scholarly journals Improving the complementary methods to estimate evapotranspiration under diverse climatic and physical conditions

2014 ◽  
Vol 18 (6) ◽  
pp. 2049-2064 ◽  
Author(s):  
F. M. Anayah ◽  
J. J. Kaluarachchi
Keyword(s):  
Meteorological Data ◽  
Large River ◽  
Single Step ◽  
Coefficient Of Determination ◽  
Universal Method ◽  
Complementary Methods ◽  
Data Intensive ◽  
Physical Conditions ◽  
Proposed Model ◽  
Using Data

Abstract. Reliable estimation of evapotranspiration (ET) is important for the purpose of water resources planning and management. Complementary methods, including complementary relationship areal evapotranspiration (CRAE), advection aridity (AA) and Granger and Gray (GG), have been used to estimate ET because these methods are simple and practical in estimating regional ET using meteorological data only. However, prior studies have found limitations in these methods especially in contrasting climates. This study aims to develop a calibration-free universal method using the complementary relationships to compute regional ET in contrasting climatic and physical conditions with meteorological data only. The proposed methodology consists of a systematic sensitivity analysis using the existing complementary methods. This work used 34 global FLUXNET sites where eddy covariance (EC) fluxes of ET are available for validation. A total of 33 alternative model variations from the original complementary methods were proposed. Further analysis using statistical methods and simplified climatic class definitions produced one distinctly improved GG-model-based alternative. The proposed model produced a single-step ET formulation with results equal to or better than the recent studies using data-intensive, classical methods. Average root mean square error (RMSE), mean absolute bias (BIAS) and R2 (coefficient of determination) across 34 global sites were 20.57 mm month−1, 10.55 mm month−1 and 0.64, respectively. The proposed model showed a step forward toward predicting ET in large river basins with limited data and requiring no calibration.

scholarly journals Improving the complementary methods to estimate evapotranspiration under diverse climatic and physical conditions

2013 ◽  
Vol 10 (11) ◽  
pp. 13595-13634
Author(s):  
F. M. Anayah ◽  
J. J. Kaluarachchi
Keyword(s):  
Meteorological Data ◽  
Large River ◽  
Single Step ◽  
Complementary Methods ◽  
Data Intensive ◽  
Physical Conditions ◽  
Proposed Model ◽  
Absolute Bias ◽  
Using Data ◽  
Resources Planning

Abstract. Reliable estimation of evapotranspiration (ET) is important for the purpose of water resources planning and management. Complementary methods, including Complementary Relationship Areal Evapotranspiration (CRAE), Advection–Aridity (AA) and Granger and Gray (GG), have been used to estimate ET because these methods are simple and practical in estimating regional ET using meteorological data only. However, prior studies have found limitations in these methods especially in contrasting climates. This study aims to develop a calibration-free universal model using the complementary relationships to compute regional ET in contrasting climatic and physical conditions with meteorological data only. The proposed methodology consists of a systematic sensitivity analysis using the existing complementary methods. This work used 34 global FLUXNET sites where eddy covariance (EC) fluxes of ET are available for validation. A total of 33 alternative model variations from the original complementary methods were proposed. Further analysis using statistical methods and simplified climatic class definitions produced one distinctly improved GG-model based alternative. The proposed model produced a single-step ET formulation with results equal or better than the recent studies using data-intensive, classical methods. Average root mean square error (RMSE), mean absolute bias (BIAS) and R2 values across 34 global sites were 20.57 mm month−1, 10.55 mm month−1 and 0.64, respectively. The proposed model showed a step forward toward predicting ET in large river basins with limited data and requiring no calibration.

A Regional Zenith Tropospheric Correction Model Based on the Guangxi CORS Network

2013 ◽  
Vol 353-356 ◽  
pp. 3438-3443
Author(s):  
Li Long Liu ◽  
Liang Ke Huang ◽  
Teng Xu Zhang ◽  
Miao Zhou ◽  
Chao Long Yao
Keyword(s):  
Interpolation Method ◽  
Meteorological Data ◽  
Correction Model ◽  
New Model ◽  
Model Based ◽  
Proposed Model ◽  
New Models ◽  
Using Data ◽  
The Relationship ◽  
Tropospheric Delays

In this paper, the relationship between zenith tropospheric delays and the altitude of stations is analyzed using the EGNOS tropospheric correction model. The new model (EHT model) is proposed for estimating zenith tropospheric delays from regional CORS data without meteorological data. The proposed model is compared with the direct interpolation method and the remove-restore method using data from Guangxi CORS. The results show that the new models significantly improve the calculated precision.

scholarly journals Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

2021 ◽  
Vol 13 (7) ◽  
pp. 3727
Author(s):  
Fatema Rahimi ◽  
Abolghasem Sadeghi-Niaraki ◽  
Mostafa Ghodousi ◽  
Soo-Mi Choi
Keyword(s):  
Regression Analysis ◽  
Mean Squared Error ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Coefficient Of Determination ◽  
Temporal Modeling ◽  
Location Data ◽  
Time Period ◽  
Proposed Model

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

scholarly journals Emerging issues in genomic selection

2021 ◽  
Author(s):  
I Misztal ◽  
I Aguilar ◽  
D Lourenco ◽  
L Ma ◽  
J Steibel ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Sparse Matrices ◽  
Large Datasets ◽  
Single Step ◽  
Genomic Information ◽  
Long Term Effects ◽  
Additive Variance ◽  
Recent Developments ◽  
Emerging Issues ◽  
Using Data

Abstract Genomic selection is now practiced successfully across many species. However, many questions remain such as long-term effects, estimations of genomic parameters, robustness of GWAS with small and large datasets, and stability of genomic predictions. This study summarizes presentations from at the 2020 ASAS symposium. The focus of many studies until now is on linkage disequilibrium (LD) between two loci. Ignoring higher level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWAS studies using small genomic datasets frequently find many marker-trait associations whereas studies using much bigger datasets find only a few. Most current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit computation of p-values from GBLUP, where models can be arbitrarily complex but restricted to genotyped animals only, and to single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as one SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. While many issues in genomic selection have been solved, many new issues that require additional research continue to surface.

scholarly journals UAV Image Multi-Labeling with Data-Efficient Transformers

2021 ◽  
Vol 11 (9) ◽  
pp. 3974
Author(s):  
Laila Bashmal ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Haikel Alhichri ◽  
Naif Al Ajlan
Keyword(s):  
Data Augmentation ◽  
Feature Representation ◽  
Aerial Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Proposed Model ◽  
Class Labels ◽  
Using Data ◽  
Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

scholarly journals Biosensing Membrane Base on Ferulic Acid and Glucose Oxidase for an Amperometric Glucose Biosensor

Molecules ◽  
2021 ◽  
Vol 26 (12) ◽  
pp. 3757
Author(s):  
Gabriela Valdés-Ramírez ◽  
Laura Galicia
Keyword(s):  
Ferulic Acid ◽  
Glucose Oxidase ◽  
Aqueous Media ◽  
Glucose Biosensor ◽  
Single Step ◽  
Coefficient Of Determination ◽  
The Novel ◽  
Carbon Paste ◽  
Glucose Response ◽  
Glucose Biosensors

A biosensing membrane base on ferulic acid and glucose oxidase is synthesized onto a carbon paste electrode by electropolymerization via cyclic voltammetry in aqueous media at neutral pH at a single step. The developed biosensors exhibit a linear response from 0.082 to 34 mM glucose concentration, with a coefficient of determination R2 equal to 0.997. The biosensors display a sensitivity of 1.1 μAmM−1 cm−2, a detection limit of 0.025 mM, and 0.082 mM as glucose quantification limit. The studies reveal stable, repeatable, and reproducible biosensors response. The results indicate that the novel poly-ferulic acid membrane synthesized by electropolymerization is a promising method for glucose oxidase immobilization towards the development of glucose biosensors. The developed glucose biosensors exhibit a broader linear glucose response than other polymer-based glucose biosensors.

scholarly journals Comparative Study of Ground Measured, Satellite-Derived, and Estimated Global Solar Radiation Data in Nigeria

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Boluwaji M. Olomiyesan ◽  
Onyedi D. Oyedum
Keyword(s):  
Solar Radiation ◽  
Comparative Study ◽  
Global Radiation ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Radiation Data ◽  
Proposed Model

In this study, the performance of three global solar radiation models and the accuracy of global solar radiation data derived from three sources were compared. Twenty-two years (1984–2005) of surface meteorological data consisting of monthly mean daily sunshine duration, minimum and maximum temperatures, and global solar radiation collected from the Nigerian Meteorological (NIMET) Agency, Oshodi, Lagos, and the National Aeronautics Space Agency (NASA) for three locations in North-Western region of Nigeria were used. A new model incorporating Garcia model into Angstrom-Prescott model was proposed for estimating global radiation in Nigeria. The performances of the models used were determined by using mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), and coefficient of determination (R2). Based on the statistical error indices, the proposed model was found to have the best accuracy with the least RMSE values (0.376 for Sokoto, 0.463 for Kaduna, and 0.449 for Kano) and highest coefficient of determination, R2 values of 0.922, 0.938, and 0.961 for Sokoto, Kano, and Kaduna, respectively. Also, the comparative study result indicates that the estimated global radiation from the proposed model has a better error range and fits the ground measured data better than the satellite-derived data.

Multiparameter Real-World System Identification using Iterative Residual Tuning

2021 ◽  
pp. 1-12
Author(s):  
Adam Allevato ◽  
Mitch W Pryor ◽  
Andrea L. Thomaz
Keyword(s):  
System Identification ◽  
Robot Control ◽  
Tracking Error ◽  
Nonlinear System Identification ◽  
Visual Observation ◽  
Free System ◽  
Proposed Model ◽  
Derivative Free ◽  
Identification Technique ◽  
Using Data

Abstract In this work we consider the problem of nonlinear system identification, using data to learn multiple and often coupled parameters that allow a simulator to more accurately model a physical system or mechanism and close the so-called reality gap for more accurate robot control. Our approach uses iterative residual tuning (IRT), a recently-developed derivative-free system identification technique that utilizes neural networks and visual observation to estimate parameter differences between a proposed model and a target model. We develop several modifications to the basic IRT approach and apply it to the system identification of a 5-parameter model of a marble rolling in a robot-controlled labyrinth game mechanism. We validate our technique both in simulation—where we outperform two baselines—and on a real system, where we achieve marble tracking error of 4% after just 5 optimization iterations.

scholarly journals A simple model for the estimation of the number of fatalities due to floods in central Europe

2014 ◽  
Vol 14 (7) ◽  
pp. 1663-1676 ◽  
Author(s):  
M. Brazdova ◽  
J. Riha
Keyword(s):  
Central Europe ◽  
Pairwise Comparison ◽  
The Czech Republic ◽  
Factors Affecting ◽  
Hazard Exposure ◽  
Proposed Model ◽  
River Floods ◽  
Using Data ◽  
Human Losses ◽  
Significant Factors

Abstract. In this paper a model for the estimation of the number of potential fatalities is proposed based on data from 19 past floods in central Europe. First, the factors contributing to human losses during river floods are listed and assigned to the main risk factors: hazard – exposure – vulnerability. The order of significance of individual factors has been compiled by pairwise comparison based on experience with real flood events. A comparison with factors used in existing models for the estimation of fatalities during floods shows good agreement with the significant factors identified in this study. The most significant factors affecting the number of human losses in floods have been aggregated into three groups and subjected to correlation analysis. A close-fitting regression dependence is proposed for the estimation of loss of life and calibrated using data from selected real floods in central Europe. The application of the proposed model for the estimation of fatalities due to river floods is shown via a flood risk assessment for the locality of Krnov in the Czech Republic.

scholarly journals Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

2020 ◽  
Vol 9 (3) ◽  
pp. 674 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Ahmed A. Ewees ◽  
Hong Fan ◽  
Mohamed Abd El Aziz
Keyword(s):  
Relative Error ◽  
Computing Time ◽  
World Health ◽  
Coefficient Of Determination ◽  
Flower Pollination Algorithm ◽  
Percentage Error ◽  
Anfis Model ◽  
Local Optima ◽  
Inference System ◽  
Proposed Model

In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination ( R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

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