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 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 Combined Multilateration with Machine Learning for Enhanced Aircraft Localization

Proceedings ◽  
2020 ◽  
Vol 59 (1) ◽  
pp. 2
Author(s):  
Benoit Figuet ◽  
Raphael Monstein ◽  
Michael Felux
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Real World ◽  
Mean Squared Error ◽  
Accurate Estimate ◽  
Regression Technique ◽  
Gradient Boosting ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Using Data

In this paper, we present an aircraft localization solution developed in the context of the Aircraft Localization Competition and applied to the OpenSky Network real-world ADS-B data. The developed solution is based on a combination of machine learning and multilateration using data provided by time synchronized ground receivers. A gradient boosting regression technique is used to obtain an estimate of the geometric altitude of the aircraft, as well as a first guess of the 2D aircraft position. Then, a triplet-wise and an all-in-view multilateration technique are implemented to obtain an accurate estimate of the aircraft latitude and longitude. A sensitivity analysis of the accuracy as a function of the number of receivers is conducted and used to optimize the proposed solution. The obtained predictions have an accuracy below 25 m for the 2D root mean squared error and below 35 m for the geometric altitude.

Modelling and Forecasting Portfolio Inflows

2022 ◽  
pp. 1427-1448
Author(s):  
Mogari I. Rapoo ◽  
Elias Munapo ◽  
Martin M. Chanza ◽  
Olusegun Sunday Ewemooje
Keyword(s):  
Banking Sector ◽  
Mean Squared Error ◽  
Model Performance ◽  
Absolute Error ◽  
Support Vector ◽  
Vector Autoregressive ◽  
Squared Error ◽  
Daily Data ◽  
Determining Factors ◽  
Push Factor

This chapter analyses efficiency of support vector regression (SVR), artificial neural networks (ANNs), and structural vector autoregressive (SVAR) models in terms of in-sample forecasting of portfolio inflows (PIs). Time series daily data sourced from Rand Merchant Bank (RMB) covering the period of 1st March 2004 to 1st February 2016 were used. Mean squared error, root mean squared error, mean absolute error, mean absolute squared error, and root mean scaled log error were used to evaluate model performance. The results showed that SVR has the best modelling performance when compared to others. In determining factors that affect allocation of PIs into South Africa based on SVAR, 69% of the variation was explained by pull factors while 9% was explained by push factor. Hence, SVR model is more accurate than ANNs. This chapter therefore recommends that banking sector particularly RMB should use machine learning technique in modelling PIs for a better financial solution.

PREDIKSI HARGA CRYPTOCURRENCY DENGAN METODE K-NEAREST NEIGHBOURS

2018 ◽  
Vol 14 (2) ◽  
pp. 137
Author(s):  
Haerul Fatah ◽  
Agus Subekti
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Search Algorithm ◽  
Absolute Error ◽  
Nearest Neighbour ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Nearest Neighbours

Uang elektronik menjadi pilihan yang mulai ramai digunakan oleh banyak orang, terutama para pengusaha, pebisnis dan investor, karena menganggap bahwa uang elektronik akan menggantikan uang fisik dimasa depan. Cryptocurrency muncul sebagai jawaban atas kendala uang eletronik yang sangat bergantung kepada pihak ketiga. Salah satu jenis Cryptocurrency yaitu Bitcoin. Analogi keuangan Bitcoin sama dengan analogi pasar saham, yakni fluktuasi harga tidak tentu setiap detik. Tujuan dari penelitian yang dilakukan yaitu melakukan prediksi harga Cryptocurrency dengan menggunakan metode KNN (K-Nearest Neighbours). Hasil dari penelitian ini diketahui bahwa model KNN yang paling baik dalam memprediksi harga Cryptocurrency adalah KNN dengan parameter nilai K=3 dan Nearest Neighbour Search Algorithm : Linear NN Search. Dengan nilai Mean Absolute Error (MAE) sebesar 0.0018 dan Root Mean Squared Error (RMSE) sebesar 0.0089.

Comparison between SARIMA and Holt–Winters models for forecasting monthly streamflow in the western region of Cuba

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Gustavo Reinel Alonso Brito ◽  
Anaily Rivero Villaverde ◽  
Andrés Lau Quan ◽  
María Elena Ruíz Pérez
Keyword(s):  
Cross Validation ◽  
Mean Squared Error ◽  
Model Performance ◽  
Absolute Error ◽  
List Type ◽  
Model Assessment ◽  
Squared Error ◽  
Monthly Streamflow ◽  
One Year ◽  
Sarima Models

Abstract The present study aims to compare SARIMA and Holt–Winters model forecasts of mean monthly flow at the V Aniversario basin, western Cuba. Model selection and model assessment are carried out with a rolling cross-validation scheme using mean monthly flow observations from the period 1971–1990. Model performance is analyzed in one- and two-year forecast lead times, and comparisons are made based on mean squared error, root mean squared error, mean absolute error and the Nash–Sutcliffe efficiency; all these statistics are computed from observed and simulated time series at the outlet of the basin. The major findings show that Holt–Winters models had better performance in reproducing the mean series seasonality when the training observations were insufficient, while for longer training subsets, both models were equally competitive in forecasting one year ahead. SARIMA models were found to be more reliable for longer lead-time forecasts, and their limitations after being trained on short observation periods are due to overfitting problems. Article Highlights Comparison based on rolling cross-validation revealed the models forecasts sensibility to available observations amount. HW and SARIMA models perform better when limited observations or long-view forecasting, respectively, otherwise they do similar. HW models were superior modeling less variable monthly flows while SARIMA models better forecast the highly variable periods.

scholarly journals Prediction Of Use Of Electric Kwh Using Support Vector Regression (SVR) Method (Case Study: Pt Pln (Persero) Rayon Seririt)

Repositor ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 525
Author(s):  
Rima Mediana ◽  
Setio Basuki ◽  
Nur Hayatin
Keyword(s):  
Radial Basis Function ◽  
Support Vector Regression ◽  
Basis Function ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Radial Basis

AbstrakPeranan listrik sangat penting bagi kehidupan masyarakat, begitu pentingnya peranan listrik tentu saja berdampak pada kebutuhan listrik yang begitu besar, maka PT. PLN (Persero) Rayon Seririt sebagai penyedia tenaga listrik harus bisa memprediksi besarnya peggunaan listrik rumah tangga setiap harinya. Selain itu menyebabkan semakin besar pula pemakian kwh listik, apabila pemakaian kwh listrik tidak diolah dengan baik akan menimbulkan beban energi listrik yang tidak terbendung. Dengan permasalahan yang telah diuraikan, penelitian ini menerapkan algoritma Support Vector Regression dalam Prediksi Pemakain KWH Listrik untuk mengetahui besarnya pemakaian kwh listrik yang akan datang. Berdasarkan hasil pengujian yang dilakukan hasil nilai akurasi terbaik Mean Absolute Error (MAE) sebesar 133560,1, Root Mean Squared Error (RMSE) sebesar 167664,1, dan Koefisien Korelasi sebesar 84,0 pada kernel polynomial. Sehingga algoritma Support Vector Regression dan fungsi kernel Radial Basis Function (RBF) cocok digunakan dalam memprediksi pemakaian kwh listrik.AbstractThe role of electricity is really significant for societies' live and it brings the huge impacts on the needs of electricity. This circumstance makes PT. PLN (Persero) Rayon Seririt as the provider of electricity must be able to predict the amount of household electricity usage steadily. This also causes the greater use of kwh electricity, if the use of kwh electricity is not treated properly, it will cause the burden of electrical energy is unstoppable.  Through the problems that have been elaborated, this study implements the Support Vector Regression algorithm in the prediction of kwh electricity usage to know the amount of  kwh electricity usage that will come.Based on the results of tests that have been conducted,  the result of best accuracy value Mean Absolute Error (MAE) equal to 133560,1, Root Mean Squared Error (RMSE) equal to 133560,1, and Correlation Coefficient equal to 84,0 at Radial Base Function kernel. It means, the Support Vector Regression algorithm and Radial Basis Function kernel function (RBF) are suitable to predict the use of kwh electricity.

Modelling and Forecasting Portfolio Inflows

2020 ◽  
pp. 329-350
Author(s):  
Mogari I. Rapoo ◽  
Elias Munapo ◽  
Martin M. Chanza ◽  
Olusegun Sunday Ewemooje
Keyword(s):  
Banking Sector ◽  
Mean Squared Error ◽  
Model Performance ◽  
Absolute Error ◽  
Support Vector ◽  
Vector Autoregressive ◽  
Squared Error ◽  
Daily Data ◽  
Determining Factors ◽  
Push Factor

This chapter analyses efficiency of support vector regression (SVR), artificial neural networks (ANNs), and structural vector autoregressive (SVAR) models in terms of in-sample forecasting of portfolio inflows (PIs). Time series daily data sourced from Rand Merchant Bank (RMB) covering the period of 1st March 2004 to 1st February 2016 were used. Mean squared error, root mean squared error, mean absolute error, mean absolute squared error, and root mean scaled log error were used to evaluate model performance. The results showed that SVR has the best modelling performance when compared to others. In determining factors that affect allocation of PIs into South Africa based on SVAR, 69% of the variation was explained by pull factors while 9% was explained by push factor. Hence, SVR model is more accurate than ANNs. This chapter therefore recommends that banking sector particularly RMB should use machine learning technique in modelling PIs for a better financial solution.

scholarly journals Non-invasive Estimation of Haemoglobin Level Using PCA and Artificial Neural Networks

2019 ◽  
Vol 13 (1) ◽  
pp. 114-119 ◽  
Author(s):  
M. Lakshmi ◽  
P. Manimegalai
Keyword(s):  
Principal Components ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Root Mean Squared Error ◽  
Non Invasive ◽  
Squared Error ◽  
Wide Range ◽  
R Value ◽  
Invasive Measurement

Objective: Haemoglobin(Hb) measurement is generally performed by the traditional “fingerstick” test i.e., by invasively drawing blood from the body. Although the conventional laboratory measurement is accurate, it has its own limitations such as time delay, inconvenience of the patient, exposure to biohazards and the lack of real-time monitoring in critical situations. Non-invasive Haemoglobin Measurement (SpHb) has gained enormous attention among researches and can provide an earlier diagnosis to polycythemia, anaemia, various cardiovascular diseases, etc. Currently, Photoplethysmograph signal (PPG) is used for measuring oxygen saturation, to monitor the depth of anesthesia, heart rate and respiration monitoring. But through detailed statistical analysis, PPG signal can provide further information about various blood components. Investigation / Methodology: In this paper, an approach for non-invasive measurement of Hb using PPG, Principal Component Analysis (PCA) and Neural Network is proposed. A transmissive type PPG sensor is developed which is interfaced with Crowduino for the acquisition of PPG. From the obtained PPG signal, Principal Components (PC) are extracted. SpHb is predicted followed by the extraction of features from the PC. The analysis involves the SpHb prediction using a single PC, double PC and finally all the three PC. The predicted SpHb is evaluated with Hblab in terms of R-value, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. Conclusion: An approach for non-invasive measurement of Hb using Principal Components obtained from the PPG signal is discussed. The SpHb value is compared with the Hblab values. Correlation R-value between SpHb and Hblab is 0.77 when three principal components are used. Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) between SpHb and Hblab are 0.3, 0.44 and 0.6633 respectively when SpHb is measured with three principal components. It is evident from the result analysis that SpHb shows the promising result when all the three principal components are used. However, one of the limitations of the work is that the population setting chosen for the work does not include paediatric patients, accurately ill patient, pregnant population and surgical patients. With detailed analysis on a wide range of population setting, Hb prediction using PPG is a promising approach for non-invasive measurement.

scholarly journals Artificial intelligence with deep learning algorithms to model relationships between total tree height and diameter at breast height

2020 ◽  
Vol 29 (2) ◽  
pp. e013
Author(s):  
İlker Ercanli
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Mean Squared Error ◽  
Tree Height ◽  
Absolute Error ◽  
Diameter At Breast Height ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Hidden Layer ◽  
Total Tree

Aim of Study: As an innovative prediction technique, Artificial Intelligence technique based on a Deep Learning Algorithm (DLA) with various numbers of neurons and hidden layer alternatives were trained and evaluated to predict the relationships between total tree height (TTH) and diameter at breast height (DBH) with nonlinear least squared (NLS) regression models and nonlinear mixed effect (NLME) regression models.Area of Study: The data of this study were measured from even-aged, pure Turkish Pine (Pinus brutia Ten.) stands in the Kestel Forests located in the Bursa region of northwestern Turkey.Material and Methods: 1132 pairs of TTH-DBH measurements from 132 sample plots were used for modeling relationships between TTH, DBH, and stand attributes such as dominant height (Ho) and diameter (Do).Main Results: The combination of 100 # neurons and 8 # hidden layer in DLA resulted in the best predictive total height prediction values with Average Absolute Error (0.4188), max. Average Absolute Error (3.7598), Root Mean Squared Error (0.6942), Root Mean Squared error % (5.2164), Akaike Information Criteria (-345.4465), Bayesian Information Criterion (-330.836), the average Bias (0.0288) and the average Bias % (0.2166), and fitting abilities with r (0.9842) and Fit Index (0.9684). Also, the results of equivalence tests showed that the DLA technique successfully predicted the TTH in the validation dataset.Research highlights: These superior fitting scores coupled with the validation results in TTH predictions suggested that deep learning network models should be considered an alternative to the traditional nonlinear regression techniques and should be given importance as an innovative prediction technique.Keywords: Prediction; artificial intelligence; deep learning algorithms; number of neurons; hidden layer alternatives.Abbreviations: TTH (total tree height), DBH (diameter at breast height), OLS (ordinary least squares), NLME (nonlinear mixed effect), AIT (Artificial Intelligence Techniques), ANN (Artificial Neural Network), DLA (Deep Learning Algorithm), GPU (Graphical Processing Units), NLS (nonlinear least squared), RMSE (root mean squared error), AIC (Akaike information criteria), BIC (Bayesian information criterion), FI (fit index), AAE (average absolute error), BLUP (best linear unbiased predictor), TOST (two one-sided test method). 

scholarly journals PENERAPAN ALGORITMA RANDOM FOREST UNTUK IDENTIFIKASI DEHIDRASI BERBASIS CITRA URINE

2020 ◽  
Vol 6 (3) ◽  
pp. 49-54
Author(s):  
Niyalatul Muna ◽  
Faisal Lutfi Afriansyah ◽  
Ameng Bagus Suprayogy
Keyword(s):  
Random Forest ◽  
Cross Validation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Root Mean Squared Error ◽  
Squared Error

Tingkat dehidrasi tidak hanya bisa dirasakan secara langsung akan tetapi dapat diamati dan dilihat secara fisik berbasis visual. Secara visual salah satu gejala dari dehidrasi dapat dilihat dari warna urine. Gejala ini biasanya tidak begitu diperhatikan dan dianggap biasa. Padahal gejala hipohidrasi atau dehidrasi merupakan dampak yang merugikan dari asupan air yang tidak memadai sehingga mempengaruhi warna urine yang dihasilkan. Kesulitan panca indra manusia membedakan gejala dehidrasi dan melihat perbedaan warna urine secara visual sering diterjemahkan berbeda-beda, dikarenakan tingkat kemiripan warna yang dihasilkan. Beberapa penelitian menunjukkan adanya pemanfaatan teknologi kamera dengan sistem cerdas dapat membantu kesulitan dan keterbatasan panca indra manusia. Penelitian ini menggunakan citra urine diambil dari sample orang dewasa yang dikelompokkan berdasarkan kategori warna urine hasil penelitian terdahulu. Pengambilan fitur dari setiap citra urine diambil nilai warna dari  YCbCr. Model warna yang dihasilkan dari setiap sampel akan diidentifikasi menggunakan algoritma Random Forest dengan cross-validation. Hasil dari percobaan yang dilakukan menunjukkan akurasi 90% dari 30 dataset yang diujikan dengan nilai precision 90.2%, recall 90%, Mean absolute error 0.2473, dan Root mean squared error sebesar 0.3208.

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