scholarly journals Estimation of the Monthly Dynamics of Surface Water in Wetlands from Satellite and Secondary Hydro-Climatological Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2380
Author(s):  
Antonio-Juan Collados-Lara ◽  
Eulogio Pardo-Igúzquiza ◽  
David Pulido-Velazquez ◽  
Leticia Baena-Ruiz
Keyword(s):  
Surface Water ◽  
Regression Models ◽  
Cross Validation ◽  
Mean Squared Error ◽  
Climatological Data ◽  
Explanatory Variables ◽  
Squared Error ◽  
Historical Series ◽  
Novel Method ◽  
The Mean

Satellites produce valuable information for studying the surface water in wetlands, but in many cases the period covered, the spatial resolution and/or the revisit frequency is not enough to produce long historical series. In this paper we propose a novel method which uses regression models that include climatic and hydrological variables to complete the satellite information. We used this method in the Lagunas de Ruidera wetland (Spain). We approached the monthly dynamic of the surface water for a long period (1984–2015). Information from LANDSAT (30-m resolution) and MODIS (250-m resolution) satellites were tested but, due to the size of some lagoons, only the LANDSAT approach produced satisfactory results. An ensemble of regression models based on hydro-climatological explanatory variables was defined to complete the gaps in the monthly surface water. It showed a root mean squared error of around 476 pixels (0.4 Km2) in the cross-validation analysis. Our analysis showed that the explanatory variables with a more significant participation in the regression ensemble are the aquifer discharge, the effective precipitation and the surface water from the previous month. From January to June, the mean surface water in Lagunas de Ruidera is around 4.3 Km2. In summer a reduction of around 13% of the surface water can be observed, which is recovered during the autumn.

scholarly journals A New Ridge-Type Estimator for the Gamma Regression Model

Scientifica ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Adewale F. Lukman ◽  
Issam Dawoud ◽  
B. M. Golam Kibria ◽  
Zakariya Y. Algamal ◽  
Benedicta Aladeitan
Keyword(s):  
Biological Activity ◽  
Regression Model ◽  
Mean Squared Error ◽  
Real Life ◽  
Regression Parameter ◽  
Likelihood Estimator ◽  
Response Variable ◽  
Explanatory Variables ◽  
Squared Error ◽  
The Mean

The known linear regression model (LRM) is used mostly for modelling the QSAR relationship between the response variable (biological activity) and one or more physiochemical or structural properties which serve as the explanatory variables mainly when the distribution of the response variable is normal. The gamma regression model is employed often for a skewed dependent variable. The parameters in both models are estimated using the maximum likelihood estimator (MLE). However, the MLE becomes unstable in the presence of multicollinearity for both models. In this study, we propose a new estimator and suggest some biasing parameters to estimate the regression parameter for the gamma regression model when there is multicollinearity. A simulation study and a real-life application were performed for evaluating the estimators' performance via the mean squared error criterion. The results from simulation and the real-life application revealed that the proposed gamma estimator produced lower MSE values than other considered estimators.

scholarly journals CV-α: designing validations sets to increase the precision and enable multiple comparison tests in genomic prediction

2020 ◽  
Author(s):  
Rafael Massahiro Yassue ◽  
José Felipe Gonzaga Sabadin ◽  
Giovanni Galli ◽  
Filipe Couto Alves ◽  
Roberto Fritsche-Neto
Keyword(s):  
Genomic Prediction ◽  
Cross Validation ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Predictive Ability ◽  
Proof Of Concept ◽  
Squared Error ◽  
High Effect ◽  
The Mean ◽  
Fold Cross Validation

AbstractUsually, the comparison among genomic prediction models is based on validation schemes as Repeated Random Subsampling (RRS) or K-fold cross-validation. Nevertheless, the design of training and validation sets has a high effect on the way and subjectiveness that we compare models. Those procedures cited above have an overlap across replicates that might cause an overestimated estimate and lack of residuals independence due to resampling issues and might cause less accurate results. Furthermore, posthoc tests, such as ANOVA, are not recommended due to assumption unfulfilled regarding residuals independence. Thus, we propose a new way to sample observations to build training and validation sets based on cross-validation alpha-based design (CV-α). The CV-α was meant to create several scenarios of validation (replicates x folds), regardless of the number of treatments. Using CV-α, the number of genotypes in the same fold across replicates was much lower than K-fold, indicating higher residual independence. Therefore, based on the CV-α results, as proof of concept, via ANOVA, we could compare the proposed methodology to RRS and K-fold, applying four genomic prediction models with a simulated and real dataset. Concerning the predictive ability and bias, all validation methods showed similar performance. However, regarding the mean squared error and coefficient of variation, the CV-α method presented the best performance under the evaluated scenarios. Moreover, as it has no additional cost nor complexity, it is more reliable and allows the use of non-subjective methods to compare models and factors. Therefore, CV-α can be considered a more precise validation methodology for model selection.

scholarly journals Bitcoin Price Prediction using SVM and ARIMA Model

2021 ◽  
pp. 1094-1096
Author(s):  
Gausiya Momin ◽  
Trupti Ingle ◽  
Vaishnavi Mirajkar ◽  
A. A. Magar
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Mean Squared Error ◽  
Learning Algorithms ◽  
Arima Model ◽  
Machine Learning Algorithms ◽  
Price Prediction ◽  
Squared Error ◽  
Minute Interval ◽  
The Mean

Bitcoin is the most profitable in the cryptocurrency market. However, the prices of Bitcoin have highly fluctuated which makes them very difficult to predict. This research aims to discover the most efficient accuracy model to predict Bitcoin prices from various machine learning algorithms. Using one-minute interval trading data on the exchange website name is bit stamp from January 1, 2012, to January 8, 2018, some different regression models with sci-kit- learn and Keras libraries had experimented. The best results showed that the Mean Squared Error (MSE) was as low as 0.00002 and the R-Square (R2) was as high as 99.2 Percentage.

Computing trade-offs in robust design: Perspectives of the mean squared error

2011 ◽  
Vol 60 (2) ◽  
pp. 248-255 ◽  
Author(s):  
Sangmun Shin ◽  
Funda Samanlioglu ◽  
Byung Rae Cho ◽  
Margaret M. Wiecek
Keyword(s):  
Robust Design ◽  
Mean Squared Error ◽  
Squared Error ◽  
Trade Offs ◽  
The Mean

scholarly journals Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

2018 ◽  
Vol 10 (12) ◽  
pp. 4863 ◽  
Author(s):  
Chao Huang ◽  
Longpeng Cao ◽  
Nanxin Peng ◽  
Sijia Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Multilayer Perception ◽  
Squared Error ◽  
The Mean ◽  
Effectiveness And Efficiency ◽  
Mlp Network ◽  
Grid Operation ◽  
Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

scholarly journals On double stage minimax-shrinkage estimator for generalized Rayleigh model

2016 ◽  
Vol 5 (1) ◽  
pp. 39 ◽  
Author(s):  
Abbas Najim Salman ◽  
Maymona Ameen
Keyword(s):  
Sample Size ◽  
Shape Parameter ◽  
Mean Squared Error ◽  
Scale Parameter ◽  
Rayleigh Distribution ◽  
Shrinkage Estimator ◽  
Squared Error ◽  
Expected Sample Size ◽  
Generalized Rayleigh Distribution ◽  
The Mean

<p>This paper is concerned with minimax shrinkage estimator using double stage shrinkage technique for lowering the mean squared error, intended for estimate the shape parameter (a) of Generalized Rayleigh distribution in a region (R) around available prior knowledge (a<sub>0</sub>) about the actual value (a) as initial estimate in case when the scale parameter (l) is known .</p><p>In situation where the experimentations are time consuming or very costly, a double stage procedure can be used to reduce the expected sample size needed to obtain the estimator.</p><p>The proposed estimator is shown to have smaller mean squared error for certain choice of the shrinkage weight factor y(<strong>×</strong>) and suitable region R.</p><p>Expressions for Bias, Mean squared error (MSE), Expected sample size [E (n/a, R)], Expected sample size proportion [E(n/a,R)/n], probability for avoiding the second sample and percentage of overall sample saved  for the proposed estimator are derived.</p><p>Numerical results and conclusions for the expressions mentioned above were displayed when the consider estimator are testimator of level of significanceD.</p><p>Comparisons with the minimax estimator and with the most recent studies were made to shown the effectiveness of the proposed estimator.</p>

scholarly journals Mapping intended spinal site of care from the upright to prone position: an interexaminer reliability study

2014 ◽  
Vol 22 (1) ◽  
Author(s):  
Robert Cooperstein ◽  
Morgan Young
Keyword(s):  
Prone Position ◽  
Mean Squared Error ◽  
Thought Experiment ◽  
Spinous Process ◽  
Intraclass Correlation ◽  
Manual Muscle Testing ◽  
Muscle Testing ◽  
Interexaminer Reliability ◽  
Novel Method ◽  
The Mean

Abstract Background Upright examination procedures like radiology, thermography, manual muscle testing, and spinal motion palpation may lead to spinal interventions with the patient prone. The reliability and accuracy of mapping upright examination findings to the prone position is unknown. This study had 2 primary goals: (1) investigate how erroneous spine-scapular landmark associations may lead to errors in treating and charting spine levels; and (2) study the interexaminer reliability of a novel method for mapping upright spinal sites to the prone position. Methods Experiment 1 was a thought experiment exploring the consequences of depending on the erroneous landmark association of the inferior scapular tip with the T7 spinous process upright and T6 spinous process prone (relatively recent studies suggest these levels are T8 and T9, respectively). This allowed deduction of targeting and charting errors. In experiment 2, 10 examiners (2 experienced, 8 novice) used an index finger to maintain contact with a mid-thoracic spinous process as each of 2 participants slowly moved from the upright to the prone position. Interexaminer reliability was assessed by computing Intraclass Correlation Coefficient, standard error of the mean, root mean squared error, and the absolute value of the mean difference for each examiner from the 10 examiner mean for each of the 2 participants. Results The thought experiment suggesting that using the (inaccurate) scapular tip landmark rule would result in a 3 level targeting and charting error when radiological findings are mapped to the prone position. Physical upright exam procedures like motion palpation would result in a 2 level targeting error for intervention, and a 3 level error for charting. The reliability experiment showed examiners accurately maintained contact with the same thoracic spinous process as the participant went from upright to prone, ICC (2,1) = 0.83. Conclusions As manual therapists, the authors have emphasized how targeting errors may impact upon manual care of the spine. Practitioners in other fields that need to accurately locate spinal levels, such as acupuncture and anesthesiology, would also be expected to draw important conclusions from these findings.

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