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.

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 State Estimation and Forecasting of the Ski-Slope Model Using an Improved Shadowing Filter

2016 ◽  
Vol 26 (04) ◽  
pp. 1650056
Author(s):  
Auni Aslah Mat Daud
Keyword(s):  
Root Mean Square Error ◽  
State Estimation ◽  
Mean Square Error ◽  
Gradient Descent ◽  
Slight Modification ◽  
Mean Square ◽  
Step Size ◽  
Adaptive Step Size ◽  
Adaptive Step

In this paper, we present the application of the gradient descent of indeterminism (GDI) shadowing filter to a chaotic system, that is the ski-slope model. The paper focuses on the quality of the estimated states and their usability for forecasting. One main problem is that the existing GDI shadowing filter fails to provide stability to the convergence of the root mean square error and the last point error of the ski-slope model. Furthermore, there are unexpected cases in which the better state estimates give worse forecasts than the worse state estimates. We investigate these unexpected cases in particular and show how the presence of the humps contributes to them. However, the results show that the GDI shadowing filter can successfully be applied to the ski-slope model with only slight modification, that is, by introducing the adaptive step-size to ensure the convergence of indeterminism. We investigate its advantages over fixed step-size and how it can improve the performance of our shadowing filter.

scholarly journals Prediction of Chromatographic Elution Order of Analytical Mixtures Based on Quantitative Structure-Retention Relationships and Multi-Objective Optimization

Molecules ◽  
2020 ◽  
Vol 25 (13) ◽  
pp. 3085
Author(s):  
Petar Žuvela ◽  
J. Jay Liu ◽  
Ming Wah Wong ◽  
Tomasz Bączek
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Retention Time ◽  
Elution Order ◽  
Mean Square ◽  
Quantitative Structure ◽  
Multi Objective Optimization ◽  
Structure Retention ◽  
Relative Root ◽  
Quantitative Structure Retention Relationships

Prediction of the retention time from the molecular structure using quantitative structure-retention relationships is a powerful tool for the development of methods in reversed-phase HPLC. However, its fundamental limitation lies in the fact that low error in the prediction of the retention time does not necessarily guarantee a prediction of the elution order. Here, we propose a new method for the prediction of the elution order from quantitative structure-retention relationships using multi-objective optimization. Two case studies were evaluated: (i) separation of organic molecules in a Supelcosil LC-18 column, and (ii) separation of peptides in seven columns under varying conditions. Results have shown that, when compared to predictions based on the conventional model, the relative root mean square error of the elution order decreases by 48.84%, while the relative root mean square error of the retention time increases by 4.22% on average across both case studies. The predictive ability in terms of both retention time and elution order and the corresponding applicability domains were defined. The models were deemed stable and robust with few to no structural outliers.

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 Impacts of Human Interventions on Water Quality of Upper Ganga River Near Haridwar, Uttarakhand: A GIS-Based Analysis

2021 ◽  
Author(s):  
Pradip Kumar Maurya ◽  
Sk Ajim Ali ◽  
Ateeque Ahmad Ahmad ◽  
Krishna Kumar Maurya
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Spatial Correlation ◽  
Water Quality Index ◽  
Human Activities ◽  
Quality Index ◽  
Mean Square

The water quality of the river is becoming deteriorated due to human interference. It is essential to understand the relationship between human activities and land-use types to assess the water quality of a region. GIS has the latest tool for analyzing the spatial correlation. Land use land cover and change detection is the best illustration to show the human interactions on land features. The study assessed water quality index of upper Ganga River near Haridwar, Uttarakhand and spatially correlated them with changing land use to reach a logical conclusion. At the upper course of Ganga along 78 Km long from Kaudiyala to Bhogpur, water samples were collected from five stations. For water quality index the physicochemical parameters like pH, EC, DO, TDS, CaCO3-, CaCO3, Cl¯, Ca++, Mg++, Na+, K+, F-, Fe2+ were considered. The result of the spatial analysis was evaluated through error estimation and spatial correlation. The root mean square error between spatial land use and water quality index of selected sampling sites was estimated as 0.1443. The spatial correlation between land-use change and site-wise differences in water quality index has also shown a high positive correlation with R² = 0.8455. The degree of positive correlation and root mean square error has strongly indicated that the water quality of the river at the upper course of Ganga is highly impacted through human activities.

Metaheuristic data fitting methods to estimate Weibull parameters for wind speed data: a case study of Hasan Polatkan Airport

2021 ◽  
pp. 1-33
Author(s):  
A. Kaba ◽  
A. E. Suzer
Keyword(s):  
Wind Speed ◽  
Numerical Methods ◽  
Root Mean Square Error ◽  
Objective Function ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Statistical Tests ◽  
Mean Square ◽  
Flight Delays

ABSTRACT Flight delays may be decreased in a predictable way if the Weibull wind speed parameters of a runway, which are an important aspect of safety during the take-off and landing phases of aircraft, can be determined. One aim of this work is to determine the wind profile of Hasan Polatkan Airport (HPA) as a case study. Numerical methods for Weibull parameter determination perform better when the average wind speed estimation is the main objective. In this paper, a novel objective function that minimises the root-mean-square error by employing the cumulative distribution function is proposed based on the genetic algorithm and particle swarm optimisation. The results are compared with well-known numerical methods, such as maximum-likelihood estimation, the empirical method, the graphical method and the equivalent energy method, as well as the available objective function. Various statistical tests in the literature are applied, such as R2, Root-Mean-Square Error (RMSE) and $\chi$ 2. In addition, the Mean Absolute Error (MAE) and total elapsed time calculated using the algorithms are compared. According to the results of the statistical tests, the proposed methods outperform others, achieving scores as high as 0.9789 and 0.9996 for the R2 test, as low as 0.0058 and 0.0057 for the RMSE test, 0.0036 and 0.0045 for the MAE test and 3.53 × 10−5 and 3.50 × 10−5 for the $\chi$ 2 test. In addition, the determination of the wind speed characteristics at HPA show that low wind speed characteristics and regimes throughout the year offer safer take-off and landing schedules for target aircraft. The principle aim of this paper is to help establish the correct orientation of new runways at HPA and maximise the capacity of the airport by minimising flight delays, which represent a significant impediment to air traffic flow.

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.

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