scholarly journals Estimating the total size of coronavirus epidemic in Algeria via different approaches

2020 ◽  
Author(s):  
Mohamed REZKI
Keyword(s):  
Root Mean Square Error ◽  
Regression Model ◽  
Time Lag ◽  
Logistic Growth ◽  
Peak Time ◽  
Mean Square ◽  
Total Size ◽  
Main Technique ◽  
Error Indicators ◽  
Over Time

In this paper, several techniques and models proposed the spread of coronavirus (Covid-19) and determines approximately the final number of coronavirus infected cases as well as infection point (peak time) in Algeria. To see the goodness of the predicting techniques, a comparative study was done by calculating error indicators such as Root-Mean-Square Error (RMSE) and the sum of squared estimate of errors (SSE). The main technique used in this study is the logistic growth regression model widely used in epidemiology. The results only relate to the two months from the beginning of the epidemic in Algeria, which should be readjusted by integrating the new data over time, because hazardous parameters like possible relaxations (decrease of vigilance or laxity of society) can affect these results and generally cause a time lag in the curve. Hence, a re-estimation of the curves is always requested.

scholarly journals Reference Evapotranspiration Prediction for Smart Irrigation

2020 ◽  
Vol 10 (1) ◽  
pp. 327-333
Keyword(s):  
Linear Regression ◽  
Root Mean Square Error ◽  
Regression Model ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Linear Regression Model ◽  
Mean Square ◽  
Crop Evapotranspiration ◽  
Crop Water ◽  
Critical Process

Irrigation is the most critical process for agriculture, but irrigation is the largest consumer of fresh water and causes the loss of large quantities because of the inaccuracy in crop water estimation. Our proposed system aims to improve irrigation management by estimating the amount of water needed by the crop accurately and reduces the number of meteorological parameters needed for such estimation. Detection of the reference crop evapotranspiration (ETo) is the most critical process in crop water estimation, that is considered through our proposed solution by implementing machine learning models using neural networks and linear regression to predict daily ETo using climate data like temperature, humidity, wind speed, and solar radiation. Comparing our system results with FAO-56 Penman-Monteith ET0 and cropwat8.0 software as benchmark, show that our proposed system is better than the linear regression model, in terms of determination coefficient (R^2)=.9677 and root mean square error(RMSE) =.1809, while the multiple linear regression model achieved determination coefficient (R^2)=.68 and root mean square error(RMSE) =3.01. Our system then used the predicted ETo and Crop coefficient (Kc) from FAO, to estimate crop evapotranspiration (ETc) for precision irrigation target.

scholarly journals Estimation of the final size of the second phase of the coronavirus COVID 19 epidemic by the logistic model

2020 ◽  
Author(s):  
Milan Batista
Keyword(s):  
South Korea ◽  
Regression Model ◽  
Logistic Model ◽  
Logistic Growth ◽  
Second Phase ◽  
Peak Time ◽  
Final Size ◽  
The World ◽  
Growth Regression

AbstractIn the note, the logistic growth regression model is used for the estimation of the final size and its peak time of the coronavirus epidemic in China, South Korea, and the rest of the World.

Sensitivity of batted-ball speed to swing speed models

2019 ◽  
Vol 233 (3) ◽  
pp. 416-423
Author(s):  
Derek Nevins ◽  
Lloyd Smith ◽  
Jeff Kensrud
Keyword(s):  
Root Mean Square Error ◽  
Regression Model ◽  
Gaussian Process ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Gaussian Process Regression ◽  
Field Measurements ◽  
Mean Square ◽  
Ball Speed ◽  
Swing Speed

In baseball and softball, batted-ball velocities depend strongly on bat speed prior to contact. The purpose of this study was to characterize the sensitivity of hit. ball speed to swing speed models. A pitching machine was used to project balls at varying speeds considered to be age appropriate for female fastpitch softball batters ranging from 9 to 24 years of age. Participants swung bats of varying moment of inertia and high-speed video was used to measure bat and ball kinematics for each hit. Several power law swing speed models were compared, including a generic model for all participants, age-specific models and participant-specific models. In addition, an exponential Gaussian process regression model was used. Estimates of batted-ball speeds were obtained by coupling the swing speed models to a model of the bat–ball collision. The root mean square error of model estimates ranged from 2.3 to 5.0 rad/s for swing speed models and 3.6 to 8.5 m/s for batted-ball speed estimates. The Gaussian process regression model performance was similar to the participant-specific models (swing speed root mean square error = 2.4 rad/s; batted-ball speed root mean square error = 3.7 m/s). The correlation of the Gaussian process regression model with field measurements was surprising given the limited number of inputs, which warrants further investigation. Agreement between all power law models and field measurements improved when the bat moment of inertia about its instantaneous center of rotation (rather than about the knob) was considered. This information will be of use to players and coaches to improve batting performance and bat selection.

scholarly journals ESTIMATION OF HEIGHT OF EUCALYPTUS TREES WITH NEUROEVOLUTION OF AUGMENTING TOPOLOGIES (NEAT)

2018 ◽  
Vol 41 (3) ◽  
Author(s):  
Daniel Henrique Breda Binoti ◽  
Paulo Junio Duarte ◽  
Mayra Luiza Marques da Silva ◽  
Gilson Fernandes da Silva ◽  
Helio Garcia Leite ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Regression Model ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Initial Density ◽  
Tree Height ◽  
Initial Population ◽  
Mean Square ◽  
Clonal Population ◽  
Total Tree

ABSTRACT The aim of this study was to evaluate the method of neuroevolution of augmenting topologies (NEAT) to adjust the weights and the topology of artificial neural networks (ANNs) in the estimation of tree height in a clonal population of eucalyptus, and compare with estimates obtained by a hypsometric regression model. To estimate the total tree height (Ht), the RNAs and the regression model, we used as variables a diameter of 1.3 m height (dbh) and the dominant height (Hd). The RNAs were adjusted and applied to the computer system NeuroForest, varying the size of the initial population (the genetic algorithm parameter) and the density of initial connections. Estimates of the total height of the trees obtained with the use of RNA and the regression model were evaluated based on the correlation coefficient, the percentage of errors scatter plot, the percentage frequency histogram of percentage errors, and the root mean square error (root mean square error - RMSE). Various settings which resulted in superior statistics to the hypsometric regression model were found. Connections had the highest correlation and the lowest RMSE% with a population size value of 300 and an initial density of 0.1 RNA. The NEAT methodology proved effective in estimating the height of trees in clonal population of eucalyptus.

scholarly journals Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

2021 ◽  
Vol 13 (9) ◽  
pp. 1630
Author(s):  
Yaohui Zhu ◽  
Guijun Yang ◽  
Hao Yang ◽  
Fa Zhao ◽  
Shaoyu Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Minimum Temperature ◽  
Price Regulation ◽  
Flowering Period ◽  
Mean Square ◽  
Prevention Measures ◽  
Agricultural Insurance

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

scholarly journals Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1020
Author(s):  
Yanqi Dong ◽  
Guangpeng Fan ◽  
Zhiwu Zhou ◽  
Jincheng Liu ◽  
Yongguo Wang ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Low Cost ◽  
Reference Value ◽  
Tree Height ◽  
Point Clouds ◽  
Tree Structure ◽  
Mean Square ◽  
Tree Model

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

scholarly journals Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1460
Author(s):  
Jinming Liu ◽  
Changhao Zeng ◽  
Na Wang ◽  
Jianfei Shi ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Root Mean Square Error ◽  
Least Squares ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mean Square ◽  
Biochemical Methane Potential ◽  
Methane Potential

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.

scholarly journals Quality of wood in the stands of poplar clones

2008 ◽  
Vol 54 (No. 1) ◽  
pp. 9-16
Author(s):  
R. Petráš ◽  
J. Mecko ◽  
V. Nociar
Keyword(s):  
Root Mean Square Error ◽  
Systematic Error ◽  
Mean Square Error ◽  
Statistical Tests ◽  
Mean Square ◽  
Poplar Clones ◽  
Mean Diameter ◽  
Empirical Material ◽  
Relative Root

The results obtained in research on the quality of raw timber by means of the structure of assortments for the stands of poplar clones Robusta and I-214 are presented in the paper. Models for an estimation of the structure of basic assortments of poplar stands were constructed separately for each clone in dependence on mean diameter, quality of stems, and damage to stems in the stand. The clone Robusta has higher proportions of higher-quality assortments than the clone I-214. The accuracy of models was determined on empirical material. It was confirmed by statistical tests that the models did not have a systematic error. The relative root mean-square error for main assortments of the clone I-214 is 15–27% and Robusta 13–24%.

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