scholarly journals Equations to Convert Compacted Crown Ratio to Uncompacted Crown Ratio for Trees in the Interior West

2009 ◽  
Vol 24 (2) ◽  
pp. 76-82 ◽  
Author(s):  
Chris Toney ◽  
Matthew C. Reeves
Keyword(s):  
Mean Squared Error ◽  
Model Fitting ◽  
Absolute Error ◽  
Logistic Function ◽  
Forest Service ◽  
Regression Equations ◽  
Us Forest Service ◽  
Using Data ◽  
Interior West ◽  
Species Specific

Abstract Crown ratio is the proportion of total tree length supporting live foliage. Inventory programs of the US Forest Service generally define crown ratio in terms of compacted or uncompacted measurements. Measurement of compacted crown ratio (CCR) involves envisioning the transfer of lower branches of trees with asymmetric crowns to fill holes in the upper portion of the crown. Uncompacted crown ratio (UNCR) is measured without adjustment for holes in the crown and may be a more appropriate measurement when interest is on height to the first live branches in the crown. CCR is more commonly available because it is a standard measurement of the Forest Inventory and Analysis (FIA) program of US Forest Service, and UNCR is an optional measurement at the discretion of regional FIA units. The mean difference between UNCR and CCR of trees in the western United States (0.17 live crown) could be large enough to introduce biologically significant bias in applications that use crown ratio to derive height to crown base. Equations were developed to convert CCR to UNCR for 35 tree species in Idaho, Montana, Wyoming, Nevada, Utah, Colorado, Arizona, and New Mexico using data from the Interior West FIA unit. UNCR was modeled as a logistic function of CCR and tree diameter, and species-specific equations were fit by nonlinear regression. Root mean squared error for the regression equations ranged from 0.06 to 0.15 UNCR (mean absolute error, 0.04ߝ0.12 UNCR). Equations for most species performed well when applied to test data that were not available at the time of model fitting.

scholarly journals Equations Relating Compacted and Uncompacted Live Crown Ratio for Common Tree Species in the South

2010 ◽  
Vol 34 (3) ◽  
pp. 118-123 ◽  
Author(s):  
KaDonna C. Randolph
Keyword(s):  
Mean Squared Error ◽  
Model Performance ◽  
Absolute Error ◽  
Logistic Function ◽  
Root Mean Squared Error ◽  
Fit Statistics ◽  
Squared Error ◽  
Hardwood Species ◽  
Using Data ◽  
Species Specific

Abstract Species-specific equations to predict uncompacted crown ratio (UNCR) from compacted live crown ratio (CCR), tree length, and stem diameter were developed for 24 species and 12 genera in the southern United States. Using data from the US Forest Service Forest Inventory and Analysis program, nonlinear regression was used to model UNCR with a logistic function. Model performance was evaluated with standard fit statistics (root mean squared error, mean absolute error, mean error, and model efficiency) and by comparing the results of using the observed and predicted UNCR values in secondary applications. Root mean squared error for the regression models ranged from 0.062 to 0.176 UNCR and averaged 0.114 UNCR across all models. Height to live crown base calculations and crown width estimations based on the observed and predicted UNCR values were in close agreement. Overall, the models performed well for the Pinus and Taxodium genera and several individual hardwood species; however, model performance was generally poor for the Acer, Quercus, and Carya genera.

scholarly journals Statistical modelling of agrometeorological time series by exponential smoothing

2016 ◽  
Vol 30 (1) ◽  
pp. 57-65 ◽  
Author(s):  
Małgorzata Murat ◽  
Iwona Malinowska ◽  
Holger Hoffmann ◽  
Piotr Baranowski
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Statistical Modelling ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Growth And Yield ◽  
Climate Change Scenarios ◽  
Autocorrelation Functions ◽  
Using Data

Abstract Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, long-term meteorological series are used in climate change scenarios. Such studies often require forecasting or projection of meteorological variables, eg the projection of occurrence of the extreme events. The aim of the article was to determine the most suitable exponential smoothing models to generate forecast using data on air temperature, wind speed, and precipitation time series in Jokioinen (Finland), Dikopshof (Germany), Lleida (Spain), and Lublin (Poland). These series exhibit regular additive seasonality or non-seasonality without any trend, which is confirmed by their autocorrelation functions and partial autocorrelation functions. The most suitable models were indicated by the smallest mean absolute error and the smallest root mean squared error.

Prediction of Apnea-Hypopnea Index Using Sound Data Collected by a Noncontact Device

2020 ◽  
Vol 162 (3) ◽  
pp. 392-399
Author(s):  
Jeong-Whun Kim ◽  
Taehoon Kim ◽  
Jaeyoung Shin ◽  
Kyogu Lee ◽  
Sunkyu Choi ◽  
...  
Keyword(s):  
Random Forest ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Sleep Stages ◽  
Support Vector ◽  
Apnea Hypopnea Index ◽  
Tertiary Referral Hospital ◽  
Obstructive Sleep ◽  
Using Data ◽  
Sound Features

Objective To predict the apnea-hypopnea index (AHI) in patients with obstructive sleep apnea (OSA) using data from breathing sounds recorded using a noncontact device during sleep. Study Design Prospective cohort study. Setting Tertiary referral hospital. Subject and Methods Audio recordings during sleep were performed using an air-conduction microphone during polysomnography. Breathing sounds recorded from all sleep stages were analyzed. After noise reduction preprocessing, the audio data were segmented into 5-second windows and sound features were extracted. Estimation of AHI by regression analysis was performed using a Gaussian process, support vector machine, random forest, and simple linear regression, along with 10-fold cross-validation. Results In total, 116 patients who underwent attended, in-laboratory, full-night polysomnography were included. Overall, random forest resulted in the highest performance with the highest correlation coefficient (0.83) and least mean absolute error (9.64 events/h) and root mean squared error (13.72 events/h). Other models resulted in somewhat lower but similar performances, with correlation coefficients ranging from 0.74 to 0.79. The estimated AHI tended to be underestimated as the severity of OSA increased. Regarding bias and precision, estimation performances in the severe OSA subgroup were the lowest, regardless of the model used. Among sound features, derivative of the area methods of moments of overall standard deviation demonstrated the highest correlation with AHI. Conclusion AHI was fairly predictable by using data from breathing sounds generated during sleep. The prediction model may be useful not only for prescreening but also for follow-up after treatment in patients with OSA.

scholarly journals Estimation of Discharge and Total Water Level at Yedgaon Dam using Data Driven Techniques

2021 ◽  
Vol 1197 (1) ◽  
pp. 012021
Author(s):  
Preeti S. Kulkarni ◽  
Shreenivas Londhe ◽  
Nikita Sainkar ◽  
Sayali Rote
Keyword(s):  
Water Level ◽  
Correlation Coefficient ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Single Equation ◽  
Data Driven ◽  
Support Vector ◽  
Good Prediction ◽  
M5 Model Tree ◽  
Using Data

Abstract A reservoir operation planning using Data driven Techniques is gaining its momentum in hydrological area with good prediction and Estimation capabilities. The present work aims at using the 5 years data of Water Level to estimate the discharge and water level at the Yedgaon dam which is like pick up weir having its own yield and storage. It receives water from Dimbhe (though DLBC), Wadaj (through MLBC), Manikdoh (through river) and through Pimpalgaojoge (through river), in the Kukadi project of Maharashtra State, India. 4 different models were developed to estimate the water level using the Data Driven Techniques: M5 Model Tree, Support Vector Regression, Multi Gene Genetic Programming and Random Forest. The Accuracy of the developed models is assessed by the values of coefficient of correlation, coefficient of efficiency, mean absolute error and root mean squared error and comparison is done between actual values and Predicted values. The results indicated that the MGGP model was superior as compared to other techniques with correlation coefficient as 0.86 with an advantage of a single equation to estimate the water level.

scholarly journals Equations for Predicting Uncompacted Crown Ratio Based on Compacted Crown Ratio and Tree Attributes

2004 ◽  
Vol 19 (4) ◽  
pp. 260-267 ◽  
Author(s):  
David Azuma ◽  
Vicente J. Monleon ◽  
Donald Gedney
Keyword(s):  
Mean Squared Error ◽  
Monitoring Program ◽  
Estimation Method ◽  
Forest Health ◽  
Tree Height ◽  
Fire Risk ◽  
Logistic Function ◽  
Crown Fire ◽  
Usda Forest Service ◽  
Using Data

Abstract Equations to predict uncompacted crown ratio as a function of compacted crown ratio, tree diameter, and tree height are developed for the main tree species in Oregon, Washington, and California using data from the Forest Health Monitoring Program, USDA Forest Service. The uncompacted crown ratio was modeled with a logistic function and fitted using weighted, nonlinear regression. The models were evaluated using cross-validation. Mean squared error of predicted uncompacted crown ratio was between 0.1 and 0.15, overall bias was negligible, and correlation between the predicted and observed uncompacted crown ratio was high for most species. The sensitivity of crown fire risk to crown ratio estimation method was evaluated using the Fire and Fuels Extension of the Forest Vegetation Simulator. Torching index, an estimate of the wind speed needed for a crown fire to develop, was significantly greater when compacted crown ratio was used instead of uncompacted crown ratio. The close agreement in torching indices simulated using predicted and observed uncompacted crown ratio provides further evidence of the utility of the models developed in this study. West. J. Appl. For. 19(4):260–267.

scholarly journals Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.

2021 ◽  
Vol 37 ◽  
pp. e37076
Author(s):  
João Everthon da Silva Ribeiro ◽  
Francisco Romário Andrade Figueiredo ◽  
Ester Dos Santos Coêlho ◽  
Marlenildo Ferreira Melo
Keyword(s):  
Leaf Area ◽  
Leaf Blade ◽  
Mean Squared Error ◽  
Information Criterion ◽  
Absolute Error ◽  
Allometric Equation ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Linear Leaf ◽  
Leaf Dimensions

Estimating leaf area using non-destructive methods from regression equations has become a more efficient, quick, and accurate way. Thus, this study aimed to propose an equation that significantly estimates the leaf area of Psychotria colorata (Rubiaceae) through linear leaf dimensions. For this purpose, 200 leaves of different shapes were collected, and length (L), width (W), product of length by width (L.W), and real leaf area (LA) of each leaf blade were determined. Then, equations were adjusted for predicting leaf area using simple linear, linear (0.0), quadratic, cubic, power, and exponential regression models. The proposed equation was selected according to the coefficient of determination (R²), Willmott's agreement index (d), Akaike's information criterion (AIC), mean absolute error (MAE), mean squared error (RMSE) and BIAS index. It was noted that the equations adjusted using L.W met the best criteria for estimating leaf area, but the equation LA = 0.59 * L.W from linear regression without intercept was the most suitable. This equation predicts that 59% of leaf area is explained by L.W. Concluding, the leaf area of P. colorata can be estimated using an allometric equation that uses linear leaf blade dimensions.

scholarly journals Evaluating Evapotranspiration By Using Data Mining Instead of The Physical Based Model In Remote Sensing

2021 ◽  
Author(s):  
Lamya Neissi ◽  
Mona Golabi ◽  
Mohammad Albaji ◽  
Abd Ali Naseri
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Decision Tree ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Plant Production ◽  
Tree Model ◽  
Water Index ◽  
Input Variables ◽  
Using Data

Abstract Precise calculations for plant water requirements and evapotranspiration is very crucial in determining the volume of water consumption for plant production. In order to estimate evapotranspiration in the extended area, different remote sensing algorithms required many climatological variables. Climatological variable measurements will cover small limited areas which can cause an error in extended areas. By using data mining and remote sensing, the evapotranspiration process can be modeled. In this research, the physical-based SEBAL evapotranspiration algorithm was modeled by M5 decision tree equations in GIS. Input variables of the M5 decision tree consisted of albedo, emissivity, and Normalized Difference Water Index (NDWI) which are represented as absorbed light, transformed light, and plant moisture, respectively. After extracting the best equations in the M5 decision tree model for 8 April 2019, these equations were modeled in GIS by using python scripts for 8 April 2019 and 3 April 2020. The calculated correlation coefficient (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for 8 April 2019 were 0.92, 0.54, and 0.42 and for 3 April 2020 were 0.95, 0.31, and 0.23, respectively. Also, sensitivity and uncertainty analysis were considered for more model evaluation. Those analysis revealed that evapotranspiration is sensitive to albedo more than the two other model inputs and the estimated evapotranspiration obtained by data mining is in acceptable range of certainty.

Total weak acid concentration and effective dissociation constant of nonvolatile buffers in human plasma

2001 ◽  
Vol 91 (3) ◽  
pp. 1364-1371 ◽  
Author(s):  
Peter D. Constable
Keyword(s):  
Dissociation Constant ◽  
Human Plasma ◽  
Total Concentration ◽  
Weak Acid ◽  
Strong Ion Difference ◽  
Acid Base ◽  
Horse Plasma ◽  
Using Data ◽  
Species Specific

The strong ion approach provides a quantitative physicochemical method for describing the mechanism for an acid-base disturbance. The approach requires species-specific values for the total concentration of plasma nonvolatile buffers (Atot) and the effective dissociation constant for plasma nonvolatile buffers ( K a), but these values have not been determined for human plasma. Accordingly, the purpose of this study was to calculate accurate Atot and K a values using data obtained from in vitro strong ion titration and CO2tonometry. The calculated values for Atot (24.1 mmol/l) and K a (1.05 × 10−7) were significantly ( P < 0.05) different from the experimentally determined values for horse plasma and differed from the empirically assumed values for human plasma (Atot = 19.0 meq/l and K a = 3.0 × 10−7). The derivatives of pH with respect to the three independent variables [strong ion difference (SID), Pco 2, and Atot] of the strong ion approach were calculated as follows: [Formula: see text] [Formula: see text], [Formula: see text]where S is solubility of CO2 in plasma. The derivatives provide a useful method for calculating the effect of independent changes in SID+, Pco 2, and Atot on plasma pH. The calculated values for Atot and K a should facilitate application of the strong ion approach to acid-base disturbances in humans.

scholarly journals A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

2021 ◽  
Vol 13 (14) ◽  
pp. 7612
Author(s):  
Mahdis sadat Jalaee ◽  
Alireza Shakibaei ◽  
Amin GhasemiNejad ◽  
Sayyed Abdolmajid Jalaee ◽  
Reza Derakhshani
Keyword(s):  
Hybrid Method ◽  
Computational Intelligence ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Coal Consumption ◽  
Squared Error ◽  
Artificial Neural ◽  
Socio Economic Variables ◽  
Intelligence Approach

Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

scholarly journals Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 861
Author(s):  
Kyeung Ho Kang ◽  
Mingu Kang ◽  
Siho Shin ◽  
Jaehyo Jung ◽  
Meina Li
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Ensemble Model ◽  
Direct Calorimetry ◽  
Patch Type ◽  
Mortality And Morbidity ◽  
Squared Error ◽  
Sensor Module

Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.

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