidence intervals are determined using the bootstrap method. During the sampling period in Tianjin in 2022, the proportional nitrate formation pathways varied between sites. For suburban and coastal sites, the ranking was fP1(NO2 + OH radical) >fP2(N2O5 + H2O) >fP3(NO3 + DMS/HC), while the rural site exhibited similar fractional contributions from all three formation pathways. Fossil fuel NOx sources consistently contributed more than non-fossil NOx sources in each season among three sites. The uncertainties in proportional contributions varied among different sources, with coal combustion and biogenic soil emission showing lower uncertainties, suggesting more stable proportional contributions than other sources. The sensitivity analysis clearly identifies that the isotopic composition of 15N-enriched and 15N-reduced sources significantly influences source contribution results, emphasizing the importance of accurately characterizing the localized and time-efficient nitrogen isotopic composition of NOx emission sources. In conclusion, this research sheds light on the importance of addressing uncertainties in NOx proportional contributions and emphasizes the need for further exploration of nitrogen isotopic composition from NOx emission sources for accurate atmospheric nitrate studies.

Fabric and Fluid Inclusions Characterization of a Stalagmite from Eastern Spain: A Precondition for Noble Gas Analysis by Step-Crushing Methodology

Fluid inclusions in stalagmites are becoming increasingly important for paleoclimate research. Within this framework, noble gas thermometry, based on noble gases dissolved in water from fluid inclusions, provides quantitative estimations of cave air paleotemperature. Two major issues of Noble Gas Temperature (NGT) determination on speleothems are (1) the potential lack of enough water for the analysis and (2) the presence of trapped gas not dissolved in water that can be released during the analysis from biphasic or all-gas fluid inclusions, as its contribution to the bulk noble gas signal can hinder NGT results. Although the step-crushing method helps to reduce the second issue, it also decreases the amount of water available for the calculations. In order to obtain reliable NGT results with low uncertainties, a major challenge is still to reach a balance between sufficient water for analysis and a small amount of “atmospheric” gas. The difficulty is that the extraction process cannot be standardized since it strongly depends on the type of sample. The objective of this work is to investigate how the characteristics of the speleothem can determine the adequacy of the extraction process. For this purpose, we consider a stalagmite from a Mediterranean cave that consists of columnar elongated calcite and contains a significant quantity of fluid inclusions, which suggests good potential for NGT analysis. Results, however, were poorly satisfactory. Trying to understand the source of the problems, an integrated study of petrography and petrophysical features was performed. The samples were found to be different depending on the stage of coalescence of crystals and thus separated into “open” and “closed” fabrics. Classic petrographic analysis and non-destructive (nuclear magnetic resonance) techniques were used to characterize the type and amount of fluid inclusions present in both types of fabrics. The study indicates that the closed fabric (total coalescence of calcite crystals) has most water trapped in water-filled, small intracrystalline fluid inclusions that usually contain very little gas. This fabric is very suitable for NGT determination, but since the amount of water is quite small, the sample should be crushed in only one step with a large number of beats to break all the inclusions. In contrast, samples with open fabric (partial coalescence of calcite crystals) contain a higher amount of water and, also, gas-filled large intercrystalline fluid inclusions. For this fabric, step-crushing of the sample is necessary. However, the low amount of water left for the second and third crushings could lead to flawed NGT results. Thus, we suggest modifying the method to get rid of part of the gas in the first crushing while leaving enough water for the following steps. This work shows the importance of characterizing speleothems and fluid inclusions, including their petrography and petrophysical characteristics, before starting NGT analysis, allowing the selection of the most favorable samples and the customization of the step-crushing procedure.

Научное обоснование среднегодовой предельно допустимой концентрации ванадия пентоксида в атмосферном воздухе по критериям допустимого риска

Relevance of this study is determined by the sanitary-epidemiological legislation of the Russian Federation with stipulated requirements to create hygiene standards for environmental factors ensuring their safety for people. These hygienic standards should guarantee absence of impermissible lifetime health risks. Divanadium pentoxide is a chemical that should be mandatorily regulated in ambient air under long-term exposure due to its wide prevalence and high toxicity. An average annual MPL for divanadium pentoxide was established by using systemic analysis of research literature and regulatory documents. According to selection results, three key epidemiological studies were taken for further analysis. They provided evidence of adverse effects produced by divanadium pentoxide on human health (the respiratory organs in particular) under chronic inhalation exposure. When analyzing a study design, we paid special attention to description of observation groups, values of exposure and nature of its effects, adverse health outcomes caused by exposure to divanadium pentoxide as well as to a type and a value of a point of departure. We calculated a value of the total (complex) uncertainty factor in order to establish an average annual maximum permissible level of the analyzed chemical. As a result, we suggest a scientifically substantiated (among other things, as per permissible health risk levels) average annual maximum permissible level for divanadium pentoxide, which equals 0.0001 mg/m3. This level is safe for human health under lifetime exposure. It is noteworthy that this level corresponds to ‘low uncertainty’, which indicates its high safety for human health.

Scientific substantiation of average annual maximum permissible level of vanadium pentoxide in ambient air as per permissible health risk

Relevance of this study is determined by the sanitary-epidemiological legislation of the Russian Federation with stipulated requirements to create hygiene standards for environmental factors ensuring their safety for people. These hygienic standards should guarantee absence of impermissible lifetime health risks. Divanadium pentoxide is a chemical that should be mandatorily regulated in ambient air under long-term exposure due to its wide prevalence and high toxicity. An average annual MPL for divanadium pentoxide was established by using systemic analysis of research literature and regulatory documents. According to selection results, three key epidemiological studies were taken for further analysis. They provided evidence of adverse effects produced by divanadium pentoxide on human health (the respiratory organs in particular) under chronic inhalation exposure. When analyzing a study design, we paid special attention to description of observation groups, values of exposure and nature of its effects, adverse health outcomes caused by exposure to divanadium pentoxide as well as to a type and a value of a point of departure. We calculated a value of the total (complex) uncertainty factor in order to establish an average annual maximum permissible level of the analyzed chemical. As a result, we suggest a scientifically substantiated (among other things, as per permissible health risk levels) average annual maximum permissible level for divanadium pentoxide, which equals 0.0001 mg/m3. This level is safe for human health under lifetime exposure. It is noteworthy that this level corresponds to ‘low uncertainty’, which indicates its high safety for human health.

Experimental determination of the hafnium L-subshell fundamental parameters using the holistic approach

By employing the recently demonstrated new holistic approach, the atomic fundamental parameters (FPs) of the three Hf-L subshells were experimentally determined using the radiometrically calibrated instrumentation of the Physikalisch-Technische Bundesanstalt. The Coster–Kronig factors, the L-subshell fluorescence yields, the L-subshell Auger yields, the subshell-photoionization cross sections, and the subshell fluorescence production cross sections were determined by means of photon energy dependent x-ray fluorescence and transmission measurements. The recently demonstrated new holistic evaluation approach allows to determine the FPs with significantly lower uncertainties as compared to the former data evaluation scheme, where only a limited regime of incident photon energies is being probed and the data evaluation scheme is performed in a sequential manner.

Does Uncertainty Matter for the Fiscal Consolidation and Investment Nexus?

Abstract The aim of this research paper is to investigate whether there are non-linearities in the relationship between fiscal consolidation and investment. To achieve this, we take into account the overall state of the economy, as represented by the level of uncertainty. We analyzed a sample of 27 OECD countries from 1996 to 2019 and identified two different regimes of low and high uncertainty. We found that the relationship between fiscal policy and investment is significantly different in these two regimes. In the low uncertainty regime, fiscal tightening has no significant effect on investment. In contrast, in the high uncertainty regime, fiscal tightening has a negative impact on investment, which is three times larger than in the low uncertainty regime. Our results are robust, and have been confirmed through a range of sensitivity tests.

딥러닝 기반 시계열 분석 모델의 불확실성 정량화 비교 연구

With the advancement of artificial intelligence, machine learning, and deep learning, their applications in various industries, particularly finance, have increased. However, interpreting predictions from deep learning models poses challenges, especially in finance where result interpretation is important. This study aims to determine the uncertainty of stable deep learning models, despite changes in the model like dropout, to establish standards for reliable models and identify those detecting anomal data through model uncertainty. In the experiment, the traditional statistical model ARIMA and deep learning models mainly used for time series analysis, CNN, LSTM, MLP, and CNN-LSTM. Uncertainty was measured from a Bayesian perspective using MC Dropout. The experimental results confirmed that deep learning models performed better than statistical models for various patterns of time series data. It was observed that, even if the performance was not the best, LSTM based models exhibited low uncertainty, indicating stability. Consequently, this study highlights the importance of considering uncertainty along with accuracy in model selection. Moreover, it was confirmed that models with higher uncertainty are suitable for anomaly detection, making CNN based models particularly fitting for this purpose.

A comprehensive assessment and comparison of the impacts of storage parameters on solute transport in streams using a novel framework

Researchers must determine the storage parameter values to understand the storage processes and compare them across streams. These parameters are highly variable in time and space, making them challenging to measure. Individual studies of a single stream cannot characterize the interrelations among the processes involved in solute transport, hydraulic variables, and physical stream characteristics. Despite decades of research, a comprehensive framework is lacking. This study uses a simple and effective Monte Carlo approach to propose a general framework within which most solute transport models can undergo sensitivity analysis. This framework provides a comprehensive depiction of model performance under various scenarios, such as flow properties, stream geomorphology, and solute status. The relative and absolute sensitivities of three solute transport models, namely, the modified advection–dispersion equation (MADE), transient storage model (TSM), and variable residence time (VART), were evaluated and compared within the proposed framework. The main findings can be summarized as follows. In most conditions, storage models are more sensitive to the ratio of the storage zone cross-section to the stream cross-section than other parameters. Increasing the exchange rate between the stream and storage zones reduces the sensitivity to its indicative parameter and, consequently, its impact on the concentration time series. MADE's capability to identify modification parameters is higher for streams where dispersion dominates over advection, and their estimates have lower uncertainty. On the other hand, as advection becomes more dominant over dispersion in TSM and VART, estimated storage parameters have lower uncertainty.“

Measurement of Gas Temperature Around a Burning Droplet: Thin Filament and Soot Pyrometry

ABSTRACT A common setup for the study of liquid fuel combustion involves hanging a droplet from one or several tiny fibers. This paper explores the capabilities and limitations of thin filament pyrometry (TFP) when applied to the fiber(s) holding the fuel droplet. A 2D, two-color pyrometer is used to obtain the temperature along the fiber with low uncertainty and good spatial resolution. The derivation of the gas temperature profile from that of the fiber, however, entails a number of assumptions regarding the flow pattern around the droplet in the radiation/convection balance and also estimating the heat conduction along the fiber. Both aspects and the associated uncertainties are discussed in detail in a test with a 750 μm eicosane droplet hanging from two 15 µm SiC fibers and burnt in air; the “corrections” needed to estimate the maximum flame temperature sum up to ~ 300 K for a final value around 2000 K. The pyrometer is then applied to the soot cloud above the eicosane droplet, an alternative approach whose uncertainty arises mainly from that regarding the emissivity of soot. A reasonable agreement with the corrected TFP results is found. Finally, TFP is applied in a worst-case scenario, that is the case of a glycerol droplet, with no noticeable soot and a short distance between the flame and the droplet surface.

Extending the code in the open-source saemix package to fit joint models of longitudinal and time-to-event data

Background and ObjectiveJoint modeling of longitudinal and time-to-event data has gained attention over recent years with extensive developments including nonlinear models for longitudinal outcomes and flexible time-to-event models for survival outcomes, possibly involving competing risks. However, in popular software such as R, the function used to describe the biomarker dynamic is mainly linear in the parameters, and the survival submodel relies on pre-implemented functions (exponential, Weibull, ...). The objective of this work is to extend the code from the saemix package (version 3.1 on CRAN) to fit parametric joint models where longitudinal submodels are not necessary linear in their parameters, with full user control over the model function. MethodsWe used the saemix package, designed to fit nonlinear mixed-effects models (NLMEM) through the Stochastic Approximation Expectation Maximization (SAEM) algorithm, and extended the main functions to joint model estimation. To compute standard errors (SE) of parameter estimates, we implemented a recently developed stochastic algorithm. A simulation study was proposed to assess (i) the performances of parameter estimation, (ii) the SE computation and (iii) the type I error when testing independence between the two submodels. Four joint models were considered in the simulation study, combining a linear or nonlinear mixed-effects model for the longitudinal submodel, with a single terminal event or a competing risk model. ResultsFor all simulation scenarios, parameters were precisely and accurately estimated with low bias and uncertainty. For complex joint models (with NLMEM), increasing the number of chains of the algorithm was necessary to reduce bias, but earlier censoring in the competing risk scenario still challenged the estimation. The empirical SE of parameters obtained over all simulations were very close to those computed with the stochastic algorithm. For more complex joint models (involving NLMEM), some estimates of random effects variances had higher uncertainty and their SE were moderately under-estimated. Finally, type I error was controlled for each joint model. Conclusionssaemix is a flexible open-source package and we adapted it to fit complex parametric joint models that may not be estimated using standard tools. Code and examples to help users get started are freely available on Github.

Match-Level Uncertainty in Professional Tennis Revisited—A Novel Approach Applied for the Time Between 2010 and 2019

Despite its popularity, outcome uncertainty in professional tennis did not receive much attention from academics in the recent past. This is astonishing because it should be of utmost importance in professional tennis in the following years with the approaching end of the era of the Big 3. This study investigates match-level uncertainty in professional women's and men's tennis between 2010 and 2019 using a logit model with a dataset consisting of 51,054 matches. In the different analyses, the variations in the ranking and quality differences between players are taken into account in addition to differences across surfaces, tournaments, and genders. In the investigated period, women's tennis has relatively higher outcome uncertainty when compared to men's. The results also illustrate that talent seems to be distributed more evenly in women's tennis, whereas men's tennis has high heterogeneity in terms of talent. Moreover, very specific subgroups of matches having the highest and lowest uncertainty in men's and women's tennis are identified. Managerial implications are also presented to show how organizers, policy makers and, eventually, fans can benefit from the insights presented in this study.

Unmasking the pivotal role of ad–target ethnic congruence in driving consumers' response to fear appeals

AbstractFear appeals are persuasive messages that scare consumers into compliance with the appeals' recommendation by highlighting a personally relevant threat. Several factors, including personality traits and message features, can influence the effectiveness of fear appeals. This article proposes an additional element that contributes to the persuasiveness of fear appeals: ethnic congruence between the fear appeal and the target audience. Specifically, this research examines the interplay of high fear arousal appeals and ad–target congruence on message persuasiveness. We operationalize congruence by ethnicity match (vs. mismatch). A series of three experiments and a follow‐up study reveal that while high fear arousal appeals are more persuasive than low‐arousal appeals in general, their effectiveness is accentuated when the target audience feels congruent with the elements of the appeal. We argue that when encountering fear appeals, consumers' perceived ethnic congruence with the elements of the advertisement (e.g., ad character–target ethnic similarity) affects whether they identify with the ad and thus influences their perceived vulnerability to the publicized threat. This, in turn, influences the level of the induced emotion (i.e., fear), which subsequently drives persuasiveness. We document this effect using both self‐reported and behavioral measures. We also rule out an alternative account based on appraisal‐tendency theory, which predicts that the uncertainty dimension characterizing the emotions—for example, fear (anger) is associated with high (low) uncertainty—explains the results. The findings highlight the importance of representing diverse characters in marketing and public policy campaigns relying on fear appeals.

Exploring the differences of moisture traceability methods based on MixSIAR model under different nitrogen applications of wheat in the Arid Region of Northwest China

The Bayesian stable isotope mixing (MixSIAR) model was widely used in water source tracing. However, double isotope and single isotope calculated by the MixSIAR model led to different results in terms of previous studies. The effect of different nitrogen treatments on the water traceability results of wheat crops is still unclear. This study investigated the wheat root water uptake patterns at different nitrogen application gradients, as well as at different isotopes in the MixSIAR model. The results showed the main soil water uptake layer was relative constant at the treatment of 15% nitrogen reduction, and the water source was mainly from 0–10 cm soil layer from jointing to harvest. The main soil water uptake layer significantly varied at the treatment of 30% nitrogen reduction, which yielded the highest. 45% reduction of nitrogen treatment showed the deepest soil water uptake absorption on average with the whole wheat growth period. The consistency of the results between the two single isotope methods was better in the early stage of wheat and worse in the later stage. The inconsistency of root water uptake distribution results between the two single isotope methods might be influenced by the water transport mechanism of wheat stem and sampling errors. The double isotope method had the lowest uncertainty, but it might amplify the error of the sampling process. The uncertainty of the single hydrogen isotope method was less than single oxygen isotope method. This study provided a new evaluation of hydrogen-oxygen stable isotope traceability methods for wheat under different nitrogen treatments, and gave more ideas and insights for subsequent crop water traceability by the MixSIAR method.

Improving retrieval of leaf chlorophyll content from Sentinel-2 and Landsat-7/8 imagery by correcting for canopy structural effects

Accurately estimating leaf chlorophyll content (Chl) at large spatial scales is crucial for monitoring agricultural production and plant photosynthesis. Sentinel-2 and Landsat-7/8 offer the potential to assess Chl with high spatial resolutions using various physically-based, empirical, and hybrid methods, but they still present challenges due to the confounding effects of canopy structure and soil background. The near-infrared reflectance of vegetation (NIRV) has been proposed to characterize canopy structural and soil background effects in sun-induced chlorophyll fluorescence. Therefore, in this study, we developed a novel strategy that incorporates NIRV to minimize canopy structural and soil background effects on Chl retrieval, using bidirectional reflectance factor (BRF) from Sentinel-2 and Landsat-7/8 imagery. We compared Chl estimation using empirical random forest (RF), the physically-based PROSAIL-5B inversion, and a hybrid method using RF to train simulated datasets from PROSAIL-5B. We validated Chl retrieval performance with measured datasets from the National Ecological Observatory Network, covering seven vegetation types. We also compared spatiotemporal Chl variations from Sentinel-2, Landsat-7/8, and MODIS. The results showed that NIRV effectively characterized the canopy structural effect on Chl estimation, associated with leaf area index, average leaf angle, and canopy fraction of senescent leaves. Compared to BRF, correcting BRF by NIRV improved Chl retrieval with RMSE values reduced by 12.2%–27.0% for Sentinel-2, 7.4%–16.4% for Landsat-7, and 6.9%–13.7% for Landsat-8, respectively. This also resulted in a significant increase in R2 values for Sentinel-2 (0.13–0.46), Landsat-7 (0.08–0.19), and Landsat-8 (0.12–0.2), respectively. Sentinel-2 outperformed Landsat-7/8 in Chl estimation, due to the contribution of red edge bands and a higher spatial resolution. Furthermore, the hybrid method performed best for Chl estimation from Sentinel-2 and Landsat-7/8 with relative RMSE values of 17.33%, 20.22%, and 19.23%, respectively. Our hybrid method demonstrated lower model uncertainty with smaller variations of RMSE values across varying biochemical and structural traits and solar and viewing angles, and exhibited more consistent spatiotemporal variations of Chl from Sentinel-2 with phenology compared to Landsat-7/8 and the published MODIS Chl data. Our hybrid method demonstrates significant potential in mitigating canopy structural effects, enhancing Chl retrieval, and facilitating the development of Chl data from various satellite imagery sources.

Inter-comparison study of wind measurement between the three-lidar-based virtual tower and four lidars using VAD techniques

ABSTRACT The accurate three-dimensional wind field obtained from a Doppler lidar not only helps to comprehend the refined structure of complex airflow but also provides important and valuable solutions for many fields. However, the underlying homogeneity assumption of the typical wind retrieval methods, such as Doppler Beam Swinging (DBS) and Velocity Azimuth Display (VAD) based on a single-lidar, will introduce the measurement uncertainty in complex terrain. In this paper, a new design of a wind measurement campaign involving seven lidars was carried out, which contained the three-lidar-based Virtual Tower (VT) using a time-space synchronization technique and four single-lidars with different elevation angles. This study investigates the performance of VT and VAD measurements under various conditions and evaluates the sensitivity of wind measurement uncertainty of VAD to the horizontal spatial- and probe volume-average effects associated with elevation angles of the laser beam. The inter-comparison results between VT and four VADs show consistent trends with small relative errors under neutral atmospheric conditions with weak wind velocity. Under convective or high Turbulence Intensity (TI) conditions, the relative errors between VT and VAD become larger and more fluctuant. Moreover, it is found that the measurement uncertainty of VAD increases at a given elevation angle with the increasing measurement heights, which is caused by the horizontal homogeneity associated with the conical scanning area. Additionally, the simulated and measured results of four VADs indicate that a larger elevation angle corresponds to a lower measurement uncertainty for a given height.

Rotational Spectroscopy of 1-Cyano-2-methylenecyclopropane (C5H5N)─A Newly Synthesized Pyridine Isomer.

The gas-phase rotational spectrum of 1-cyano-2-methylenecyclopropane (C1, C5H5N), an isomer of pyridine, is presented for the first time, covering the range from 235 to 500 GHz. Over 3600 a-, b-, and c-type transitions for the ground vibrational state have been assigned, measured, and least-squares fit to partial-octic A- and S-reduced distorted-rotor Hamiltonians with low statistical uncertainty (σfit = 42 kHz). Transitions for the two lowest-energy fundamental states (ν27 and ν26) and the lowest-energy overtone (2ν27) have been similarly measured, assigned, and least-squares fit to single-state Hamiltonians. Computed vibration-rotation interaction constants (B0-Bv) using the B3LYP and MP2 levels of theory are compared with the corresponding experimental values. Based upon our preliminary analysis, the next few vibrationally excited states form one or more complex polyads of interacting states via Coriolis and anharmonic coupling. The spectroscopic constants and transition frequencies presented here form the foundation for both future laboratory spectroscopy and astronomical searches for 1-cyano-2-methylenecyclopropane.

Uncertainty analysis of discharge coefficient predicted for rectangular side weir using machine learning methods

Abstract The present study used three machine learning models, including Least Square Support Vector Regression (LSSVR) and two non-parametric models, namely, Quantile Regression Forest (QRF) and Gaussian Process Regression (GPR), to quantify uncertainty and precisely predict the side weir discharge coefficient (Cd) in rectangular channels. So, 15 input structures were examined to develop the models. The results revealed that the machine learning models used in the study offered better accuracy compared to the classical equations. While the LSSVR and QRF models provided a good prediction performance, the GPR slightly outperformed them. The best input structure that was developed included all four dimensionless parameters. Sensitivity analysis was conducted to identify the effective parameters. To evaluate the uncertainty in the predictions, the LSSVR, QRF, and GPR were used to generate prediction intervals (PI), which quantify the uncertainty coupled with point prediction. Among the implemented models, the GPR and LSSVR models provided more reliable results based on PI width and the percentage of observed data covered by PI. According to point prediction and uncertainty analysis, it was concluded that the GPR model had a lower uncertainty and could be successfully used to predict Cd.

Incorporating interpretation uncertainties from deterministic 3D hydrostratigraphic models in groundwater models

Abstract. Many 3D hydrostratigraphic models of the subsurface are interpreted as deterministic models, where an experienced modeler combines relevant geophysical and geological information with background geological knowledge. Depending on the quality of the information from the input data, the interpretation phase will typically be accompanied by an estimated qualitative interpretation uncertainty. Given the qualitative nature of uncertainty, it is difficult to propagate the uncertainty to groundwater models. In this study, a stochastic-simulation-based methodology to characterize interpretation uncertainty within a manual-interpretation-based layer model is applied in a groundwater modeling setting. Three scenarios with different levels of interpretation uncertainty are generated, and three locations representing different geological structures are analyzed in the models. The impact of interpretation uncertainty on predictions of capture zone area and median travel time is compared to the impact of parameter uncertainty in the groundwater model. The main result is that in areas with thick and large aquifers and low geological uncertainty, the impact of interpretation uncertainty is negligible compared to the hydrogeological parameterization, while it may introduce a significant contribution in areas with thinner and smaller aquifers with high geologic uncertainty. The influence of the interpretation uncertainties is thus dependent on the geological setting as well as the confidence of the interpreter. In areas with thick aquifers, this study confirms existing evidence that if the conceptual model is well defined, interpretation uncertainties within the conceptual model have limited impact on groundwater model predictions.