scholarly journals Global Mean Sea Surface Height Estimated from Spaceborne Cyclone-GNSS Reflectometry

2020 ◽  
Vol 12 (3) ◽  
pp. 356 ◽  
Author(s):  
Hui Qiu ◽  
Shuanggen Jin
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
Absolute Error ◽  
Satellite System ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Mean Square ◽  
Mean Sea Surface

Mean sea surface height (MSSH) is an important parameter, which plays an important role in the analysis of the geoid gap and the prediction of ocean dynamics. Traditional measurement methods, such as the buoy and ship survey, have a small cover area, sparse data, and high cost. Recently, the Global Navigation Satellite System-Reflectometry (GNSS-R) and the spaceborne Cyclone Global Navigation Satellite System (CYGNSS) mission, which were launched on 15 December 2016, have provided a new opportunity to estimate MSSH with all-weather, global coverage, high spatial-temporal resolution, rich signal sources, and strong concealability. In this paper, the global MSSH was estimated by using the relationship between the waveform characteristics of the delay waveform (DM) obtained by the delay Doppler map (DDM) of CYGNSS data, which was validated by satellite altimetry. Compared with the altimetry CNES_CLS2015 product provided by AVISO, the mean absolute error was 1.33 m, the root mean square error was 2.26 m, and the correlation coefficient was 0.97. Compared with the sea surface height model DTU10, the mean absolute error was 1.20 m, the root mean square error was 2.15 m, and the correlation coefficient was 0.97. Furthermore, the sea surface height obtained from CYGNSS was consistent with Jason-2′s results by the average absolute error of 2.63 m, a root mean square error ( RMSE ) of 3.56 m and, a correlation coefficient ( R ) of 0.95.

scholarly journals An Appraisal of Dynamic Bayesian Model Averaging-based Merged Multi-Satellite Precipitation Datasets Over Complex Topography and the Diverse Climate of Pakistan

2019 ◽  
Vol 12 (1) ◽  
pp. 10 ◽  
Author(s):  
Khalil Ur Rahman ◽  
Songhao Shang ◽  
Muhammad Shahid ◽  
Yeqiang Wen
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Model Averaging ◽  
Absolute Error ◽  
Efficiency Score ◽  
Mean Square ◽  
Satellite Precipitation

Merging satellite precipitation products tends to reduce the errors associated with individual satellite precipitation products and has higher potential for hydrological applications. The current study evaluates the performance of merged multi-satellite precipitation dataset (daily temporal and 0.25° spatial resolution) developed using the Dynamic Bayesian Model Averaging algorithm across four different climate regions, i.e., glacial, humid, arid and hyper-arid regions, of Pakistan during 2000–2015. Four extensively evaluated SPPs over Pakistan, i.e., Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Climate Prediction Center MORPHing technique (CMORPH), and Era-Interim, are used to develop the merged multi-satellite precipitation dataset. Six statistical indices, including Mean Bias Error, Mean Absolute Error, Root Mean Square Error, Correlation Coefficient, Kling-Gupta efficiency, and Theil’s U coefficient, are used to evaluate the performance of merged multi-satellite precipitation dataset over 102 ground precipitation gauges both spatially and temporally. Moreover, the ensemble spread score and standard deviation are also used to depict the spread and variation of precipitation of merged multi-satellite precipitation dataset. Skill scores for all statistical indices are also included in the analyses, which shows improvement of merged multi-satellite precipitation dataset against Simple Model Averaging. The results revealed that DBMA-MSPD assigned higher weights to TMPA (0.32) and PERSIANN-CDR (0.27). TMPA presented higher skills in glacial and humid regions with average weights of 0.32 and 0.37 as compared to PERSIANN-CDR of 0.27 and 0.25, respectively. TMPA and Era-Interim depicted higher skills during pre-monsoon and monsoon seasons, with average weights of 0.31 and 0.52 (TMPA) and 0.25 and 0.21 (Era-Interim), respectively. Merged multi-satellite precipitation dataset overestimated precipitation in glacial/humid regions and showed poor performance, with the poorest values of mean absolute error (2.69 mm/day), root mean square error (11.96 mm/day), correlation coefficient (0.41), Kling-Gupta efficiency score (0.33) and Theil’s U (0.70) at some stations in glacial/humid regions. Higher performance is observed in hyper-arid region, with the best values of 0.71 mm/day, 1.72 mm/day, 0.84, 0.93, and 0.37 for mean absolute error, root mean square error, correlation coefficient, Kling-Gupta Efficiency score, and Theil’s U, respectively. Merged multi-Satellite Precipitation Dataset demonstrated significant improvements as compared to TMPA across all climate regions with average improvements of 45.26% (mean bias error), 30.99% (mean absolute error), 30.1% (root mean square error), 11.34% (correlation coefficient), 9.53% (Kling-Gupta efficiency score) and 8.86% (Theil’s U). The ensemble spread and variation of DBMA-MSPD calculated using ensemble spread score and standard deviation demonstrates high spread (11.38 mm/day) and variation (12.58 mm/day) during monsoon season in the humid and glacial regions, respectively. Moreover, the improvements of DBMA-MSPD quantified against fixed weight SMA-MSPD reveals supremacy of DBMA-MSPD, higher improvements (40–50%) in glacial and humid regions.

Metabolic Rate in Adolescent Athletes: The Development and Validation of New Equations, and Comparison to Previous Models

2020 ◽  
Vol 30 (4) ◽  
pp. 249-257
Author(s):  
Reid J. Reale ◽  
Timothy J. Roberts ◽  
Khalil A. Lee ◽  
Justina L. Bonsignore ◽  
Melissa L. Anderson
Keyword(s):  
Root Mean Square Error ◽  
Metabolic Rate ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Compartment Model ◽  
Absolute Error ◽  
Mean Square ◽  
Adolescent Athletes ◽  
Two Compartment Model

We sought to assess the accuracy of current or developing new prediction equations for resting metabolic rate (RMR) in adolescent athletes. RMR was assessed via indirect calorimetry, alongside known predictors (body composition via dual-energy X-ray absorptiometry, height, age, and sex) and hypothesized predictors (race and maturation status assessed via years to peak height velocity), in a diverse cohort of adolescent athletes (n = 126, 77% male, body mass = 72.8 ± 16.6 kg, height = 176.2 ± 10.5 cm, age = 16.5 ± 1.4 years). Predictive equations were produced and cross-validated using repeated k-fold cross-validation by stepwise multiple linear regression (10 folds, 100 repeats). Performance of the developed equations was compared with several published equations. Seven of the eight published equations examined performed poorly, underestimating RMR in >75% to >90% of cases. Root mean square error of the six equations ranged from 176 to 373, mean absolute error ranged from 115 to 373 kcal, and mean absolute error SD ranged from 103 to 185 kcal. Only the Schofield equation performed reasonably well, underestimating RMR in 51% of cases. A one- and two-compartment model were developed, both r2 of .83, root mean square error of 147, and mean absolute error of 114 ± 26 and 117 ± 25 kcal for the one- and two-compartment model, respectively. Based on the models’ performance, as well as visual inspection of residual plots, the following model predicts RMR in adolescent athletes with better precision than previous models; RMR = 11.1 × body mass (kg) + 8.4 × height (cm) − (340 male or 537 female).

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 Utilizing Artificial Intelligence to Predict the Superplasticizer Demand of Self-Consolidating Concrete Incorporating Pumice, Slag, and Fly Ash Powders

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 6792
Author(s):  
Jing Liu ◽  
Masoud Mohammadi ◽  
Yubao Zhan ◽  
Pengqiang Zheng ◽  
Maria Rashidi ◽  
...  
Keyword(s):  
Fly Ash ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
Mix Design ◽  
Mean Square ◽  
Fresh Properties ◽  
Self Consolidating Concrete ◽  
Significant Parameter

Self-consolidating concrete (SCC) is a well-known type of concrete, which has been employed in different structural applications due to providing desirable properties. Different studies have been performed to obtain a sustainable mix design and enhance the fresh properties of SCC. In this study, an adaptive neuro-fuzzy inference system (ANFIS) algorithm is developed to predict the superplasticizer (SP) demand and select the most significant parameter of the fresh properties of optimum mix design. For this purpose, a comprehensive database consisting of verified test results of SCC incorporating cement replacement powders including pumice, slag, and fly ash (FA) has been employed. In this regard, at first, fresh properties tests including the J-ring, V-funnel, U-box, and different time interval slump values were considered to collect the datasets. At the second stage, five models of ANFIS were adjusted and the most precise method for predicting the SP demand was identified. The correlation coefficient (R2), Pearson’s correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), mean absolute error (MAE), and Wilmot’s index of agreement (WI) were used as the measures of precision. Later, the most effective parameters on the prediction of SP demand were evaluated by the developed ANFIS. Based on the analytical results, the employed algorithm was successfully able to predict the SP demand of SCC with high accuracy. Finally, it was deduced that the V-funnel test is the most reliable method for estimating the SP demand value and a significant parameter for SCC mix design as it led to the lowest training root mean square error (RMSE) compared to other non-destructive testing methods.

scholarly journals Multi-grained cascade forest for effluent quality prediction of papermaking wastewater treatment processes

2020 ◽  
Vol 81 (5) ◽  
pp. 1090-1098
Author(s):  
Chen Xin ◽  
Xueqing Shi ◽  
Dongsheng Wang ◽  
Chong Yang ◽  
Qian Li ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
Environmental Conservation ◽  
Support Vector ◽  
Mean Square ◽  
A Value ◽  
Conventional Models ◽  
Papermaking Wastewater

Abstract The real time estimation of effluent indices of papermaking wastewater is vital to environmental conservation. Ensemble methods have significant advantages over conventional single models in terms of prediction accuracy. As an ensemble method, multi-grained cascade forest (gcForest) is implemented for the prediction of wastewater indices. Compared with the conventional modeling methods including partial least squares, support vector regression, and artificial neural networks, the gcForest model shows prediction superiority for effluent suspended solid (SSeff) and effluent chemical oxygen demand (CODeff). In terms of SSeff, gcForest achieves the highest correlation coefficient with a value of 0.86 and the lowest root-mean-square error (RMSE) value of 0.41. In comparison with the conventional models, the RMSE value using gcForest is reduced by approximately 46.05% to 50.60%. In terms of CODeff, gcForest achieves the highest correlation coefficient with a value of 0.83 and the lowest root-mean-square error value of 4.05. In comparison with the conventional models, the RMSE value using gcForest is reduced by approximately 10.60% to 18.51%.

scholarly journals AZƏRBAYCANDA COVID-19 KORONAVİRUSUNA YOLUXMANIN PROQNOZLAŞDIRILMASI ÜÇÜN ARIMA MODELLƏRİNİN TƏTBİQİ

2021 ◽  
Vol 12 (1) ◽  
pp. 95-104
Author(s):  
Firəngiz Sadıyeva ◽  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Moving Average ◽  
Absolute Error ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average

Məqalədə COVID-19 pandemiyasını proqnozlaşdırmaq üçün avtoreqressiv inteqrasiya edilmiş hərəkətli ortalama (ing. ARIMA. Autoregressive İntegrated Moving Average) modeli təklif edilmişdir. COVID-19 dünyada sürətlə yayılan və hazırda davam edən yeni növ pandemiyadır. Son dövrlərdə pandemiyaya yoluxanların sayı Azərbaycanda rekord həddə çatmışdır. Məhz bu səbəbdən COVID-19 pandemiyasının proqnozu məsələsinə baxılmışdır və bir neçə ayı əhatə edən real verilənlərlə eksperimentlərdə təklif edilmiş ARIMA modelinin COVID-19 zaman sıralarının proqnozlaşdırılması üçün müxtəlif parametrlərlə tətbiq edilmişdir. Verilənlər dedikdə, 22.01.2020 – 22.10.2020 tarixləri arasında Azərbaycan Respublikasının Səhiyyə Nazirliyi (www.sehiyye.gov.az) tərəfindən rəsmi olaraq qeydiyyata alınan gündəlik yoluxma hallarının sayı nəzərdə tutulur. Bu verilənlərdən istifadə etməklə, növbəti zaman aralığında ölkəmizdə baş verəcək yoluxma halları proqnoz edilmişdir. Bunun üçün ARIMA modelinə müxtəlif parametrlər verilmiş və uyğun olaraq hər bir modelin səhv dərəcəsi qiymətləndirilmişdir. Səhvin qiymətləndirilməsi üçün MAPE (Mean Absolute Persentace Error), MAE (Mean Absolute Error) və RMSE (Root Mean Square Error) funksiyaları istifadə edilib. Müqayisələr nəticəsində ən uyğun model seçilmişdir. Alınmış nəticələr ölkəmizdə pandemiya dövründə həm səhiyyə sistemi, həm də adi vətəndaşlar üçün vacib amildir. Əldə edilmiş nəticələr statistik metodların koronavirusa aid qeyri-stasionar zaman sıralarının proqnozlaşdırılmasının digər məsələlərə tətbiqində də məhsuldar ola biləcəyini təsdiqləyir.

scholarly journals Wavelet-Gaussian Process Regression Model for Regression Daily Solar Radiation in Ghardaia, Algeria

2021 ◽  
Vol 20 (2) ◽  
pp. 113-119
Author(s):  
Khaled Ferkous ◽  
Farouk Chellali ◽  
Abdalah Kouzou ◽  
Belgacem Bekkar
Keyword(s):  
Solar Radiation ◽  
Root Mean Square Error ◽  
Gaussian Process ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Gaussian Process Regression ◽  
Absolute Error ◽  
Horizontal Surface ◽  
Total Solar Radiation ◽  
Mean Square

Several methods have been used to predict daily solar radiation in recent years, such as artificial intelligence and hybrid models. In this paper, a Wavelet coupled Gaussian Process Regression (W-GPR) model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia (Algeria). A statistical period of four years (2013 -2016) was used where the first three years (2013-2015) are used to train model and the last year (2016) to test the model for predicting daily total solar radiation. Different types of wave mother and different combinations of input data were evaluated based on the minimum air temperature, relative humidity and extraterrestrial solar radiation on a horizontal surface. The results demonstrated the effectiveness of the new hybrid model W-GPR compared to the classical GPR model in terms of Root Mean Square Error (RMSE), relative Root Mean Square Error (rRMSE), Mean Absolute Error (MAE) and determination coefficient (R2).

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.

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