Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts

Reliable predictions of the energy consumption and production is important information for the management and integration of renewable energy sources. Several different Machine Learning (ML) methodologies have been tested for predicting the energy consumption/production based on the information of hydro-meteorological data. The methods analysed include Multivariate Adaptive Regression Splines (MARS) and various Quantile Regression (QR) models like Quantile Random Forest (QRF) and Gradient Boosting Machines (GBM). Additionally, a Nonhomogeneous Gaussian Regression (NGR) approach has been tested for combining and calibrating monthly ML based forecasts driven by ensemble weather forecasts. The novelty and main focus of this study is the comparison of the capability of ML methods for producing reliable predictive uncertainties and the application of monthly weather forecasts. Different skill scores have been used to verify the predictions and their uncertainties and first results for combining the ML methods applying the NGR approach and coupling the predictions with monthly ensemble weather forecasts are shown for the southern Switzerland (Canton of Ticino). These results highlight the possibilities of improvements using ML methods and the importance of optimally combining different ML methods for achieving more accurate estimates of future energy consumptions and productions with sharper prediction uncertainty estimates (i.e., narrower prediction intervals).

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Aerosol optical depth prediction using machine learning techniques

10.5194/ems2021-22 ◽

2021 ◽

Author(s):

Stavros Andreas Logothetis ◽

Vasileios Salamalikis ◽

Andreas Kazantzidis

Keyword(s):

Machine Learning ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Multivariate Adaptive Regression Splines ◽

Surface Radiation ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Support Vector ◽

Climate Projections ◽

Extreme Gradient Boosting

<p>Aerosol optical depth (AOD) describes adequately aerosols&#8217;s burden and extinction within an atmospheric column. AOD can be retrieved using remote sensing instruments such as ground-based sun photometers. Despite the very good quality of ground based AOD measurements, their spatiotemporal coverage is restricted. In this study, an alternative approach of AOD estimation is proposed with the synergy of ground-based measurements and machine learning (ML) techniques, in order to expand and complement the existing spatiotemporal capabilities of AOD data. The ML algorithms which are implemented are: Random Forests, Gradient Boosting Machines, Extreme Gradient Boosting Machines, Support Vector Regression, K-nearest Neighbors Regression, and Multivariate Adaptive Regression Splines. Each model receives as input information the Global Horizontal Irradiance (GHI) as well as water vapor (WV) content in hourly basis and under clear skies. A randomized cross-validation search scheme is implemented to obtain the optimal hyperparameters and avoid overfitting for each ML algorithm. GHI and WV are retrieved from Baseline Surface Radiation Network (BSRN) and NASA&#8217;s Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) reanalysis product respectively. AOD estimations are evaluated against AOD from AErosol RObotic NETwork (AERONET) inversion product, using the Level 2.0 Version 3 (L2V3) which provides cloud-screened and quality assured measurements. In total, 29 collocated AERONET-BSRN stations are used spanning from 2000 to 2019. Since, the aerosol pattern is different at each site, the effect of various aerosol types is further investigated. ML-based AOD predictions are adequately good, highlighting the feasibility of ML algorithms on producing AOD data. The results of this study could be useful for direct normal irradiance estimations as well as aerosols radiative effect calculations and climate projections.</p>

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Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease

BMC Cardiovascular Disorders ◽

10.1186/s12872-021-01925-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Moojung Kim ◽

Young Jae Kim ◽

Sung Jin Park ◽

Kwang Gi Kim ◽

Pyung Chun Oh ◽

...

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Influenza Vaccination ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Support Vector ◽

Age Group ◽

Learning Models ◽

Extreme Gradient Boosting ◽

Machine Learning Models

Abstract Background Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to develop a machine learning model to identify Korean adult CVD patients with low adherence to influenza vaccination Methods Adults with CVD (n = 815) from a nationally representative dataset of the Fifth Korea National Health and Nutrition Examination Survey (KNHANES V) were analyzed. Among these adults, 500 (61.4%) had answered "yes" to whether they had received seasonal influenza vaccinations in the past 12 months. The classification process was performed using the logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) machine learning techniques. Because the Ministry of Health and Welfare in Korea offers free influenza immunization for the elderly, separate models were developed for the < 65 and ≥ 65 age groups. Results The accuracy of machine learning models using 16 variables as predictors of low influenza vaccination adherence was compared; for the ≥ 65 age group, XGB (84.7%) and RF (84.7%) have the best accuracies, followed by LR (82.7%) and SVM (77.6%). For the < 65 age group, SVM has the best accuracy (68.4%), followed by RF (64.9%), LR (63.2%), and XGB (61.4%). Conclusions The machine leaning models show comparable performance in classifying adult CVD patients with low adherence to influenza vaccination.

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Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽

10.3390/ma14051089 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1089

Author(s):

Sung-Hee Kim ◽

Chanyoung Jeong

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Multiclass Classification ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Smart Manufacturing ◽

Gradient Boosting ◽

Experimental Conditions ◽

Learning Techniques ◽

Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

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Machine Learning-Based Models for the Estimation of the Energy Consumption in Metal Forming Processes

Metals ◽

10.3390/met11050833 ◽

2021 ◽

Vol 11 (5) ◽

pp. 833

Author(s):

Irene Mirandola ◽

Guido A. Berti ◽

Roberto Caracciolo ◽

Seungro Lee ◽

Naksoo Kim ◽

...

Keyword(s):

Machine Learning ◽

Finite Element ◽

Energy Consumption ◽

Metal Forming ◽

Mutual Influence ◽

Rolling Process ◽

Ring Rolling ◽

Gradient Boosting ◽

Average Error ◽

Ring Rolling Mill

This research provides an insight on the performances of machine learning (ML)-based algorithms for the estimation of the energy consumption in metal forming processes and is applied to the radial-axial ring rolling process. To define the mutual influence between ring geometry, process settings, and ring rolling mill geometries with the resulting energy consumption, measured in terms of the force integral over the processing time (FIOT), FEM simulations have been implemented in the commercial SW Simufact Forming 15. A total of 380 finite element simulations with rings ranging from 650 mm < DF < 2000 mm have been implemented and constitute the bulk of the training and validation datasets. Both finite element simulation settings (input), as well as the FI (output), have been utilized for the training of eight machine learning models, implemented with Python scripts. The results allow defining that the Gradient Boosting (GB) method is the most reliable for the FIOT prediction in forming processes, being its maximum and average errors equal to 9.03% and 3.18%, respectively. The trained ML models have been also applied to own and literature experimental cases, showing a maximum and average error equal to 8.00% and 5.70%, respectively, thus proving once again its reliability.

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Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10010042 ◽

2021 ◽

Vol 10 (1) ◽

pp. 42

Author(s):

Kieu Anh Nguyen ◽

Walter Chen ◽

Bor-Shiun Lin ◽

Uma Seeboonruang

Keyword(s):

Machine Learning ◽

Soil Erosion ◽

Ensemble Methods ◽

Machine Learning Algorithms ◽

Multivariate Adaptive Regression Splines ◽

Gradient Boosting ◽

Support Vector ◽

Ensemble Machine Learning ◽

Boosting Method ◽

Bagging Method

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.

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Predictors of outpatients’ no-show: big data analytics using apache spark

Journal Of Big Data ◽

10.1186/s40537-020-00384-9 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Tahani Daghistani ◽

Huda AlGhamdi ◽

Riyad Alshammari ◽

Raed H. AlHazme

Keyword(s):

Machine Learning ◽

Big Data ◽

Negative Impact ◽

Big Data Analytics ◽

Quality Of Healthcare ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Healthcare Organizations ◽

Data Framework ◽

Learning Techniques

AbstractOutpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813‬) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques.

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Learning from Imbalanced Educational Data Using Ensemble Machine Learning Algorithms

Webology ◽

10.14704/web/v18si01/web18053 ◽

2021 ◽

Vol 18 (Special Issue 01) ◽

pp. 183-195

Author(s):

Thingbaijam Lenin ◽

N. Chandrasekaran

Keyword(s):

Machine Learning ◽

Random Forest ◽

Missing Values ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Adaptive Boosting ◽

Stochastic Gradient Boosting ◽

Ensemble Machine Learning ◽

Learning Techniques ◽

Student’S Performance

Student’s academic performance is one of the most important parameters for evaluating the standard of any institute. It has become a paramount importance for any institute to identify the student at risk of underperforming or failing or even drop out from the course. Machine Learning techniques may be used to develop a model for predicting student’s performance as early as at the time of admission. The task however is challenging as the educational data required to explore for modelling are usually imbalanced. We explore ensemble machine learning techniques namely bagging algorithm like random forest (rf) and boosting algorithms like adaptive boosting (adaboost), stochastic gradient boosting (gbm), extreme gradient boosting (xgbTree) in an attempt to develop a model for predicting the student’s performance of a private university at Meghalaya using three categories of data namely demographic, prior academic record, personality. The collected data are found to be highly imbalanced and also consists of missing values. We employ k-nearest neighbor (knn) data imputation technique to tackle the missing values. The models are developed on the imputed data with 10 fold cross validation technique and are evaluated using precision, specificity, recall, kappa metrics. As the data are imbalanced, we avoid using accuracy as the metrics of evaluating the model and instead use balanced accuracy and F-score. We compare the ensemble technique with single classifier C4.5. The best result is provided by random forest and adaboost with F-score of 66.67%, balanced accuracy of 75%, and accuracy of 96.94%.

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Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)

PeerJ ◽

10.7717/peerj.9656 ◽

2020 ◽

Vol 8 ◽

pp. e9656

Author(s):

Sugandh Kumar ◽

Srinivas Patnaik ◽

Anshuman Dixit

Keyword(s):

Machine Learning ◽

Expression Profiles ◽

Disease Process ◽

Penalized Regression ◽

Functional Enrichment ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Support Vector ◽

Sequencing Data ◽

Therapeutic Modalities

Machine learning techniques are increasingly used in the analysis of high throughput genome sequencing data to better understand the disease process and design of therapeutic modalities. In the current study, we have applied state of the art machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine Radial Kernel (svmR), Adaptive Boost (AdaBoost), averaged Neural Network (avNNet), and Gradient Boosting Machine (GBM)) to stratify the HNSCC patients in early and late clinical stages (TNM) and to predict the risk using miRNAs expression profiles. A six miRNA signature was identified that can stratify patients in the early and late stages. The mean accuracy, sensitivity, specificity, and area under the curve (AUC) was found to be 0.84, 0.87, 0.78, and 0.82, respectively indicating the robust performance of the generated model. The prognostic signature of eight miRNAs was identified using LASSO (least absolute shrinkage and selection operator) penalized regression. These miRNAs were found to be significantly associated with overall survival of the patients. The pathway and functional enrichment analysis of the identified biomarkers revealed their involvement in important cancer pathways such as GP6 signalling, Wnt signalling, p53 signalling, granulocyte adhesion, and dipedesis. To the best of our knowledge, this is the first such study and we hope that these signature miRNAs will be useful for the risk stratification of patients and the design of therapeutic modalities.

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Short- and Medium-range Prediction of Relativistic Electron Flux in the Earth’s Outer Radiation Belt by Machine Learning Methods

Meteorologiya i Gidrologiya ◽

10.52002/0130-2906-2021-3-47-57 ◽

2021 ◽

Vol 3 ◽

pp. 47-57

Author(s):

I. N. Myagkova ◽

◽

V. R. Shirokii ◽

Yu. S. Shugai ◽

O. G. Barinov ◽

...

Keyword(s):

Machine Learning ◽

Radiation Belt ◽

Gradient Boosting ◽

Relativistic Electrons ◽

Learning Methods ◽

Outer Radiation Belt ◽

Machine Learning Methods ◽

The Earth ◽

Skill Scores ◽

Medium Range

The ways are studied to improve the quality of prediction of the time series of hourly mean fluxes and daily total fluxes (fluences) of relativistic electrons in the outer radiation belt of the Earth 1 to 24 hours ahead and 1 to 4 days ahead, respectively. The prediction uses an approximation approach based on various machine learning methods, namely, artificial neural networks (ANNs), decision tree (random forest), and gradient boosting. A comparison of the skill scores of short-range forecasts with the lead time of 1 to 24 hours showed that the best results were demonstrated by ANNs. For medium-range forecasting, the accuracy of prediction of the fluences of relativistic electrons in the Earth’s outer radiation belt three to four days ahead increases significantly when the predicted values of the solar wind velocity near the Earth obtained from the UV images of the Sun of the AIA (Atmospheric Imaging Assembly) instrument of the SDO (Solar Dynamics Observatory) are included to the list of the input parameters.

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Understanding Multi-Vehicle Collision Patterns on Freeways—A Machine Learning Approach

Infrastructures ◽

10.3390/infrastructures5080062 ◽

2020 ◽

Vol 5 (8) ◽

pp. 62

Author(s):

Clint Morris ◽

Jidong J. Yang

Keyword(s):

Machine Learning ◽

Statistical Methods ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Learning Approaches ◽

Crash Analysis ◽

Suitable Alternative ◽

Crash Data ◽

Extreme Gradient Boosting ◽

Modern Machine

Generating meaningful inferences from crash data is vital to improving highway safety. Classic statistical methods are fundamental to crash data analysis and often regarded for their interpretability. However, given the complexity of crash mechanisms and associated heterogeneity, classic statistical methods, which lack versatility, might not be sufficient for granular crash analysis because of the high dimensional features involved in crash-related data. In contrast, machine learning approaches, which are more flexible in structure and capable of harnessing richer data sources available today, emerges as a suitable alternative. With the aid of new methods for model interpretation, the complex machine learning models, previously considered enigmatic, can be properly interpreted. In this study, two modern machine learning techniques, Linear Discriminate Analysis and eXtreme Gradient Boosting, were explored to classify three major types of multi-vehicle crashes (i.e., rear-end, same-direction sideswipe, and angle) occurred on Interstate 285 in Georgia. The study demonstrated the utility and versatility of modern machine learning methods in the context of crash analysis, particularly in understanding the potential features underlying different crash patterns on freeways.

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