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 Characterization of Synthetic Non-Metallic Inclusions Consisting of TiN, Ti2O3, and Oxides of Al2O3 and MgO·Al2O3 Spinel Using Raman Spectroscopy

Metals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1549
Author(s):  
Francis Gyakwaa ◽  
Tuomas Alatarvas ◽  
Qifeng Shu ◽  
Matti Aula ◽  
Timo Fabritius
Keyword(s):  
Raman Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Raman Spectra ◽  
Model Performance ◽  
Qualitative Evaluation ◽  
Quantitative Prediction ◽  
Mean Square ◽  
Improved Model

Steel quality and properties can be affected by the formation of complex inclusions, including Ti-based inclusions such as TiN and Ti2O3 and oxides like Al2O3 and MgO·Al2O3 (MA). This study assessed the prospective use of Raman spectroscopy to characterize synthetic binary inclusion samples of TiN–Al2O3, TiN–MA, Ti2O3–MA, and Ti2O3–Al2O3 with varying phase fractions. The relative intensities of the Raman peaks were used for qualitative evaluation and linear regression calibration models were used for the quantitative prediction of individual phases. The model performance was evaluated with root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP). For the raw Raman spectra data, R2 values were between 0.48–0.98, the RMSECV values varied between 3.26–14.60 wt%, and the RMSEP ranged between 2.98–15.01 wt% for estimating the phases. The SNV Raman spectra data had estimated R2 values within 0.94–0.99 and RMSECV and RMSEP values ranged between 2.50–3.26 wt% and 2.80–9.01 wt%, respectively, showing improved model performance. The study shows that the specific phases of TiN, Al2O3, MA, and Ti2O3 in synthetic inclusion mixtures of TiN–(Al2O3 or MA) and Ti2O3–(Al2O3 or MA) could be characterized by the Raman spectroscopy.

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 Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature

2014 ◽  
Vol 7 (3) ◽  
pp. 1247-1250 ◽  
Author(s):  
T. Chai ◽  
R. R. Draxler
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Model Evaluation ◽  
Mean Absolute Error ◽  
Evaluation Studies ◽  
Model Performance ◽  
Absolute Error ◽  
Average Error ◽  
Mean Square

Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.

scholarly journals Root mean square error (RMSE) or mean absolute error (MAE)?

2014 ◽  
Vol 7 (1) ◽  
pp. 1525-1534 ◽  
Author(s):  
T. Chai ◽  
R. R. Draxler
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Model Evaluation ◽  
Mean Absolute Error ◽  
Evaluation Studies ◽  
Model Performance ◽  
Absolute Error ◽  
Average Error ◽  
Mean Square

Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error and thus the MAE would be a better metric for that purpose. Their paper has been widely cited and may have influenced many researchers in choosing MAE when presenting their model evaluation statistics. However, we contend that the proposed avoidance of RMSE and the use of MAE is not the solution to the problem. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric.

Pathogenic Escherichia coli and Salmonella Can Survive in Kimchi during Fermentation

2018 ◽  
Vol 81 (6) ◽  
pp. 942-946 ◽  
Author(s):  
YUKYUNG CHOI ◽  
SOOMIN LEE ◽  
HYUN JUNG KIM ◽  
HEEYOUNG LEE ◽  
SEJEONG KIM ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Model Performance ◽  
Mean Square ◽  
E Coli ◽  
Cell Counts ◽  
Primary Model ◽  
Increasing Temperature

ABSTRACT The survival of Escherichia coli and Salmonella strains during diced white radish kimchi fermentation was studied. Kimchi batches inoculated with the pathogens were fermented at 4, 15, and 25°C for 42 to 384 h. Cell counts of E. coli and Salmonella were enumerated on E. coli–coliform count plates and xylose lysine deoxycholate agar, respectively. Baranyi (primary model) and polynomial (secondary model) models, validated by root mean square error, were used to describe the kinetic behavior of the pathogens. In the primary model, both the death phase shoulder (E. coli: 208.18 to 8.25 h, 4 to 25°C; Salmonella: 79.91 to 0.97 h, 4 to 25°C) and bacterial cell counts (log CFU per gram per hour) decreased with increasing temperature (P < 0.05) (death rate: E. coli: −0.02 to −0.09, 4 to 25°C; Salmonella: −0.01 to −0.10, 4 to 25°C), the results being equally significant in the secondary model. The root mean square error (0.480 to 0.485) showed that the model performance was good. The fermentation temperature and time are the critical factors that control pathogenic E. coli and Salmonella in kimchi.

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 Simulation of phenology, total nutrient uptake and grain yield of wheat under different irrigation and nitrogen application managements in Hisar, India using the DSSAT-CSM-CERES-Wheat model

2018 ◽  
Author(s):  
Mukesh Kumar ◽  
R.K. Pannu ◽  
Bhagat Singh
Keyword(s):  
Root Mean Square Error ◽  
Grain Yield ◽  
Nutrient Uptake ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Average Error ◽  
Mean Square ◽  
Nitrogen Levels

The purpose of this study was the calibration and validation of DSSAT-CSM-CERES-Wheat model (v4.5) for wheat in Hisar conditions. The DSSAT-CSM-CERES-Wheat model was calibrated with the field experimental data of rabi 2010-11 having 3 levels of irrigation (I1-one irrigation at crown root initiation [CRI], I2- two irrigations at CRI and heading and I3- four irrigations at CRI, late tillering, heading and milking) and 5 nitrogen levels (0, 50, 100, 150 and 200 kg N/ha) and validated with data of experiment rabi 2011-12 conducted at Hisar (29°10’ N and 75°46’ E). The model performance was evaluated using average error (Bias), root mean square error (RMSE), normalized root mean square error (nRMSE), index of agreement (d-stat) and coefficient of determination (r2), and it was observed that DSSAT-CSM-CERES-Wheat model was able to predict the phenology, total nutrient uptake and grain yield of wheat with reasonably good accuracy. The simulated results were within the permissible limit of the error (error % less than ±15).

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