Using pattern recognition for estimating cultivar coefficients of a crop simulation model

수공구조물의 위험도에 관한 불확실성을 검토하기 위하여 본 연구에서는 빈도해석을 통하여 추정되는 설계홍수량의 분산량을 고려한 불확실성 해석을 실시하였다. Gumbel 분포형을 기본 분포형으로 가정하였으며, 모멘트법, 최우도법, 확률가중모멘트법을 이용하여 각 매개변수 추정방법별로 추정된 설계홍수량에 대한 이론적인 분산량을 산정하였다. 이론적으로 유도한 분산량의 특성을 규명하기 위하며 다양한 표본크기와 설계연한, 비초과확률 및 변동계수조건에 대하여 Monte-Carlo 모의를 실시하고 각 매개변수 추정방법별 비교를 실시하였다. 그 결과 확률 가중 모멘트법을 사용한 경우 위험도에 대하여 상대적으로 가장 작은 상대편의 및 상대제곱근오차를 발생시키는 것으로 나타났으며, 최우도법의 경우에는 상대적으로 큰 표본자료에 대해서는 설계연한 및 비초과확률에 관계없이 작은 상대편의 및 상대제곱근오차를 발생시키는 것으로 나타났다. 또한 다양한 변동계수 조건은 상대편의 및 상대제곱근오차의 측면에서 고려하여 볼 때 거의 영향을 주지 않는 것으로 나타났다. The uncertainty of the risk of failure of hydraulic structures can be determined by estimating the variance of the risk of failure based on the methods of moments, probability weighted moments, and maximum likelihood assuming that the underlying model is the Gumbel distribution. In this paper, the variance of the risk of failure was derived. Monte Carlo simulation was peformed to verify the characteristics of the derived formulas for various sample size, design life, nonexceedance probability, and variation coefficient. As the results, PWM showed the smallest relative bias and root mean square error than the others while ML showed the smallest ones for relatively large sample siBes regardless of design life and nonexceedance probability. Also, it was found that variation coefficient does not effect on the relative bias and relative root mean square error.

HighlightsSoftware was developed for estimation of DSSAT CSM-CROPGRO-Soybean cultivar coefficients.Phenology-related coefficients were estimated based on observed phenological events.Growth-related cultivar coefficients were estimated based on time-series observations.Cultivar coefficients were optimized based on single- and multiple-experiment data sets.Abstract. The Decision Support System for Agrotechnology Transfer (DSSAT) is one of the most popular software solutions for predicting crop growth and yield while capturing the effects of management practices and interactions between the crop and the environment. Accurate estimation of the crop cultivar coefficients that govern in-season growth and development is critical for correct yield estimates. The manual cultivar coefficient estimation process is time-consuming and results in user-dependent, subjective optimums that are difficult to reproduce. Typically, end-of-season observations (point-based) are used for estimating dynamic in-season biomass accumulation rates. The objective of this study was to develop a time-series estimator (TSE) capable of using multiple in-season observations for estimating the coefficients that define in-season growth and biomass partitioning. Using the TSE, cultivar coefficients were estimated based on multiple in-season observations of leaf area index (LAI) and shoot, leaf, and grain dry matter weights. The cultivar coefficients were estimated from single- and multiple-treatment (seasons and locations) in-season observations. This was done for two cultivars for six management × environment combinations. Estimated multiple-treatment based cultivar coefficients were evaluated with an independent data set and compared to DSSAT standard (manual) coefficients and the cultivar coefficients estimated with the GLUE method. The average normalized root mean squared error (nRSME) for LAI and shoot, leaf, and grain weights was 26% lower for one cultivar and about the same for the other cultivar when compared to the DSSAT standard. Because GLUE uses end-of-season point-based cultivar coefficient estimation, the grain weight over time was underestimated in earlier phases and more accurate toward harvest. The TSE-estimated cultivar coefficients based on 346 in-season observations across multiple target variables and six experiments more accurately reflected in-season growth and grain weight without compromising final grain weight predictions. Keywords: . CROPGRO-Soybean, DSSAT, Genetic coefficients, Normalized root mean square error minimization, Time-seris observations.

Three-phase sampling can be a very effective design for the estimation of regional and national forest cover type frequencies. Simultaneous estimation of frequencies and sampling variances require estimation of a large number of parameters; often so many that consistency and robustness of results becomes an issue. A new stepwise estimation model, in which bias in phase one and two is corrected sequentially instead of simultaneously, requires fewer parameters. Simulated three-phase sampling tested the new model with 144 settings of sample sizes, the number of classes and classification accuracy. Relative mean absolute deviations and root mean square errors were, in most cases, about 8% lower with the stepwise method than with a simultaneous approach. Differences were a function of design parameters. Average expected relative root mean square errors, derived from the assumption of a Dirichlet distribution of cover-type frequencies, tracked the empirical root mean square errors obtained from repeated sampling with - 10%. Resampling results indicate that the relative bias of the most frequent cover types was slightly inflated by the stepwise method. For the least common cover type, the simultaneous method produced the largest relative bias.

Multi-environment evaluation of peanut lines by model simulation with the cultivar coefficients derived from a reduced set of observed field data

Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfil these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20% and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green leaf area index, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.

BackgroundMost of the objective and quantitative methods proposed for spasticity measurement are not suitable for clinical application, and methods for surface electromyography (sEMG) signal processing are mainly limited to the time-domain. This study aims to quantify muscle activity in the time–frequency domain, and develop a practical clinical method for the objective and reliable evaluation of the spasticity based on the Hilbert–Huang transform marginal spectrum entropy (HMSEN) and the root mean square (RMS) of sEMG signals.MethodsTwenty-six stroke patients with elbow flexor spasticity participated in the study. The subjects were tested at sitting position with the upper limb stretched towards the ground. The HMSEN of the sEMG signals obtained from the biceps brachii was employed to facilitate the stretch reflex onset (SRO) detection. Then, the difference between the RMS of a fixed-length sEMG signal obtained after the SRO and the RMS of a baseline sEMG signal, denoted as the RMS difference (RMSD), was employed to evaluate the spasticity level. The relations between Modified Ashworth Scale (MAS) scores and RMSD were investigated by Ordinal Logistic Regression (OLR). Goodness-of-fit of the OLR was obtained with Hosmer–Lemeshow test.ResultsThe HMSEN based method can precisely detect the SRO, and the RMSD scores and the MAS scores were fairly well related (test: χ2 = 8.8060, p = 0.2669; retest: χ2 = 1.9094, p = 0.9647). The prediction accuracies were 85% (test) and 77% (retest) when using RMSD for predicting MAS scores. In addition, the test–retest reliability was high, with an interclass correlation coefficient of 0.914 and a standard error of measurement of 1.137. Bland–Altman plots also indicated a small bias.ConclusionsThe proposed method is manually operated and easy to use, and the HMSEN based method is robust in detecting SRO in clinical settings. Hence, the method is applicable to clinical practice. The RMSD can assess spasticity in a quantitative way and provide greater resolution of spasticity levels compared to the MAS in clinical settings. These results demonstrate that the proposed method could be clinically more useful for the accurate and reliable assessment of spasticity and may be an alternative clinical measure to the MAS.

A technique is described to map surface UV-B irradiance (erythemal ultraviolet irradiance) for a section of the Antarctic coast bounded by latitudes 54°–69°S; 140°–160°E. Daily NOAA/AVHRR images have been acquired for this region over four consecutive austral spring, summer and autumn season (November–April), starting in 1990. A model developed by Green et al. is applied to estimate cloudless erythemal irradiances using cosine estimates from TOMS and surface albedo from NOAA/AVHRR. Cloudy irradiances are estimated as the product of the cloudless irradiance and a cloud transmittance derived from satellite imagery. When the model estimates were compared with surface measurements at Hobart, Tasmania and Davis, Antarctica, it was found that the root mean square (RMS) differences (model−measurements) for clear skies were equal to 5·7 per cent of the mean measured value, whereas for cloudy skies the RMS differences were 13·6 per cent of the mean for daily totals; and 4·2 per cent of the mean for monthly averages. Monthly statistics are presented as average monthly cloudless irradiance, average monthly cloudy irradiance and a ‘worst case’ monthly irradiance. Considerable interannual variability is observed in the ‘worst case’ monthly irradiance. Depletion by clouds is very significant and is larger than interannual variability in ozone depletion. The effect of clouds is minimum at 55°S and increases polewards to reach maximum values at the edge of the pack ice. Further to the south, cloud depletion decreases due to the moderating effect of the high surface albedo for snow and ice. © 1997 The Royal Meteorological Society.

An integrated orthognathic surgery system for virtual planning and image-guided transfer without intermediate splint

This work describes the surface temperature retrieval algorithm for the U.S. Department of Energy Multispectral Thermal Imager (MTI) satellite and its performance at three test sites. The MTI is a 15-band multispectral research and technology development satellite that provides high spatial resolution imagery of the earth's surface from a polar orbit. It has a capability for pixel-by-pixel retrieval of total atmospheric water vapor during the day. The surface temperature retrieval process uses atmospheric profiles of temperature and water vapor from numerical weather prediction data assimilation products available for the globe. The profiles of water vapor are scaled using the retrieved pixel-by-pixel water vapor during the day to improve atmospheric compensation within the image. The algorithm requires specification of a maximum emissivity or the acceptance of a default: here the maximum band emissivity is known and specified. The results of the algorithm are evaluated at three sites with a high degree of surface uniformity and known spectral emissivity: Lake Tahoe, NV/CA, Ivanpah Playa, NV/CA, and the Mauna Loa caldera, HI. At the first two sites, coincident measurements of surface temperature and emissivity are available. At Mauna Loa, surface emissivity measurements are available, but surface temperature measurements are not. Therefore, at Mauna Loa, the surface emissivity estimates derived from the surface temperature retrieval are compared with emissivity measurements. The results show that the surface temperature retrievals had a root mean square (RMS) difference with surface measurements of 0.6 K during the day and 1.0 K at night. This indicates, albeit weakly, that the use of pixel-by-pixel water vapor retrieved by the satellite improves the overall surface temperature retrievals. At Ivanpah Playa, all the data are daytime, and the surface temperature retrievals have an RMS difference with the in situ measurements of 0.8 K. The Mauna Loa emissivities derived from the surface temperature retrieval agreed excellently with in situ emissivity measurements. The approach taken to perform surface temperature retrieval, used operationally within the MTI research and development program, is seen to be simple and effective.

Efficient use of water in agro-ecosystems requires accurate quantification of actual evapotranspiration (ETa). The two-step approach, i.e., reference ET (ETref) adjusted with crop coefficients (Kc), is an extensively used approach to estimate ETa. Explicit combination equations, i.e., Penman-type combination-based equations and their derivatives (e.g., Penman-Monteith, ASCE-EWRI Penman-Monteith), have been widely tested and used. These equations rely on an assumption to remove the surface temperature from the calculation, and the validity of this assumption has been questioned as likely underestimating ET, particularly in semiarid and arid climates. Information on the validity of this assumption in subhumid regions is rare. Recursive combination methods, based on an equation proposed by Budyko, make no assumptions for the surface temperature, but they require iteration to arrive at a solution. The objective of this research was to compare ET calculated by explicit and recursive combination equations with measured values from a Bowen ratio energy balance system (BREBS) for an irrigated grassland. Results indicated that both equations were very sensitive to the value used for the surface resistance (rs). For equivalent values of rs, the explicit and recursive combination equations performed nearly equally as well in predicting BREBS-measured ET on both hourly and daily time steps. A surface resistance value of 40 s m-1 provided the best fit between both the Budyko and ASCE-EWRI equation-estimated ET and the measured ET values. Root mean square difference (RMSD) using rs = 40 s m-1 was 0.06 mm h-1, and model efficiency and R2 were both greater than 0.90. The same value (rs = 40 s m-1) also provided the best fit for nighttime ET estimates. Similarly, a daytime rs of 40 s m-1 provided the best fit between the ASCE-EWRI equation estimate and measured ET. On a daily time step, a sum-of-hourly approach provided better agreements with the BREBS-measured ET for both explicit (R2 = 0.95, RMSD = 0.57 mm d-1) and recursive (R2 = 0.96, RMSD = 0.57 mm d-1) combination methods than calculating ET using a daily average approach (explicit: R2 = 0.82, RMSD = 0.64 mm d-1; recursive: R2 = 0.78, RMSD = 0.72 mm d-1). While the recursive combination equation may potentially provide more accuracy under some conditions (i.e., arid and semiarid regions), it was no more accurate than the explicit equation under these experimental conditions.

A novel method for aircraft system identification is presented that is based on a new multivariate spline type; the multivariate multiplex B-spline. The multivariate multiplex B-spline is a generalization of the recently introduced tensor-simplex B-spline. Multivariate multiplex splines obtain similar or better approximation accuracy using less parameters (B-coefficients) than standard multivariate simplex B-splines which are currently used for aircraft system identification. The multiplex spline allows the user to incorporate a-priori knowledge of the modelled system in the definition of the model structure. In particular, while the standard simplex B-splines use a multi-dimensional triangulation in which all dimensions are coupled, the multiplex spline enables the user to decouple specific model dimensions based on expert knowledge of the system. The new method is used to approximate a 4-dimensional nonlinear quadrotor inflow dataset. The results show that the multiplex B-spline obtains a relative root mean square error of 0.672% using 1440 B-coefficients. This compares favorably to results obtained with the standard 4-dimensional simplex B-spline on the same dataset, which resulted in an relative root mean square error of 1.608% using 1540 B-coefficients.

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOS [ADD1_IRSTEA]Dynamiques spatiales d'anthropisation

Comparison of kinematics between Theia markerless and conventional marker-based gait analysis in clinical patients

Abstract. The global availability of satellite rainfall products (SRPs) at an increasingly high temporal and spatial resolution has made their exploitation in hydrological applications possible, especially in data-scarce regions. In this context, understanding how uncertainties transfer from SRPs to river discharge simulations, through the hydrological model, is a main research question. SRPs' accuracy is normally characterized by comparing them with ground observations via the calculation of categorical (e.g. threat score, false alarm ratio and probability of detection) and/or continuous (e.g. bias, root mean square error, Nash–Sutcliffe index, Kling–Gupta efficiency index and correlation coefficient) performance scores. However, whether these scores are informative about the associated performance in river discharge simulations (when the SRP is used as input to a hydrological model) is an under-discussed research topic. This study aims to relate the accuracy of different SRPs both in terms of rainfall and in terms of river discharge simulation. That is, the following research questions are addressed: is there any performance score that can be used to select the best performing rainfall product for river discharge simulation? Are multiple scores needed? And, which are these scores? To answer these questions, three SRPs, namely the Tropical Rainfall Measurement Mission (TRRM) Multi-satellite Precipitation Analysis (TMPA), the Climate Prediction Center MORPHing (CMORPH) algorithm and the SM2RAIN algorithm applied to the Advanced SCATterometer (ASCAT) soil moisture product (SM2RAIN–ASCAT) have been used as input into a lumped hydrologic model, “Modello Idrologico Semi-Distribuito in continuo” (MISDc), for 1318 basins over Europe with different physiographic characteristics. Results suggest that, among the continuous scores, the correlation coefficient and Kling–Gupta efficiency index are not reliable indices to select the best performing rainfall product for hydrological modelling, whereas bias and root mean square error seem more appropriate. In particular, by constraining the relative bias to absolute values lower than 0.2 and the relative root mean square error to values lower than 2, good hydrological performances (Kling–Gupta efficiency index on river discharge greater than 0.5) are ensured for almost 75 % of the basins fulfilling these criteria. Conversely, the categorical scores have not provided suitable information for addressing the SRP selection for hydrological modelling.

Spectrophotometric determination of celecoxib and tramadol in the new approved formulated dosage form using principle component regression assistive model