The role of station density for predicting daily runoff by top-kriging interpolation in Austria

Abstract Direct interpolation of daily runoff observations to ungauged sites is an alternative to hydrological model regionalisation. Such estimation is particularly important in small headwater basins characterized by sparse hydrological and climate observations, but often large spatial variability. The main objective of this study is to evaluate predictive accuracy of top-kriging interpolation driven by different number of stations (i.e. station densities) in an input dataset. The idea is to interpolate daily runoff for different station densities in Austria and to evaluate the minimum number of stations needed for accurate runoff predictions. Top-kriging efficiency is tested for ten different random samples in ten different stations densities. The predictive accuracy is evaluated by ordinary cross-validation and full-sample crossvalidations. The methodology is tested by using 555 gauges with daily observations in the period 1987-1997. The results of the cross-validation indicate that, in Austria, top-kriging interpolation is superior to hydrological model regionalisation if station density exceeds approximately 2 stations per 1000 km2 (175 stations in Austria). The average median of Nash-Sutcliffe cross-validation efficiency is larger than 0.7 for densities above 2.4 stations/1000 km2. For such densities, the variability of runoff efficiency is very small over ten random samples. Lower runoff efficiency is found for low station densities (less than 1 station/1000 km2) and in some smaller headwater basins.

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Assessing elderly’s functional balance and mobility via analyzing data from waist-mounted tri-axial wearable accelerometers in timed up and go tests

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01463-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Lisha Yu ◽

Yang Zhao ◽

Hailiang Wang ◽

Tien-Lung Sun ◽

Terrence E. Murphy ◽

...

Keyword(s):

Predictive Models ◽

Cross Validation ◽

Motor Coordination ◽

Predictive Accuracy ◽

Short Form ◽

Community Dwelling ◽

Regularized Regression ◽

Balance Assessment ◽

Timed Up And Go ◽

Functional Balance

Abstract Background Poor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly’s functional balance based on Short Form Berg Balance Scale (SFBBS) score. Methods Data were collected from a waist-mounted tri-axial accelerometer while participants performed a timed up and go test. Clinically relevant variables were extracted from the segmented accelerometer signals for fitting SFBBS predictive models. Regularized regression together with random-shuffle-split cross-validation was used to facilitate the development of the predictive models for automatic balance estimation. Results Eighty-five community-dwelling older adults (72.12 ± 6.99 year) participated in our study. Our results demonstrated that combined clinical and sensor-based variables, together with regularized regression and cross-validation, achieved moderate-high predictive accuracy of SFBBS scores (mean MAE = 2.01 and mean RMSE = 2.55). Step length, gender, gait speed and linear acceleration variables describe the motor coordination were identified as significantly contributed variables of balance estimation. The predictive model also showed moderate-high discriminations in classifying the risk levels in the performance of three balance assessment motions in terms of AUC values of 0.72, 0.79 and 0.76 respectively. Conclusions The study presented a feasible option for quantitatively accurate, objectively measured, and unobtrusively collected functional balance assessment at the point-of-care or home environment. It also provided clinicians and elderly with stable and sensitive biomarkers for long-term monitoring of functional balance.

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Mapping Arctic clam abundance using multiple datasets, models, and a spatially explicit accuracy assessment

ICES Journal of Marine Science ◽

10.1093/icesjms/fsz099 ◽

2019 ◽

Vol 76 (7) ◽

pp. 2349-2361

Author(s):

Benjamin Misiuk ◽

Trevor Bell ◽

Alec Aitken ◽

Craig J Brown ◽

Evan N Edinger

Keyword(s):

Spatial Autocorrelation ◽

Cross Validation ◽

Predictive Accuracy ◽

Accuracy Assessment ◽

Small Scale ◽

Suitable Habitat ◽

Distribution Models ◽

Management Tools ◽

Seabed Topography ◽

Leave One Out

Abstract Species distribution models are commonly used in the marine environment as management tools. The high cost of collecting marine data for modelling makes them finite, especially in remote locations. Underwater image datasets from multiple surveys were leveraged to model the presence–absence and abundance of Arctic soft-shell clam (Mya spp.) to support the management of a local small-scale fishery in Qikiqtarjuaq, Nunavut, Canada. These models were combined to predict Mya abundance, conditional on presence throughout the study area. Results suggested that water depth was the primary environmental factor limiting Mya habitat suitability, yet seabed topography and substrate characteristics influence their abundance within suitable habitat. Ten-fold cross-validation and spatial leave-one-out cross-validation (LOO CV) were used to assess the accuracy of combined predictions and to test whether this was inflated by the spatial autocorrelation of transect sample data. Results demonstrated that four different measures of predictive accuracy were substantially inflated due to spatial autocorrelation, and the spatial LOO CV results were therefore adopted as the best estimates of performance.

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Pharmacokinetics of Propofol in Adult Patients Undergoing Coronary Revascularization

Anesthesiology ◽

10.1097/00000542-199606000-00003 ◽

1996 ◽

Vol 84 (6) ◽

pp. 1288-1297 ◽

Cited By ~ 42

Author(s):

James M. Bailey ◽

Christina T. Mora ◽

Stephen L. Shafer ◽

Keyword(s):

Cardiac Surgery ◽

Cross Validation ◽

Predictive Accuracy ◽

Coronary Revascularization ◽

Artery Bypass ◽

Bypass Graft ◽

Adult Patients ◽

Pharmacokinetic Parameters ◽

Surgery Patient ◽

Prospective Comparison

Background Propofol is increasingly used for cardiac anesthesia and for perioperative sedation. Because pharmacokinetic parameters vary among distinct patient populations, rational drug dosing in the cardiac surgery patient is dependent on characterization of the drug's pharmacokinetic parameters in patients actually undergoing cardiac procedures and cardiopulmonary bypass (CPB). In this study, the pharmacokinetics of propofol was characterized in adult patients undergoing coronary revascularization. Methods Anesthesia was induced and maintained by computer-controlled infusions of propofol and alfentanil, or sufentanil, in 41 adult patients undergoing coronary artery bypass graft surgery. Blood samples for determination of plasma propofol concentrations were collected during the predefined study periods and assayed by high-pressure liquid chromatography. Three-compartment model pharmacokinetic parameters were determined by nonlinear extended least-squares regression of pooled data from patients receiving propofol throughout the perioperative period. The effect of CPB on propofol pharmacokinetics was modeled by allowing the parameters to change with the institution and completion of extracorporeal circulation and selecting the optimal model on the basis of the logarithm of the likelihood. Predicted propofol concentrations were calculated by convolving the infusion rates with unit disposition functions using the estimated parameters. The predictive accuracy of the parameters was evaluated by cross-validation and by a prospective comparison of predicted and measured levels in a subset of patients. Results Optimal pharmacokinetic parameters were: central compartment volume = 6.0 l; second compartment volume = 49.5 l; third compartment volume = 429.3 l; Cl1 (elimination clearance) = 0.68 l/min; Cl2 (distribution clearance) = 1.97 l/min1; and Cl3 (distribution clearance) = 0.70 l/min. The effects of CPB were optimally modeled by step changes in V1 and Cl1 to values of 15.9 and 1.95, respectively, with the institution of CPB. Median absolute prediction error was 18% in the cross-validation assessment and 19% in the prospective evaluation. There was no evidence for nonlinear kinetics. Previously published propofol pharmacokinetic parameter sets poorly predicted the observed concentrations in cardiac surgical patients. Conclusions The pharmacokinetics of propofol in adult patients undergoing cardiac surgery with CPB are dissimilar from those reported for other adult patient populations. The effect of CPB was best modeled by an increase in V1 and Cl1. Predictive accuracy of the derived pharmacokinetic parameters was excellent as measured by cross-validation and a prospective test.

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Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

10.5194/egusphere-egu21-8782 ◽

2021 ◽

Author(s):

Moctar Dembélé ◽

Bettina Schaefli ◽

Grégoire Mariéthoz

Keyword(s):

West Africa ◽

Large Scale ◽

Hydrological Model ◽

Meteorological Data ◽

Model Performance ◽

Hydrologic Model ◽

Hydrological Modelling ◽

Climate Change Scenarios ◽

Depth Analysis ◽

Input Dataset

The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Demb&#233;l&#233; et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.Demb&#233;l&#233;, M., Schaefli, B., van de Giesen, N., & Mari&#233;thoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020

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Cost-Effective Reporting of Travel on Local Roads

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198105191700101 ◽

2005 ◽

Vol 1917 (1) ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Kelly Blume ◽

Marais Lombard ◽

Shaun Quayle ◽

Phill Worth ◽

John Zegeer

Keyword(s):

Performance Monitoring ◽

Census Data ◽

Evaluation Criteria ◽

Cost Effective ◽

Research Process ◽

Test Case ◽

Statistical Validity ◽

Random Samples ◽

Local Roads ◽

Minimum Number

The purpose of the Highway Performance Monitoring System of the Rural and Local Roads Reporting Project was to develop a more accurate, yet cost-effective, methodology for estimating vehicle miles traveled (VMT) on urban local roads, rural local roads, and rural minor collectors throughout Florida. A review of Florida's existing methodology, methodologies of five “peer” states, and available research was completed to develop specific evaluation criteria that could then be applied to several conceptual methodologies developed from the synthesis of information. The project team selected a preferred VMT estimation methodology that used census data and random sampling. The preferred methodology makes use of available census data and an intuitive correlation between travel and population density, job density, and roadway density, while it eliminates the dependence on volume groups that is part of FHWA's methodology for higher-order roadways. The density factors are used to group similar zip code tabulation areas (ZCTAs) into subregions to allow random samples taken in one subregion to represent similar ZCTAs statewide on the basis of any or all the following: population, job, and roadway density. A minimum number of random samples is selected to retain statistical validity while minimizing costs to conduct traffic counts. The methodology is flexible enough to allow adjustments (e.g., restratification based on indicators such as coefficients of variation) that improve the quality of the results from year to year. Implementing a test case of the methodology to determine real-world applicability is the next logical step in the research process.

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Prediction of Maize Single Cross Hybrids Using the Total Effects of Associated Markers Approach Assessed by Cross-Validation and Regional Trials

The Scientific World JOURNAL ◽

10.1155/2014/924348 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Wagner Mateus Costa Melo ◽

Renzo Garcia Von Pinho ◽

Marcio Balestre

Keyword(s):

Training Trial ◽

Cross Validation ◽

Predictive Accuracy ◽

Field Performance ◽

Training Population ◽

Maize Hybrids ◽

Single Cross ◽

The Cross ◽

Predictive Validation ◽

Single Cross Hybrids

The present study aimed to predict the performance of maize hybrids and assess whether the total effects of associated markers (TEAM) method can correctly predict hybrids using cross-validation and regional trials. The training was performed in 7 locations of Southern Brazil during the 2010/11 harvest. The regional assays were conducted in 6 different South Brazilian locations during the 2011/12 harvest. In the training trial, 51 lines from different backgrounds were used to create 58 single cross hybrids. Seventy-nine microsatellite markers were used to genotype these 51 lines. In the cross-validation method the predictive accuracy ranged from 0.10 to 0.96, depending on the sample size. Furthermore, the accuracy was 0.30 when the values of hybrids that were not used in the training population (119) were predicted for the regional assays. Regarding selective loss, the TEAM method correctly predicted 50% of the hybrids selected in the regional assays. There was also loss in only 33% of cases; that is, only 33% of the materials predicted to be good in training trial were considered to be bad in regional assays. Our results show that the predictive validation of different crop conditions is possible, and the cross-validation results strikingly represented the field performance.

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Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm

BioMed Research International ◽

10.1155/2019/2304128 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12

Author(s):

Li-Yeh Chuang ◽

Guang-Yu Chen ◽

Sin-Hua Moi ◽

Fu Ou-Yang ◽

Ming-Feng Hou ◽

...

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Predictive Accuracy ◽

Cancer Prognosis ◽

University Hospital ◽

Health Concern ◽

Survival Models ◽

Survival Prediction ◽

Prediction Ability ◽

Minimum Number

Breast cancer is the most common cancer among women and is considered a major public health concern worldwide. Biogeography-based optimization (BBO) is a novel metaheuristic algorithm. This study analyzed the relationship between the clinicopathologic variables of breast cancer using Cox proportional hazard (PH) regression on the basis of the BBO algorithm. The dataset is prospectively maintained by the Division of Breast Surgery at Kaohsiung Medical University Hospital. A total of 1896 patients with breast cancer were included and tracked from 2005 to 2017. Fifteen general breast cancer clinicopathologic variables were collected. We used the BBO algorithm to select the clinicopathologic variables that could potentially contribute to predicting breast cancer prognosis. Subsequently, Cox PH regression analysis was used to demonstrate the association between overall survival and the selected clinicopathologic variables. C-statistics were used to test predictive accuracy and the concordance of various survival models. The BBO-selected clinicopathologic variables model obtained the highest C-statistic value (80%) for predicting the overall survival of patients with breast cancer. The selected clinicopathologic variables included tumor size (hazard ratio [HR] 2.372, p = 0.006), lymph node metastasis (HR 1.301, p = 0.038), lymphovascular invasion (HR 1.606, p = 0.096), perineural invasion (HR 1.546, p = 0.168), dermal invasion (HR 1.548, p = 0.028), total mastectomy (HR 1.633, p = 0.092), without hormone therapy (HR 2.178, p = 0.003), and without chemotherapy (HR 1.234, p = 0.491). This number was the minimum number of discriminators required for optimal discrimination in the breast cancer overall survival model with acceptable prediction ability. Therefore, on the basis of the clinicopathologic variables, the survival prediction model in this study could contribute to breast cancer follow-up and management.

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Nested and Repeated Cross Validation for Classification Model With High-Dimensional Data

Revista Colombiana de Estadística ◽

10.15446/rce.v43n1.80000 ◽

2020 ◽

Vol 43 (1) ◽

pp. 103-125

Author(s):

Yi Zhong ◽

Jianghua He ◽

Prabhakar Chalise

Keyword(s):

Feature Selection ◽

Cross Validation ◽

Predictive Accuracy ◽

Simulated Data ◽

Classification Model ◽

High Dimensional ◽

Clinical Settings ◽

Feature Subset ◽

Validation Method ◽

High Dimensional Datasets

With the advent of high throughput technologies, the high-dimensional datasets are increasingly available. This has not only opened up new insight into biological systems but also posed analytical challenges. One important problem is the selection of informative feature-subset and prediction of the future outcome. It is crucial that models are not overfitted and give accurate results with new data. In addition, reliable identification of informative features with high predictive power (feature selection) is of interests in clinical settings. We propose a two-step framework for feature selection and classification model construction, which utilizes a nested and repeated cross-validation method. We evaluated our approach using both simulated data and two publicly available gene expression datasets. The proposed method showed comparatively better predictive accuracy for new cases than the standard cross-validation method.

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Determination of minimum number of random SNP for accurate population classification in rice (Oryza sativa L.)

10.21203/rs.3.rs-35906/v1 ◽

2020 ◽

Author(s):

Shan Wang ◽

Junhua Ye ◽

Qun Xu ◽

Xin Xu ◽

Yingying Yang ◽

...

Keyword(s):

Genetic Diversity ◽

Structure Analysis ◽

Sampling Method ◽

Cross Validation ◽

Rice Varieties ◽

Germplasm Collections ◽

Population Structure Analysis ◽

Cross Validation Error ◽

Minimum Number

Abstract Background: Classification of germplasm collections is of great importance for both the conservation and utilization of genetic resources. Thus, it is necessary to estimate and classify rice varieties in order to utilize these germplasms more efficiently for rice breeding. However, molecular classification of large germplasm collections can be costly and labor-intensive. Development of an informative panel of a few markers would allow for rapid and cost-effective assignment of crops to genetic sub-populations.Results: Here, the minimum number of random SNP for rice classification (MNRSRC) was studied using a panel of 51 rice varieties belonging to different sub-groups. Through the genetic structure analysis, the rice panel can be obviously divided into five subgroups. The estimation of the MNRSRC was performed using SNP random sampling method based on genetic diversity and population structure analysis. In the genetic diversity analysis, statistical analysis of the coefficient of variation (CV) was performed for MNRSRC estimation, and we found that CV variation tended to plateau when the number of SNP was around 200, which was verified by the both cross-validation error of K value and correlation analysis of genetic distance. When the number of SNPs was greater than 200, the distribution of cross-validation error value tended to be similar, and correlation coefficients, almost greater than 0.95, exhibited small range of variation. In addition, we found that MNRSRC might not be affected by the number of varieties and the type of varieties.Conclusion: The estimation of the MNRSRC was performed using SNP random sampling method based on genetic diversity and population structure analysis. The results demonstrated that at least about 200 random filtered SNP loci were required for classification in a rice panel. In addition, we also found that MNRSRC might not be affected by the number of varieties and the type of varieties. The study on MNRSRC in this study can provide a reference and theoretical basis for classification of different types of rice panels.

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Modified Nodal Stage of esophageal cancer based on the evaluation of the hazard rate of the negative and positive lymph node

10.21203/rs.3.rs-32922/v2 ◽

2020 ◽

Author(s):

Jinling Zhang ◽

Hongyan Li ◽

Liangjian Zhou ◽

lianling Yu ◽

Fengyuan Che ◽

...

Keyword(s):

Esophageal Cancer ◽

Lymph Node ◽

Roc Curve ◽

Hazard Rate ◽

Cross Validation ◽

Predictive Accuracy ◽

Positive Lymph Node ◽

Validation Method ◽

Nodal Stage ◽

N Stage

Abstract Objective: The study aimed to propose a modified Nodal stage of esophageal cancer (EC) on basis of the number of positive lymph node (PLN) and the number of negative lymph node (NLN) simultaneously. Method: Data from 13,491 patients with EC registered in the SEER database were reviewed. The parameters related to prognosis were investigated using a Cox proportional hazards regression model. A modified N stage was proposed based on the cut-off number of the re-adjusted ratio of the number of PLN (numberPLN) to the number of NLN (numberNLN), which were derived from the comparison of the hazard rate (HR) of numberPLN and numberNLN. The modified N stage was confirmed using the cross-validation method with the training and validation cohort, and it was also compared to the N stage from the American Joint Committee on Cancer (AJCC) staging system (7th edition) using Receiver Operating Characteristic (ROC) curve analysis.Results: The numberPLN on prognosis was 1.064, while numberNLN was 0.962. The modified N stage was defined as follows: N1 stage: the ratio range was from 0 to 0.08; N2 stage: more than 0.08, but no more than 0.63; N3 stage: more than 0.63. Cross-validation method within the cohort identified the predictive accuracy of this modified N stage, and ROC curve analysis demonstrated the relative superiority of the modified N stage over that of the AJCC N stage.Conclusion: The modified N stage based on the re-adjusted ratio of numberPLN to numberNLN can evaluate tumor stage relative accurately than the traditional N stage.

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