Index Of Agreement Values Research Articles

Photosynthetically Active Radiation Temperature Dependent Models and Its Variability with Climatic Variables over Akure, Nigeria

Photosynthetically Active Radiation (PAR) is an essential input for applications pertaining to biomass production, plant physiology, and natural lighting in greenhouses. It is the amount of light energy necessary for photosynthesis to occur, and the wavelengths of this light are typically between 400 and 700 nm. In this study, the photosynthetically active radiation for Akure (latitude 7.28°N, longitude 5.30°E, and 375 m asl.), located in the coastal region of Nigeria, was estimated and investigated using data on the monthly averaged daily global solar radiation, relative humidity, wind speed, minimum and maximum temperature from the National Aeronautics and Space Administration (NASA) archives over a thirty-eight-year period (1984–2021). In order to determine the accuracy of the models, eight (8) new temperature PAR-based models were developed for the location and statistical tests were conducted using the coefficient of determination (R2), mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), t-test, and index of agreement (IA). The findings demonstrated that PAR for Akure is lowest in the month of August with 6.5320 MJm-2day-1 during the rainy season (April to October) and highest during the dry season (November to March), with 8.6670 MJm-2day-1 in March. The best model equation for calculating PAR for Akure was found to be the one that links the PAR and natural logarithm of temperature changes; its R2, MBE, RMSE, MPE, t-test, and IA values were 68.1%, 0.0032 MJm-2day-1, 0.5208 MJm-2day-1, -0.4479%, 0.0204, and 82.8013%, respectively.

Estimation and Investigation of Photosynthetically Active Radiation Using Meteorological Parameters over Ikeja, Nigeria

Photosynthetically active radiation (PAR; 0.4 - 0.7μm) is a key driver in ecosystem biochemical processes, and thus a critical factor in agriculture productivity, ecosystem-atmosphere energy, and CO2 fluxes. In this study, the measured monthly averaged daily global solar radiation, relative humidity, wind speed, minimum and maximum temperature dataset was utilized to estimate and investigate the Photosynthetically Active Radiation (PAR) for Ikeja (latitude 6.58 N, longitude 3.33 E and 39.4 m above sea level) located in the coastal region ofNigeria. The meteorological parameters used in this work were obtained from the archives of National Aeronautics and space Administration (NASA) for a period of thirty-eight years (1984-2021).The newly developed temperature PAR based models were statistically tested using the coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test and index of Agreement (IA) to ascertain the accuracy and applicability of the models. The results show that, the highest and lowest values of PAR were found during the rainy season with 8.3426 MJ and 6.4936 MJ in the months of April and July respectively. Based on the developed models, the model equation (16g) is the most suitable for estimating PAR for Ikeja with R2, MBE, RMSE, MPE, t-test and IA values of 80.9 , 0.0062 MJ , 0.3415 MJ -0.1974 0.0601 and 92.8712 respectively.

Determining the long-term impact area of coastal thermal discharge based on a harmonic model of sea surface temperature

AbstractCoastal nuclear power plants discharge large amounts of warm cooling water, which may have environmental impacts. This study proposes a method for determining the long-term impact area based on the average distribution of sea surface temperate (SST) increases. Taking the Daya Bay Nuclear Power Plant as a case study, 101 TM/ETM+ images acquired from 2000 to 2013 were used to obtain SST products. Cross-validation with NR_2P products showed that the accuracy of the SST products, in terms of the systematic error, root-mean-square error, and mean absolute error of 1,000 randomly selected verification points, was all <0.3°C, while Willmott’s index of agreement values was all >0.7. An annual SST cycle harmonic model was established. The mean difference between the modeled and observed SSTs was −2.1 to 2.5°C with a standard deviation range of 0–1°C. The long-term impact area was extracted by the harmonic analysis method and multi-year average method for comparison. The following conclusions can be drawn: 1) with sufficient SST samples, the temperature distributions of the two methods are similar, with the multi-year average method giving less noise and clearer boundaries. 2) When SST data are lacking for some months, the mean and standard deviation of the percentage of pixels belonging to areas of different temperature rise were calculated. The standard deviations of the two methods were both <0.04 in the temperature-rise classes of 1–2, 2–3, 3–4, and 4–5°C, while in the 0–1°C class, the standard deviation of the multi-year average method was 0.461, which is much higher than that of the multi-year average method (0.098). Performing statistical analysis on all pixels of >0°C, the multi-year average method had a standard deviation of 0.506, while the harmonic analysis method had a value of 0.128. Overall, the harmonic analysis method makes it possible to obtain and evaluate the long-term stability impact area of the thermal discharge over a period of time comprehensively and quantitatively. Even though it introduces a small amount of noise, it has less dependence on the input SST products and could improve the stability and reliability of thermal discharge monitoring, providing technical support for precise pollution control.

Application of MLR-PRN model for estimation of arsenic concentration in drinking water: a case study for İzmir City

ABSTRACT Arsenic in drinking water can have serious adverse health effects depending on consumption. This study includes the comparison of two neural networks, Pattern Recognition Network (PRN) and Cascade Forward Neural Network (CFNN), that uses chloride, pH, and electrical conductivity variables at their inputs for estimation of arsenic in drinking water at the water distribution stations in İzmir. The data set consists of arsenic, electrical conductivity, iron, aluminum, pH, and chloride chemical variables, measured every two weeks (average) and taken from 16 water distribution points. Multiple linear regression (MLR) analysis showed that electrical conductivity is the most significant variable for arsenic estimation. The MAE (Mean Absolute Error), NSE (Nash-Sutcliffe Efficiency), IA (Index of Agreement), and R2 (coefficient of determination) values obtained with the PRN model were 0.653, 0.789, 0.944, and 0.802, respectively. These results showed that arsenic concentration in drinking water could be estimated using chloride, pH, and electrical conductivity.

Evaluating urban outdoor thermal comfort: a validation of ENVI-met simulation through field measurement

Thermal comfort plays a significant role in encouraging people to utilize outdoor spaces. Therefore, this feature must be analyzed and evaluated in order to be improvised. Computational fluid dynamics (CFD) is an alternative technique that predicts thermal comfort and environmental parameters. Validation of CFD is important to ensure its effectiveness. This study assessed the performance of ENVI-met for its ability to estimate thermal indices (PET) by comparing it to field measurement for various points in a street canyon in Port Said, Egypt, throughout the summer and winter seasons. Except for the limited air velocity correlation, the results presented very good agreement, particularly with respect to the final results of the PET visually curved and numerical values, with an index of agreement value ranging from 0.81 to 0.95. The study's conclusions concern the use of the ENVI-met simulation model as a tool for assessing outdoor thermal comfort. Highlights Outdoor thermal comfort was investigated in a hot-humid climate. The effectiveness of using a Computational fluid dynamics (CFD) simulation software program such as ENVI-met was evaluated as an alternative technique in urban design. Varied environmental parameters and outdoor thermal comfort indices were evaluated. It was concluded that validating CFD simulation through field measurement was significant in offering integrated decisions for urban design. Abbreviations: Environmental parameters; Ta; Air temperature; Tmrt; Mean radiant temperature; RH; Relative humidity; Va; Air velocity; Thermal comfort indices; PMV; Predicted mean vote; r; PPD; Predicted percentage of dissatisfaction; PET; Physiological Equivalent Temperature; UTCI; Universal Thermal Climate; UCB; University of California-Berkeley; ETU; universal effective temperature; Simulation Software; CFD; Computational fluid dynamics; UCB; University of California-Berkeley; LES; Large Eddy Simulation; RANS; Reynolds Averaged Navier Stoke; Error calculations; r; Pearson correlation coefficient; R2; Coefficient of determination; RMSE; Root Mean Squared Error; MAE; Mean Absolute Error; IA; Index of agreement; ASHRAE; American Society of Heating; Refrigeration; and Air-Conditioning Engineers.

Testing the accuracy of high-resolution satellite-based and numerical model output precipitation products over Ethiopia

The indirect rainfall estimates by satellites and numerical models are the alternative options for the regions lacking enough and accurate ground observations. However, these indirect estimates often lack homogeneity and need to be evaluated before application. This study used gauge observations to test the accuracy of recently produced high-resolution satellite-based and numerical model output rainfall products over Ethiopia. Tropical Applications of Meteorology Using Satellite data and Ground-Based Observations (TAMSAT v3.1), Climate Hazard group Infrared Precipitation with Stations (CHIRPS v2.0), and the ERA5 reanalysis products were evaluated at monthly, seasonal, and annual temporal scales for the years 1992–2009. The satellite products showed nearly similar characteristics with much better accuracy than the model reanalysis output, which underestimated the rainfall amounts. Both satellite and reanalysis products captured the shapes of the rainfall at a monthly scale but less accurately at a seasonal scale. In general, the satellite-based products outperformed the reanalysis data set with a high correlation coefficient and index of agreement values, as well as low Root Mean Square Error and BIAS values. On the other hand, the reanalysis (ERA5) product showed a considerable underestimation in all sites. Therefore, satellite-based products are more reliable for researches in the region. However, the algorithms in both satellites need further calibration for a better estimation of seasonal rainfall amounts.

Application of ddPCR for detection of Enterococcus spp. in coastal water quality monitoring

Droplet digital polymerase chain reaction (ddPCR) was evaluated for the detection of fecal indicator bacteria (FIB), Enterococcus spp., in San Diego County beach water samples collected under diverse conditions, from multiple pollution sources, as part of regulatory monitoring activities over 20 months. Two US EPA-approved methods, qPCR (EPA 1609.1) and Enterolert (SM9230D), were used as reference comparator methods. A total of 361 samples were assayed by both ddPCR and qPCR and yielded an acceptable Index of Agreement (IA) of 0.89, based on EPA Site-Specific analysis guidelines. A Pearson's correlation coefficient of r = 0.87 (p < 0.001), further indicated a strong relationship between the methods results. From the 361 samples, 185 split samples with ddPCR and Enterolert values within the limits of quantification, were used as a ‘training’ data set to derive an intrinsic copy number equation (ICE) for scaling ddPCR gene copy number to Enterolert most probable number (MPN). Of the 1993 samples that comprised the complete ‘test’ data set assayed by ddPCR and Enterolert, 1086 generated results that fell within the limits of quantification for Enterolert and yielded an overall IA of 0.64. Re-analysis using median as a measure of central tendency to account for significant skewing of Enterolert data yielded an IA of 0.72. Beach grouping-specific IA values ranged from 0.63 to 0.93. Pearson's correlation coefficient, r, ranged from 0.13 to 0.94 within beach groupings and generated a combined value of 0.60 for all groupings. Using the ICE, a ddPCR advisory threshold of 1413 DNA copy number/100 mL was empirically determined to be the equivalent to the California Enterolert beach action threshold of 104 MPN/100 mL, based on comparison with all 1993 paired ddPCR and Enterolert results. Using the 1413 DNA copy number/100 mL as a beach action threshold for ddPCR resulted in a 90.4% agreement with Enterolert (6.0% false negative and 3.7% false positive). Together these findings support the conclusion that ddPCR readouts align closely with Enterolert MPN for identifying FIB exceedance levels of Enterococcus spp. in coastal waters of San Diego, CA.

Modeling the effect of VOCs from biomass burning emissions on ozone pollution in upper Southeast Asia

We used a Weather Research and Forecasting Model with Chemistry (WRF-CHEM) model that includes anthropogenic emissions from EDGAR-HTAP, biomass burning from FINN, and biogenic emissions from MEGAN to investigate the main volatile organic compound (VOC) ozone precursors during high levels of biomass burning emissions in March 2014 over upper Southeast Asia. A comparison between the model and ground-based measurement data shows that the WRF-CHEM model simulates the precipitation and 2 m temperature reasonably well, with index of agreement (IOA) values ranging from 0.76 to 0.78. Further, the model predicts O3, NO2, and CO fairly well, with IOA values ranging from 0.50 to 0.57. However, the magnitude of the simulated NO2 concentration was generally underestimated compared to OMI satellite observations. The model result shows that CO and VOCs such as BIGENE play an important role in atmospheric oxidation to surface O3. In addition, biomass burning emissions are responsible for increasing surface O3 by ∼1 ppmv and increasing the reaction rate of CO and BIGENE by approximately 0.5 × 106 and 1 × 106 molecules/cm3/s, respectively, in upper Southeast Asia.

Forecasting municipal solid waste generation using artificial intelligence models—a case study in India

Proper management of municipal solid waste is one of the prime matters of concern for metropolitan cities. To be able to successfully manage the solid waste generated, we need to plan in advance. A very essential pre-requirement for an efficient solid waste management is an accurate prediction of the garbage generation. Accurate forecasting of the quantity of MSW generation will enable us to design and operate an effective waste collection system. The main objective of this paper is to compare different models of artificial intelligence, viz. artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), discrete wavelet theory–artificial neural network (DWT-ANN), discrete wavelet theory–adaptive neuro-fuzzy inference system (DWT-ANFIS), genetic algorithm–artificial neural network (GA-ANN) and genetic algorithm–adaptive neuro-fuzzy inference system (GA-ANFIS) to examine and evaluate their capability in forecasting the amount of garbage being generated. A case example of the city of New Delhi, India, has been used for better understanding of different models. Root mean square error (RMSE), coefficient of determination (R2) and index of agreement (IA) values for every model were calculated, and the models were compared on the basis of it. The hybrid model of genetic algorithm and artificial neural network was found to have the lowest RMSE, the highest IA value and the highest R2 values, and hence is the most accurate of the above six models.

Automatic, unstructured mesh optimization for simulation and assessment of tide- and surge-driven hydrodynamics in a longitudinal estuary: St. Johns River

A localized truncation error analysis with complex derivatives (LTEA+CD) is applied recursively with advanced circulation (ADCIRC) simulations of tides and storm surge for finite element mesh optimization. Mesh optimization is demonstrated with two iterations of LTEA+CD for tidal simulation in the lower 200 km of the St. Johns River, located in northeast Florida, and achieves more than an over 50% decrease in the number of mesh nodes, relating to a twofold increase in efficiency, at a zero cost to model accuracy. The recursively generated meshes using LTEA+CD lead to successive reductions in the global cumulative truncation error associated with the model mesh. Tides are simulated with root mean square error (RMSE) of 0.09–0.21 m and index of agreement (IA) values generally in the 80s and 90s percentage ranges. Tidal currents are simulated with RMSE of 0.09–0.23 m s−1 and IA values of 97% and greater. Storm tide due to Hurricane Matthew 2016 is simulated with RMSE of 0.09–0.33 m and IA values of 75–96%. Analysis of the LTEA+CD results shows the M2 constituent to dominate the node spacing requirement in the St. Johns River, with the M4 and M6 overtides and the STEADY constituent contributing some. Friction is the predominant physical factor influencing the target element size distribution, especially along the main river stem, while frequency (inertia) and Coriolis (rotation) are supplementary contributing factors. The combination of interior- and boundary-type computational molecules, providing near-full coverage of the model domain, renders LTEA+CD an attractive mesh generation/optimization tool for complex coastal and estuarine domains. The mesh optimization procedure using LTEA+CD is automatic and extensible to other finite element-based numerical models. Discussion is provided on the scope of LTEA+CD, the starting point (mesh) of the procedure, the user-specified scaling of the LTEA+CD results, and the iteration (termination) of LTEA+CD for mesh optimization.

Modeling hourly subsurface drainage using steady-state and transient methods

Computer models have been frequently used to simulate the hydrologic and environmental processes in subsurface-drained cropland. The widely-tested steady-state Hooghoudt (ssH) equation, implemented in the Root Zone Water Quality Model (RZWQM2, version 2.94.00), serves in simulating subsurface drainage. However, transient methods such as the integrated Hooghoudt (inH) and van Schilfgaarde (vanS) equations have seldom been implemented in models. In the present study, RZWQM2’s hydrologic component was modified to initiate the soil water redistribution process when rainfall occurred. The three drainage equations (ssH, inH and vanS) were tested in each of two versions of RZWQM2 (original and modified). Field data from Iowa (2007–2008) and Ontario (2009–2010) were used to evaluate different model version×equation combinations’ simulation accuracy at both daily and hourly scales, evaluated using the percent of bias (PBIAS), Nash-Sutcliffe efficiency coefficient (NSE), and the Index of Agreement (IoA). On a daily scale and across equations, for the Iowa data the original model (PBIAS⩽14.96,NSE⩾0.40,⩾0.69) was outperformed by the modified model (PBIAS⩽6.48,NSE⩾0.70,IoA⩾0.76). Similarly, for the Ontario data, the original model (PBIAS⩽8.87,NSE⩾0.19,IoA⩾0.65) was outperformed by the modified model (PBIAS⩽3.59,NSE⩾0.31,IoA⩾0.67). However, based on a parity of PBIAS, NSE and IoA values, hourly scale tile drainage computed using the modified model equipped with transient equations did not improve model performance compared with the original ssH equation.

Estimation of daily reference evapotranspiration using artificial neural network for Raipur region in Chhattisgarh

This study presents estimation of daily reference evapotranspiration using artificial neural network (ANN) for Raipur region of Chhattisgarh. An ANN is a flexible mathematical structure, which is capable of identifying complex nonlinear relationships between input and output data sets. To test artificial neural networks (ANN) for estimating reference evapotranspiration with limited climatic data, comparison has been made with Penman Montieth (PM) method. Data on daily maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), wind speed (WS) and sunshine hours (SSH) of 31 years were collected from the observatory of Department of Agrometeorology, IGKV, Raipur. Four different models have been developed based on input combination viz: M1 (Tmax, Tmin, RH (I), RH (II), WS and SSH), M2 (Tmax, RH (I), RH (II), WS and SSH), M3 (Tmax, WS and SSH) and M4 (Tmax and SSH). The analysis was carried out in MATLAB R2014b software. Performance evaluation of the models have been carried out by calculating mean absolute deviation (MAD), root mean square error (RMSE), absolute Prediction Error (APE), coefficient of correlation (CC), coefficient of efficiency (CE) and Index of Agreement (IOA). Upon comparison the Model M1R showed the highest CC, CE and IOA value as 99.7, 99.4 and 99.8 during training and highest CC, CE and IOA value as 99.7, 99.5 and 99.6 during testing period. The developed ANN models may therefore be adopted for estimating ET0 in the region with reasonable degree of accuracy.

Validating the California Puff (CALPUFF) Modelling System Using an Industrial Area in Accra, Ghana as a Case Study

The performance of the California Puff (CALPUFF) modelling system has been evaluated using a case study in Ghana. The performance evaluation consisted of a quantitative comparison of dispersion simulation results of SO2 and NO2 with measurements at the Tema Oil Refinery, and meteorological simulation results with observations from the Tema Meteorological Station, both in the Greater Accra region of Ghana. Four statistical indicators—Index of Agreement (IOA), Fractional Bias (FB), Normalized Mean Square Error (NMSE) and the Pearson correlation coefficient(R) employed in the assessment indicate sufficient reliability of both CALPUFF and its meteorological simulator, CALMET. IOA values of 0.73 and 0.67 and FB values of 1.65 and 1.42 were obtained for SO2 and NO2 respectively. IOA between measured and modelled emissions were 0.72 and 0.69 for SO2 and NO2 respectively. The correlations between the simulated and observed emission were 0.66 and 0.08 for SO2 and NO2 respectively. An IOA value of 0.66 was obtained for both wind speed and wind direction with correlations of 0.29 and 0.58 in comparison with observations from the meteorological station.

Simulating the Impacts of Projected Climate Change on Streamflow Hydrology for the Chesapeake Bay Watershed

A gridded model was developed to simulate the hydrology of the Chesapeake Bay Watershed, the largest estuary in the United States. CMIP3 and CMIP5 climate projections were used to drive the model to assess changes in streamflow and watershed-wide hydrology. Index of agreement values indicated good model performance. Annual average temperature is projected to increase 1.9°C to 5.4°C by 2080 to 2099, with the greatest warming occurring in summer and fall in the northern part of the watershed. Annual total precipitation is projected to increase between 5.2 percent and 15.2 percent by 2080 to 2099, with the largest increases generally occurring in winter. Average evapotranspiration and rainfall are projected to increase while snowfall, snow water storage, and snowmelt decrease. Subsurface moisture is projected to decrease during the warmer months and the time to recharge increases and, in some cases, never actually occurs. Changes in annual runoff for all 346 climate projections averaged 0 percent (2020–2039), –1.5 percent (2050–2069), and –5.1 percent (2080–2099). There is a 48 percent, 52 percent, and 60 percent chance, respectively, for the future time periods that annual runoff will be less than baseline values (1950–1999). Extreme runoff projections are overwhelmingly associated with the negative end of the distribution. Runoff increases are confined to January through March and to higher elevations. This study is novel in its use of a large number of climate models, the gridded nature of the hydrologic model, and the simulation of several hydrologic variables, all of which allowed for the assessment of both uncertainty in the projections and variation across multiple spatial and temporal scales.

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

Rice is a primary staple food for the world population and there is a strong need to map its cultivation area and monitor its crop status on regional scales. This study was conducted in the Qixing Farm County of the Sanjiang Plain, Northeast China. First, the rice cultivation areas were identified by integrating the remote sensing (RS) classification maps from three dates and the Geographic Information System (GIS) data obtained from a local agency. Specifically, three FORMOSAT-2 (FS-2) images captured during the growing season in 2009 and a GIS topographic map were combined using a knowledge-based classification method. A highly accurate classification map (overall accuracy = 91.6%) was generated based on this Multi-Data-Approach (MDA). Secondly, measured agronomic variables that include biomass, leaf area index (LAI), plant nitrogen (N) concentration and plant N uptake were correlated with the date-specific FS-2 image spectra using stepwise multiple linear regression models. The best model validation results with a relative error (RE) of 8.9% were found in the biomass regression model at the phenological stage of heading. The best index of agreement (IA) value of 0.85 with an RE of 13.6% was found in the LAI model, also at the heading stage. For plant N uptake estimation, the most accurate model was again achieved at the heading stage with an RE of 11% and an IA value of 0.77; however, for plant N concentration estimation, the model performance was best at the booting stage. Finally, the regression models were applied to the identified rice areas to map the within-field variability of the four agronomic variables at different growth stages for the Qixing Farm County. The results provide detailed spatial information on the within-field variability on a regional scale, which is critical for effective field management in precision agriculture.

Evaluation of FAO-56 Penman–Monteith and alternative methods for estimating reference evapotranspiration using limited climatic data at Pusa

The FAO-56 Penman–Monteith method (FAO-56 PM) is standard method recognized by the Food and Agriculture Organization of the United Nations for estimating reference evapotranspiration (ETO). Unfortunately, some of climatic variables, especially relative humidity, solar radiation and wind speed are often missing which could impede the estimation of ETO with the FAO-56 PM method. To overcome the problem of availability of climatic variables, procedures to estimate ETO with missing climate data are proposed as part of the FAO methodology. Therefore, assessing the accuracy of these procedures for Pusa Observatory is important.&#x0D; The comparison of ETO estimates using limited data to those computed with full data set revealed that the difference between ETO obtained from full and limited data set is small. Both the Mean Bias Error (MBE) and the Root Mean Square Error (RMSE) of the comparison were less than 0.35 and 1.00 with a minimum of “0.11 and 0.29 mm day”1, respectively, leading to small errors in the ETO estimates. R2, ÷2-test and index of agreement values confirmed strong relationships among data for the year 1998 to 2006.

Hydrological evaluation of the LPX dynamic global vegetation model for small river catchments in the UK

Assessments of water resources by using macro-scale models tend to be conducted at the continental or large catchment scale. However, security of freshwater supplies is a local issue and thus necessitates study at such a scale. This research aims to evaluate the suitability of the Land Processes and eXchanges dynamic global vegetation model (LPX-DGVM) for simulating runoff for small catchments in the UK. Simulated annual and monthly runoff is compared against the National River Flow Archive streamflow observations from 12 catchments of varying size (500–10 000 km2) and climate regimes. Results show that LPX reproduces observed inter-annual and intra-annual runoff variability successfully in terms of both flow timings and magnitudes. Inter-annual variability in flow timings is simulated particularly well (as indicated by Willmott's index of agreement values of ≥0.7 for the majority of catchments), whereas runoff magnitudes are generally slightly overestimated. In the densely populated Thames catchment, these overestimations are partly accounted for by water consumption. Seasonal variability in runoff is also modelled well, as shown by Willmott's index of agreement values of ≥0.9 for all but one catchment. Absence of river routing and storage from the model, in addition to precipitation uncertainties, is also suggested as contributing to simulated runoff discrepancies. Overall, the results show that the LPX-DGVM can successfully simulate runoff processes for small catchments in the UK. This study offers promising insights into the use of global-scale models and datasets for local-scale studies of water resources, with the eventual aim of providing local-scale projections of future water distributions. Copyright © 2013 John Wiley & Sons, Ltd.

A Novel Data Preprocessing Method for the Modeling and Prediction of Freeze-Drying Behavior of Apples: Multiple Output–Dependent Data Scaling (MODDS)

In the present study, the freeze drying behavior of apples have been modeled and predicted. Because freeze-drying is a very expensive and complex process, modeling of the freeze-drying process is a challenging task. In this study, a novel data scaling method called multiple output–dependent data scaling (MODDS) has been proposed and combined with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the moisture content (MC), moisture ratio (MR), and drying rate (DR) values, which are outputs of freeze-drying behavior of apples. The input parameters of the freeze drying system are the sample thicknesses, drying time, pressure, relative humidity, chamber temperature, and sample temperature. Using the input parameters, the outputs of the freeze-drying process of apples were predicted using a hybrid system based on MODDS and ANFIS. In the first stage, only input parameters were scaled using MODDS. In the second stage, the outputs of freeze drying of apples were predicted with the scaled input parameters using ANFIS algorithm. Ninety-two samples were included in the data set, including 10-, 7-, and 5-mm samples. In order to evaluate the performance of the proposed model, the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), coefficient of determination (R 2), index of agreement (IA), and mean absolute percentage error (MAPE) were used. Though MSE values of 2.48, 0.035, and 0.011 and IA values of 0.887, 0.887, and 0.466 were obtained for MC, MR, and DR, respectively, using the ANFIS prediction algorithm the hybrid MODDS-ANFIS model achieved MSE values of 0.003, 0.00005, and 0.00007 and IA values of 0.999, 0.999, and 0.993 for the prediction of MC, MR, and DR, respectively. The results obtained demonstrate that the proposed hybrid system is a robust and efficient method for the modeling and prediction of freeze-drying behavior of apples.

SU‐D‐110‐06: Delineation of Target Volume Based on FDG PET‐CT Images and Its Effect on Tumor and Normal Tissue Doses

Purpose: To evaluate the effect of FDG PET‐CT imaging on target volume definition, inter and intra observer perceptions, and on tumor and normal tissue doses. Methods: The positron emission tomography with fluorine‐18‐ fluorodeoxyglucose (18F‐FDG) radiopharmaceutical and computed tomography (CT) combined provide structural and functional information for the definition of tumor and radio‐resistant regions that can be treated to high doses. This study uses data from twenty patients who had CT and PET imaging for treatment plan preparation. Treatment plans used non‐opposing co‐planar beams of four to seven fields. The prescribed dose was 70.2 Gy in 39 fractions. Physicians H and T independently delineated tumor volumes on CT and PET‐CT images and treatment plans were generated. Doses to CT contoured structures were obtained from plans that were originally planned based on contoured PET‐CT fused images. Tumor volumes of PET‐ CT and CT were compared by an index of agreement (IA). The intra and inter observer variations on tumor delineation, mean, minimum and maximum tumor doses, and percent differences in uninvolved lung doses were compared. Results: The mean intra observer IA value was approximately 0.50 for both physicians. The mean inter observer IA value was 0.45 and 0.52 for CT and fused PET‐CT tumor outlining respectively. The percent differences in average minimum and mean tumor doses for PET‐CT and CT plans were 60.1% and 8.1% respectively for physician H, and 78.4% and 11.7% respectively for physician T. The uninvolved lung mean dose variations for PET‐CT and CT plans were 5.6% and 1.9% for physicians H and T respectively, and between both physicians the differences were 9.6% and 12.7% for PET‐CT and CT plans respectively. Conclusions: The volumes created on PET‐CT images by two experienced physicians were greatly different compared to volumes created on CT only, resulting in extreme differences in calculated doses.

A novel data preprocessing method to estimate the air pollution (SO2): neighbor-based feature scaling (NBFS)

The forecasting of air pollution is important for living environment and public health. The prediction of SO2 (sulfur dioxide), which is one of the indicators of air pollution, is a significant part of steps to be done in order to decrease the air pollution. In this study, a novel feature scaling method called neighbor-based feature scaling (NBFS) has been proposed and combined with artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) prediction algorithms in order to predict the SO2 concentration value is from air quality metrics belonging to Konya province in Turkey. This work consists of two stages. In the first stage, SO2 concentration dataset has been scaled using neighbor-based feature scaling. In the second stage, ANN and ANFIS prediction algorithms have been used to forecast the SO2 value of scaled SO2 concentration dataset. SO2 concentration data- set was obtained from Air Quality Statistics database of Turkish Statistical Institute. To constitute dataset, the mean values belonging to seasons of winter period have been used with the aim of watching the air pollution changes between dates of December, 1, 2003 and December, 30, 2005. In order to evaluate the performance of the proposed method, the performance measures including mean abso- lute error (MAE), mean square error (MSE), root mean square error (RMSE), and IA (Index of Agreement) values have been used. After NBFS method applied to SO2 con- centration dataset, the obtained RMSE and IA values are 83.87-0.27 (IA) and 93-0.33 (IA) using ANN and ANFIS, respectively. Without NBFS, the obtained RMSE and IA values are 85.31-0.25 (IA) and 117.71-0.29 (IA) using ANN and ANFIS, respectively. The obtained results have demonstrated that the proposed feature scaling method has been obtained very promising results in the prediction of SO2 concentration values.