Wind Speed and Direction on Water Application Uniformity of a Mechanical Lateral-Move Irrigation System

2017 ◽  
Vol 33 (4) ◽  
pp. 543-549 ◽  
Author(s):  
Bernardo Gomes Nörenberg ◽  
Lessandro Coll Faria ◽  
Osvaldo Rettore Neto ◽  
Samuel Beskow ◽  
Alberto Colombo ◽  
...  
Keyword(s):  
Wind Speed ◽  
Cross Validation ◽  
Irrigation System ◽  
Field Tests ◽  
Sprinkler Irrigation ◽  
Support Tool ◽  
Validation Method ◽  
Wind Speeds ◽  
Statistical Measures ◽  
The Cross

Abstract. In order to develop models for representation of Christiansen’s Uniformity (CU) and Distribution Uniformity (DU) as a function of wind speed, 32 in-field tests evaluating a mechanical lateral-move irrigation system, used in rice production, were carried out in southern Rio Grande do Sul, Brazil. These tests were used to generate two third-order polynomial models for estimation of CU and DU, which were then validated based on a cross-validation approach. The generated models had their accuracy quantified by means of the following statistical measures: determination coefficient (R2), reliability and performance index (c), root mean square error (RMSE), and Nash-Sutcliffe coefficient (CNS). Wind direction had no significant influence on CU and DU. The CU values estimated from the cross-validation method were compared to those observed, resulting in R2 = 0.44, c = 0.53, RMSE = 1.82%, and CNS = 0.43. Likewise, DU values estimated from the cross-validation method were compared to the observed values, culminating in R2, c, RMSE, and CNS equal to 0.41%, 0.51%, 2.81% and 0.40%, respectively. The models developed in this study can be useful as a support tool for decision making when applying mechanical lateral-move irrigation systems, allowing estimation of CU and DU values with satisfactory precision for wind speeds less than 5.5 m s-1. Keywords: In-field tests, Rice, Sprinkler irrigation.

Calibration and Cross Validation of Global Ocean Wind Speed Based on Scatterometer Observations

2020 ◽  
Vol 37 (2) ◽  
pp. 279-297 ◽  
Author(s):  
Agustinus Ribal ◽  
Ian R. Young
Keyword(s):  
Wind Speed ◽  
Cross Validation ◽  
Regression Line ◽  
Total Duration ◽  
Global Ocean ◽  
High Wind ◽  
Wind Speeds ◽  
Buoy Data ◽  
Ocean Wind

AbstractGlobal ocean wind speed observed from seven different scatterometers, namely, ERS-1, ERS-2, QuikSCAT, MetOp-A, OceanSat-2, MetOp-B, and Rapid Scatterometer (RapidScat) were calibrated against National Data Buoy Center (NDBC) data to form a consistent long-term database of wind speed and direction. Each scatterometer was calibrated independently against NDBC buoy data and then cross validation between scatterometers was performed. The total duration of all scatterometer data is approximately 27 years, from 1992 until 2018. For calibration purposes, only buoys that are greater than 50 km offshore were used. Moreover, only scatterometer data within 50 km of the buoy and for which the overpass occurred within 30 min of the buoy recording data were considered as a “matchup.” To carry out the calibration, reduced major axis (RMA) regression has been applied where the regression minimizes the size of the triangle formed by the vertical and horizontal offsets of the data point from the regression line and the line itself. Differences between scatterometer and buoy data as a function of time were investigated for long-term stability. In addition, cross validation between scatterometers and independent altimeters was also performed for consistency. The performance of the scatterometers at high wind speeds was examined against buoy and platform measurements using quantile–quantile (Q–Q) plots. Where necessary, corrections were applied to ensure scatterometer data agreed with the in situ wind speed for high wind speeds. The resulting combined dataset is believed to be unique, representing the first long-duration multimission scatterometer dataset consistently calibrated, validated and quality controlled.

Identifying protein subcellular location with embedding features learned from networks

2020 ◽  
Vol 17 ◽  
Author(s):  
Hongwei Liu ◽  
Bin Hu ◽  
Lei Chen ◽  
Lin Lu
Keyword(s):  
Computational Methods ◽  
Protein Function ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Subcellular Location ◽  
Support Vector ◽  
Network Embedding ◽  
Validation Method ◽  
Protein Subcellular Location ◽  
The Cross

Background: Identification of protein subcellular location is an important problem because the subcellular location is highly related to protein function. It is fundamental to determine the locations with biology experiments. However, these experiments are of high costs and time-consuming. The alternative way to address such problem is to design effective computational methods. Objective: To date, several computational methods have been proposed in this regard. However, these methods mainly adopted the features derived from proteins themselves. On the other hand, with the development of network technique, several embedding algorithms have been proposed, which can encode nodes in the network into feature vectors. Such algorithms connected the network and traditional classification algorithms. Thus, they provided a new way to construct models for the prediction of protein subcellular location. Method: In this study, we analyzed features produced by three network embedding algorithms (DeepWalk, Node2vec and Mashup) that were applied on one or multiple protein networks. Obtained features were learned by one machine learning algorithm (support vector machine or random forest) to construct the model. The cross-validation method was adopted to evaluate all constructed models. Results: After evaluating models with the cross-validation method, embedding features yielded by Mashup on multiple networks were quite informative for predicting protein subcellular location. The model based on these features were superior to some classic models. Conclusion: Embedding features yielded by a proper and powerful network embedding algorithm were effective for building the model for prediction of protein subcellular location, providing new pipelines to build more efficient models.

scholarly journals Tropical Cyclone Wind Speed Retrieval from Dual-Polarization Sentinel-1 EW Mode Products

2020 ◽  
Vol 37 (9) ◽  
pp. 1713-1724
Author(s):  
Yuan Gao ◽  
Changlong Guan ◽  
Jian Sun ◽  
Lian Xie
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Noise Removal ◽  
Cross Polarization ◽  
High Wind ◽  
Dual Polarization ◽  
High Wind Speed ◽  
Wind Speeds ◽  
Hurricane Wind ◽  
The Cross

AbstractRecent studies indicate that the cross-polarization synthetic aperture radar (SAR) images have the ability of retrieving high wind speed on ocean surface without wind direction input. This study presents a new approach for tropical cyclone (TC) wind speed retrieval utilizing thermal-noise-removed Sentinel-1 dual-polarization (VV + VH) Extra-Wide Swath (EW) Mode products. Based on 20 images of 9 TCs observed in the 2016 and 2018 and SAR-collocated European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) data and the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division’s (HRD) Real-time Hurricane Wind Analysis System (H*Wind) data, a subswath-based geophysical model function (GMF) Sentinel-1 EW Mode Wind Speed Retrieval Model after Noise Removal (S1EW.NR) is developed and validated statistically. TC wind speed is retrieved by using the proposed GMF and the C-band model 5.N (CMOD5.N). The results show that the wind speeds retrieved by the S1EW.NR model are in good agreement with wind references up to 31 m s−1. The correlation coefficient, bias, and standard deviation between the retrieval results and reference wind speeds are 0.74, −0.11, and 3.54 m s−1, respectively. Comparison of the wind speeds retrieved from both channels suggests that the cross-polarized signal is more suitable for high–wind speed retrieval, indicating the promising capability of cross-polarization SAR for TC monitoring.

scholarly journals Estimation of Heat-Attributable Mortality Using the Cross-Validated Best Temperature Metric in Switzerland and South Korea

2021 ◽  
Vol 18 (12) ◽  
pp. 6413
Author(s):  
Jae Young Lee ◽  
Martin Röösli ◽  
Martina S. Ragettli
Keyword(s):  
South Korea ◽  
Minimum Temperature ◽  
Cross Validation ◽  
Specific Model ◽  
Apparent Temperature ◽  
Maximum Temperature ◽  
Attributable Mortality ◽  
Validation Method ◽  
Attributable Fractions ◽  
The Cross

This study presents a novel method for estimating the heat-attributable fractions (HAF) based on the cross-validated best temperature metric. We analyzed the association of eight temperature metrics (mean, maximum, minimum temperature, maximum temperature during daytime, minimum temperature during nighttime, and mean, maximum, and minimum apparent temperature) with mortality and performed the cross-validation method to select the best model in selected cities of Switzerland and South Korea from May to September of 1995–2015. It was observed that HAF estimated using different metrics varied by 2.69–4.09% in eight cities of Switzerland and by 0.61–0.90% in six cities of South Korea. Based on the cross-validation method, mean temperature was estimated to be the best metric, and it revealed that the HAF of Switzerland and South Korea were 3.29% and 0.72%, respectively. Furthermore, estimates of HAF were improved by selecting the best city-specific model for each city, that is, 3.34% for Switzerland and 0.78% for South Korea. To the best of our knowledge, this study is the first to observe the uncertainty of HAF estimation originated from the selection of temperature metric and to present the HAF estimation based on the cross-validation method.

scholarly journals Random Small Sample Prediction Model on Displacement of Extensive Deep Soil Excavation

2015 ◽  
Vol 9 (1) ◽  
pp. 107-114
Author(s):  
Zhou Shengquan ◽  
Zhao Xiaolong ◽  
Yao Zhaoming
Keyword(s):  
Prediction Model ◽  
Kernel Function ◽  
Cross Validation ◽  
Small Sample ◽  
Support Vector ◽  
Cauchy Kernel ◽  
Deep Soil ◽  
Deep Foundation ◽  
Validation Method ◽  
The Cross

In order to forecast the displacement of deep foundation pit support, this document proposes a new method which combines the cross validation method and supports vector machine (SVM) based on random small samples. Because the random small monitoring data are difficult to fit and forecast, the cross validation method and different kernel function of support vector machine algorithm arerepeatedly used to establish and optimize the displacement prediction model of underground continuous wall, and then uses validation samples to test the accuracy of the models. The results show that this method can meet the requirements of precision relatively well, and Cauchy kernel function is better than the other. In the aspect of accuracy of model fitting and prediction, this method has great advantages, which can be applied to practical engineering.

scholarly journals Development of an experiment-split method for benchmarking the generalization of a PTM site predictor: Lysine methylome as an example

2021 ◽  
Vol 17 (12) ◽  
pp. e1009682
Author(s):  
Guoyang Zou ◽  
Yang Zou ◽  
Chenglong Ma ◽  
Jiaojiao Zhao ◽  
Lei Li
Keyword(s):  
Experimental Data ◽  
Cross Validation ◽  
Post Translational Modification ◽  
Validation Method ◽  
Split Method ◽  
Split Test ◽  
Estimate Method ◽  
Practical Performance ◽  
The Cross ◽  
Different Sources

Many computational classifiers have been developed to predict different types of post-translational modification sites. Their performances are measured using cross-validation or independent test, in which experimental data from different sources are mixed and randomly split into training and test sets. However, the self-reported performances of most classifiers based on this measure are generally higher than their performances in the application of new experimental data. It suggests that the cross-validation method overestimates the generalization ability of a classifier. Here, we proposed a generalization estimate method, dubbed experiment-split test, where the experimental sources for the training set are different from those for the test set that simulate the data derived from a new experiment. We took the prediction of lysine methylome (Kme) as an example and developed a deep learning-based Kme site predictor (called DeepKme) with outstanding performance. We assessed the experiment-split test by comparing it with the cross-validation method. We found that the performance measured using the experiment-split test is lower than that measured in terms of cross-validation. As the test data of the experiment-split method were derived from an independent experimental source, this method could reflect the generalization of the predictor. Therefore, we believe that the experiment-split method can be applied to benchmark the practical performance of a given PTM model. DeepKme is free accessible via https://github.com/guoyangzou/DeepKme.

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