Index Of Agreement Values Research Articles

The conversion of permittivity as measured by a PR2 capacitance probe into soil moisture values for Des Moines lobe soils in Iowa

Measurement of soil moisture is essential for irrigation scheduling and capacitance sensors have been widely used to monitor soil moisture at different depths. Two approaches for converting permittivity measures using the capacitance probe (PR2, Delta-T Devices) to soil water content are to (a) use the default equation and parameters provided by the manufacturer, and (b) use site specific calibration equations. The objective of this study was to evaluate the performance of the manufacturer’s default equation and in-situ calibrated equations for estimating soil water content. Permittivity measurement using the PR2 probe coincided with soil sampling during the growing seasons in 2006, 2007 and 2008 for Des Moines lobe soils in north-central Iowa. The default equation provided by Delta-T Devices for the PR2 probe estimated the soil water content for 3 years with an average root mean square error (RMSE) and index of agreement (IoA) values of 0.097 cm3/cm3 and 0.587, respectively. The default equation was calibrated by a 1-year (2006) and a 2-year (2006 + 2007) data set. The resultant statistics indicate that site specific calibration gives more accurate estimates of soil water content compared to the uncalibrated default equation. Three-year averaged RMSE and IoA values were 0.049 cm3/cm3 and 0.742 for equations calibrated by the 1-year data set, and 0.034 cm3/cm3 and 0.807 for equations calibrated by the 2-year data set. The results from this study indicate that a site specific calibration is necessary for the PR2 probe, and equations calibrated by data from a longer period performed better than data from a shorter period. Where a site-specific field calibration cannot be applied, coefficients are provided for various cropping systems in Des Moines Lobe soils of Iowa based on the results from this study.

A modeling approach for iron concentration in sand filtration effluent using adaptive neuro-fuzzy model

Effluent iron concentration is an important water quality criterion used for the assessment of the performance of rapid sand filters, in addition to other criteria. This study deals with the prediction of effluent iron concentrations by adaptive neuro-fuzzy (ANFIS) model with input parameters including filter hydraulic loading rate, influent iron concentration, bed porosity and operation time. With trying various types of membership functions, two rule base generation methods, namely subtractive clustering and grid partition were used for a first order Sugeno type inference system. Models were evaluated using root mean squared error (RMSE), index of agreement (IA) and R 2 as statistical performance parameters. The fit between experimental results and model outputs showed good agreement for tap water and deionized water; testing RMSE values were 36.33 and 7.66 μg/L, the IA values were 0.996 and 0.971, and R 2 values were 0.99 and 0.89, respectively. It was concluded that neuro-fuzzy modeling may be successfully used to predict effluent iron concentration in sand filtration.

Evaluation of a modelling system for predicting the concentrations of PM 2.5 in an urban area

We present a modelling system that contains a treatment of the emissions and atmospheric dispersion of fine particulate matter (PM 2.5) on an urban scale, combined with a statistical model for estimating the contribution of long-range transported aerosols. The model of PM 2.5 emissions includes exhaust emissions, cold starts and driving, as well as, non-exhaust emissions originated from urban vehicular traffic. The influence of primary vehicular emissions from the road and street network was evaluated using a roadside emission and dispersion model, CAR-FMI, in combination with a meteorological pre-processing model, MPP-FMI. We have computed hourly sequential time series of the PM 2.5 concentrations in 2002 in a numerical grid in the Helsinki Metropolitan Area. The predicted results were compared against measured data at two locations in central Helsinki: urban roadside station of Vallila and urban background station of Kallio. The predicted daily average PM 2.5 concentrations agreed well with the measured values; e.g., the index of agreement values were 0.83 and 0.86 at Vallila and Kallio, respectively, and the absolute values of fractional bias ⩽0.13. As expected, the scatter of data points is substantially wider for the hourly concentration values; e.g., the index of agreement values were 0.69 and 0.74. We also computed the spatial concentration distributions of PM 2.5. The predicted contribution from long-range transport to the street level PM 2.5 varied spatially from 40% in the most trafficked areas to nearly 100% in the outskirts of the area. The emissions originated from cold starts and driving were responsible for <4% of the annual average concentrations at the roadside and urban stations. The model can potentially be used as a practical tool of assessment of urban PM 2.5 contributions in various European regions.

Modelling nitrogen dynamics in soil–crop systems with HERMES

Model runs with HERMES were performed over entire crop rotation cycles for two experimental sites on loamy and sandy soils in Germany with differently managed plots. The model was able to simulate soil water and nitrogen contents on the sandy plots of Muncheberg with an index of agreement (IA) >0.8 and >0.69. Crop growth and N-uptake was simulated well with IA values >0.89 and >0.75, respectively. For the loess site in Bad Lauchstadt model results for above-ground biomass, storage organ and N-uptake agreed well with observations over a 4-year rotation with IA values of 0.93, 0.94 and 0.71, respectively. Soil mineral nitrogen was significantly overestimated on the cropped plot (IA = 0.45) as well as on the black fallow plot (IA = 0.65) using the default initialization of the decomposable nitrogen pools from the organic matter content. Equilibration of the pools, using data from a neighbouring long term experiment, improved soil mineral nitrogen simulation to an IA of 0.72 for the cropped and 0.91 for the fallow plot. The long term model performance was investigated using data from 1903 to 2002 of four differently managed plots in Bad Lauchstadt. Soil organic carbon, derived from simulated nitrogen pools, showed acceptable results for the unfertilized plot, but a distinct underestimation on plots with farmyard manure application. Simulated historical winter wheat and potato yields were distinctly overestimated during the initial 50 years. Therefore, an adoption of crop parameters for older varieties is necessary. The index of agreement of 0.9 indicates that the annual weather impact on yield fluctuations was correctly reflected.

Parameterization of the Snowmelt Runoff Model for the Salt-Verde System, Arizona During Drought Conditions

Abstract The snowmelt season for water years 2001 to 2003 were modeled for the Salt, Tonto, and Verde basins in Arizona using the Snowmelt Runoff Model (SRM). The SRM is a degree-day-driven snowmelt model. The purpose was to assess how well the SRM could simulate the snowmelt runoff conditions in Arizona, an arid subtropical environment that was also experiencing severe drought. Arizona's snowmelt season is characterized by multiple accumulation periods and near complete melt off periods that often occur during midwinter. Successful simulation of the streamflow would necessitate defining the proper values and patterns of the SRM parameters and assessing how this parameterization differs from other basins where the SRM has been run. Correlation coefficient values for modeled and measured streamflow on the Salt, Tonto, and Verde basins were 0.89, 0.89, and 0.91, respectively. Index of agreement values were 0.93, 0.94, and 0.95, respectively. Runoff coefficients increased during the snowmelt season until the...

SU‐FF‐T‐166: Reproducibility Of A Method For The Quanitative Assessment Of The Agreement Between Planned And Measured Dose Distributions

Purpose: To introduce a new quality assurance parameter, the index of agreement (IOA), with which to quantify the agreement between planned and measured radiation dose distributions, and to assess the reproducibility of the parameter. Method and Materials: One IMRT radiation portal, consisting of three segments, was used to irradiate twenty separate pieces of calibrated EDR film. The field was delivered with a Siemens PRIMUS linear accelerator with 10 MV photons. A planar dose file was generated with the ADAC Pinnacle RTP system at a resolution of 2 mm by 2 mm to represent the dose distribution of the field. Both the planar dose file and the measured dose distributions derived from the film were imported into MATLAB for processing and analysis. At each pixel of the planned dose distribution, the pixel of the measured dose distribution, within a closed neighborhood of radius c (chosen to be 3 mm) that exhibits the smallest absolute fractional dose deviation was found. If the absolute deviation was less than p% (chosen to be 3%), the pixel was assigned a score of zero, while if the absolute fractional deviation exceeded the tolerance value, the pixel was assigned a score of zero. The index of agreement (IOA) was then calculated as the ratio of the sum of the score values of all clinically relevant pixels divided by the total number of clinically relevant pixels. Results: The IOA values were approximately normally distributed, with a mean value of 0.9330 and a standard deviation of 1.1%. Areas of poor agreement were reproduced during each irradiation. Conclusion: The IOA is a promising new quantitative quality assurance tool, and its value is highly reproducible.

Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki

Five neural network (NN) models, a linear statistical model and a deterministic modelling system (DET) were evaluated for the prediction of urban NO2 and PM10 concentrations. The model evaluation work considered the sequential hourly concentration time series of NO2 and PM10, which were measured at two stations in central Helsinki, from 1996 to 1999. The models utilised selected traffic flow and pre-processed meteorological variables as input data. An imputed concentration dataset was also created, in which the missing values were replaced, in order to obtain a harmonised database that is well suited for the inter-comparison of models. Three statistical criteria were adopted: the index of agreement (IA), the squared correlation coefficient (R2) and the fractional bias. The results obtained with various non-linear NN models show a good agreement with the measured concentration data for NO2; for instance, the annual mean of the IA values and their standard deviations range from 0.86±0.02 to 0.91±0.01. In the case of NO2, the non-linear NN models produce a range of model performance values that are slightly better than those by the DET. NN models generally perform better than the statistical linear model, for predicting both NO2 and PM10 concentrations. In the case of PM10, the model performance statistics of the NN models were not as good as those for NO2 over the entire range of models considered. However, the currently available NN models are neither applicable for predicting spatial concentration distributions in urban areas, nor for evaluating air pollution abatement scenarios for future years.

Do Individual Perceptions of Group Cohesion Reflect Shared Beliefs?

This study's primary purpose was to examine the degree to which individual perceptions of cohesiveness reflect shared beliefs in sport teams. The secondary purposes were to examine how the type of cohesion, the task interactive nature of the group, and the absolute level of cohesion relate to the index of agreement. Teams (n = 192 containing 2,107 athletes) were tested on the Group Environment Questionnaire. Index of agreement values were greater for the group integration (GI) manifestations of cohesiveness (GI-task, rwg(j) = .721; GI-social,rwg(j) = .694) than for the individual attractions to the group (ATG) manifestations (ATG-task, rwg(j) = .621; ATG-social, rwg(j) = .563). No differences were found for interactive versus coactive/independent sport teams. A positive relationship was observed between the absolute level of cohesiveness and the index of agreement. Results were discussed in terms of their implication for the aggregation of individual perceptions of cohesion to represent the group construct.

Validation of two air quality models for Indian mining conditions.

All major mining activity particularly opencast mining contributes to the problem of suspended particulate matter (SPM) directly or indirectly. Therefore, assessment and prediction are required to prevent and minimize the deterioration of SPM due to various opencast mining operations. Determination of emission rate of SPM for these activities and validation of air quality models are the first and foremost concern. In view of the above, the study was taken up for determination of emission rate for SPM to calculate emission rate of various opencast mining activities and validation of commonly used two air quality models for Indian mining conditions. To achieve the objectives, eight coal and three iron ore mining sites were selected to generate site specific emission data by considering type of mining, method of working, geographical location, accessibility and above all resource availability. The study covers various mining activities and locations including drilling, overburden loading and unloading, coal/mineral loading and unloading, coal handling or screening plant, exposed overburden dump, stock yard, workshop, exposed pit surface, transport road and haul road. Validation of the study was carried out through Fugitive Dust Model (FDM) and Point, Area and Line sources model (PAL2) by assigning the measured emission rate for each mining activity, meteorological data and other details of the respective mine as an input to the models. Both the models were run separately for the same set of input data for each mine to get the predicted SPM concentration at three receptor locations for each mine. The receptor locations were selected such a way that at the same places the actual filed measurement were carried out for SPM concentration. Statistical analysis was carried out to assess the performance of the models based on a set measured and predicted SPM concentration data. The value of coefficient of correlation for PAL2 and FDM was calculated to be 0.990-0.994 and 0.966-0.997, respectively, which shows a fairly good agreement between measured and predicted values of SPM concentration. The average index of agreement values for PAL2 and FDM was found to be 0.665 and 0.752, respectively, which represents that the prediction by PAL2 and FDM models are accurate by 66.5 and 75.2%, respectively. These indicate that FDM model is more suited for Indian mining conditions.

A semi-empirical model for urban PM 10 concentrations, and its evaluation against data from an urban measurement network

We have developed a model for evaluating the mass-based concentrations of urban particulate matter. The basic model assumption is that local vehicular traffic is responsible for a substantial fraction of the street-level concentrations of both PM 10 and NO x , either due to primary emissions or resuspension from street surfaces. The modelling system utilises the data from an air quality monitoring network in the Helsinki Metropolitan Area. We have determined linear relationships between the measured urban PM 10 data against those of NO x in various urban surroundings, based on continuously measured hourly concentration values. The data was obtained from two stations in central Helsinki and one suburban station in the Helsinki Metropolitan Area during a period of 3 yr, from 1996 to 1998. The model also includes a treatment of the regional background concentrations, and resuspended particulate matter. The model performance was evaluated against the measured PM 10 data from the above-mentioned three stations and from two other stations, using data that was measured in 1999. We used two alternative model versions, one based on separate correlation parameters (PM 10 vs. NO x ) for each station, and another based on parameters averaged over the stations considered. We analysed the agreement between the measured and predicted hourly concentration time series, utilising the values of the fractional bias (FB) and the so-called index of agreement (IA). As expected, the model predicts relatively well the yearly mean concentrations of PM 10: the FB values range from −0.05 to +0.09. Model performance is also relatively good when predicting the yearly mean values that are classified separately for each hour of the day: the corresponding IA values range from 0.85 to 0.96. However, model performance is substantially worse in predicting the hourly time series of the year: the IA values using the station-specific parameters range from 0.46 to 0.65. The model was applied in evaluating the yearly average spatial concentration distribution of PM 10 in central Helsinki, based on the corresponding modelled NO x concentrations. With re-evaluation of a few parameters that can be determined empirically, the model could be evaluated, and most probably applied, in other urban areas as well.