Effect of the Relative Root-Mean-Square Error on Pavement Response

2007 ◽  
Author(s):  
Wael Alkasawneh ◽  
Ernie Pan ◽  
Roger Green
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Square ◽  
Pavement Response ◽  
Relative Root

scholarly journals BISAKAH MEMPEROLEH STATISTIK INDEKS HARGA KONSUMEN TINGKAT PROVINSI DI INDONESIA DENGAN KETELITIAN YANG LEBIH BAIK?

2020 ◽  
Vol 2019 (1) ◽  
pp. 297-306
Author(s):  
Andi Okta Fengki ◽  
Khairil Anwar Notodiputro ◽  
Kusman Sadik
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Small Area ◽  
Price Index ◽  
Small Area Estimation ◽  
Consumer Price Index ◽  
Mean Square ◽  
Area Estimation ◽  
Relative Root

Statistik indeks harga konsumen (IHK) atau consumer price index (CPI) juga dibutuhkan pada tingkat provinsi di era desentralisasi saat ini. Ketika IHK ingin diduga pada tingkat provinsi, permasalahan ukuran contoh kecil (small area) muncul karena survei untuk menghasilkan IHK ini di Indonesia dirancang untuk tingkat nasional. Akan tetapi, informasi dari statistik IHK 82 kota dapat membantu untuk menduga IHK provinsi. Metode pendugaan area kecil atau small area estimation (SAE) dapat diterapkan sebagai solusi untuk meningkatkan ketelitian hasil pendugaan langsung. Pada penelitian ini IHK provinsi diduga menggunakan model Fay-Herriot (FH). Hasilnya menunjukan bahwa model FH dapat menghasilkan statistik IHK provinsi dengan ketelitian yang lebih baik dari pendugaan langsung. Hal ini ditunjukan dengan nilai average relative root mean square error (ARRMSE) penduga FH IHK provinsi yang lebih kecil dari penduga langsungnya.

scholarly journals Estimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar

2019 ◽  
Vol 16 (17) ◽  
pp. 3457-3474 ◽  
Author(s):  
Marcos A. S. Scaranello ◽  
Michael Keller ◽  
Marcos Longo ◽  
Maiza N. dos-Santos ◽  
Veronika Leitold ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Dead Wood ◽  
Model Performance ◽  
Forest Carbon ◽  
Airborne Lidar ◽  
Mean Square ◽  
Improved Model ◽  
Relative Root

Abstract. Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, burned, or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of a number of disturbance events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably with independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and variables quantifying forest structure derived from airborne lidar highlight the opportunity to quantify this important but rarely measured component of forest carbon over large areas in tropical forests.

scholarly journals Estimation of Coarse Woody Debris Stocks in Intact and Degraded Forests in the Brazilian Amazon Using Airborne Lidar

2019 ◽  
Author(s):  
Marcos A. S. Scaranello ◽  
Michael Keller ◽  
Marcos Longo ◽  
Maiza N. dos-Santos ◽  
Veronika Leitold ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Dead Wood ◽  
Woody Debris ◽  
Model Performance ◽  
Forest Carbon ◽  
Airborne Lidar ◽  
Mean Square ◽  
Relative Root

Abstract. Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, and burned or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of number of disturbances events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably to independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and structural variables derived from airborne lidar highlight the opportunity to quantify this important, but rarely measured component of forest carbon over large areas in tropical forests.

scholarly journals Projeção do diâmetro de árvores de Hevea brasiliensis por meio de modelos mistos não lineares

2020 ◽  
Vol 12 (1) ◽  
pp. 31-41
Author(s):  
Sandro Da Silva Barros ◽  
Jeferson Pereira Martins Silva ◽  
Evandro Ferreira da Silva ◽  
Jeangelis Silva Santos ◽  
Adriano Ribeiro de Mendonça ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Hevea Brasiliensis ◽  
Mean Square ◽  
Espírito Santo ◽  
Espirito Santo ◽  
Relative Root

O estudo teve como objetivo avaliar a acurácia de modelos mistos não lineares na projeção do crescimento em diâmetro de árvores individuais de Hevea brasiliensis. A área de estudo está localizada no município de Linhares, Espírito Santo e possui área total de 784 m². As árvores estão plantadas no espaçamento de 2,0 x 2,0 m. As medições do diâmetro a 1,3 m do solo das árvores foram realizadas anualmente dos dois aos 14 anos de idade. Foram ajustados três modelos não lineares considerando efeitos fixos e efeitos aleatórios, sendo estes os modelos de Pienaar e Schiver, Mitscherlich e Chapman-Richards. A avaliação das estimativas geradas pelos modelos mistos e fixos foi realizada, tanto para o ajuste como para a projeção, com base no coeficiente de correlação (r), viés [V (%)], relative root mean square error [RMSE(%)]. O desempenho dos modelos de regressão quando considerado também efeitos aleatórios foi superior aos modelos de efeito fixo, sendo capaz de modelar a heterocedasticidade e a autocorrelação observada na análise gráfica dos ajustes dos modelos com efeito fixo.  O RMSE mais baixo dos modelos de efeito fixo foi 4,53% e para o efeito misto foi 3,71%. Quando comparado o valor de RMSE da projeção, o menor valor obtido com o modelo de efeito fixo foi de 22% e com efeito misto de 4,38%. A utilização de modelos de efeitos fixos e aleatórios resultou em ganhos significativos de acurácia, boa aplicação em dados agrupados e permitiu modelar a heterocedasticidade e a autocorrelação dos dados.

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 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%.

Comparison of Thresholding and Edge Detection Segmentation Techniques

2013 ◽  
Vol 860-863 ◽  
pp. 2783-2786
Author(s):  
Yu Bing Dong ◽  
Hai Yan Wang ◽  
Ming Jing Li
Keyword(s):  
Image Segmentation ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Experimental Results ◽  
Mean Square ◽  
Segmentation Method ◽  
Segmentation Algorithms

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

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