scholarly journals Modelling Using Machine Learning Models and Prediction of Monthly Global Irradiation

2021 ◽  
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ann Model ◽  
Learning Models ◽  
Machine Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Machine Learning Models

Different machine learning models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to predict the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best machine model was checked in two independent stations. The results obtained confirmed that the best ML methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 122.6·10kJ/(m2∙day) and 113.6·10kJ/(m2∙day), respectively, and predict conveniently for independent stations, 201.3·10kJ/(m2∙day) and 209.4·10kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

scholarly journals Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2332
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Prediction Models ◽  
Meteorological Station ◽  
Ann Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

scholarly journals A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models

2021 ◽  
Vol 11 (19) ◽  
pp. 9296
Author(s):  
Talha Mahboob Alam ◽  
Mubbashar Mushtaq ◽  
Kamran Shaukat ◽  
Ibrahim A. Hameed ◽  
Muhammad Umer Sarwar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Feature Selection ◽  
Artificial Neural Networks ◽  
Performance Indicators ◽  
Key Performance Indicators ◽  
Educational Institutions ◽  
Institutional Performance ◽  
Learning Models ◽  
Machine Learning Models

Lack of education is a major concern in underdeveloped countries because it leads to poor human and economic development. The level of education in public institutions varies across all regions around the globe. Current disparities in access to education worldwide are mostly due to systemic regional differences and the distribution of resources. Previous research focused on evaluating students’ academic performance, but less has been done to measure the performance of educational institutions. Key performance indicators for the evaluation of institutional performance differ from student performance indicators. There is a dire need to evaluate educational institutions’ performance based on their disparities and academic results on a large scale. This study proposes a model to measure institutional performance based on key performance indicators through data mining techniques. Various feature selection methods were used to extract the key performance indicators. Several machine learning models, namely, J48 decision tree, support vector machines, random forest, rotation forest, and artificial neural networks were employed to build an efficient model. The results of the study were based on different factors, i.e., the number of schools in a specific region, teachers, school locations, enrolment, and availability of necessary facilities that contribute to school performance. It was also observed that urban regions performed well compared to rural regions due to the improved availability of educational facilities and resources. The results showed that artificial neural networks outperformed other models and achieved an accuracy of 82.9% when the relief-F based feature selection method was used. This study will help support efforts in governance for performance monitoring, policy formulation, target-setting, evaluation, and reform to address the issues and challenges in education worldwide.

scholarly journals Predicting fire resistance ratings of timber structures using artificial neural networks

2020 ◽  
Vol 14 (2) ◽  
pp. 28-39
Author(s):  
Pham Thanh Tung ◽  
Pham Thanh Hung
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Artificial Neural Networks ◽  
Fire Resistance ◽  
Input Parameter ◽  
Training Algorithms ◽  
Ann Model ◽  
Wooden Floor ◽  
Artificial Neural ◽  
Input Variables

This paper describes a method to predict the fire resistance ratings of the wooden floor assemblies using Artificial Neural Networks. Experimental data collected from the previously published reports were used to train, validate, and test the proposed ANN model. A series of model configurations were examined using different popular training algorithms to obtain the optimal structure for the model. It is shown that the proposed ANN model can successfully predict the fire resistance ratings of the wooden floor assemblies from the input variables with an average absolute error of four percent. Besides, the sensitivity analysis was conducted to explore the effects of the separate input parameter on the output. Results from analysis revealed that the fire resistance ratings are sensitive to the change of Applied Load (ALD) and the number of the Ceiling Finish Layer (CFL) input variables. On the other hand, the outputs are less sensitive to a variation of the Joist Type (JTY) parameter. Keywords: artificial neural networks; fire resistance; wooden floor assembly; sensitivity analysis.

scholarly journals Prediction of nitrate concentration in Danube River water by using artificial neural networks

2020 ◽  
Vol 20 (6) ◽  
pp. 2119-2132
Author(s):  
Lidija J. Stamenković ◽  
Sanja Mrazovac Kurilić ◽  
Vladanka Presburger Ulniković
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
River Water ◽  
Nitrate Concentration ◽  
Absolute Error ◽  
Quality Parameters ◽  
Danube River ◽  
Ann Model ◽  
Artificial Neural ◽  
Input Variables

Abstract This paper describes the development of a model based on artificial neural networks (ANN) which aims to predict the concentration of nitrates in river water. Another 26 water quality parameters were also monitored and used as input parameters. The models were trained and tested with data from ten monitoring stations on the Danube River, located in its course through Serbia, for the period from 2011 to 2016. Multilayer perceptron (MLP), standard three-layer network is used to develop models and two input variable selection techniques are used to reduce the number of input variables. The obtained results have shown the ability of ANN to predict the nitrate concentration in both developed models with a value of mean absolute error of 0.53 and 0.42 mg/L for the test data. Also, the application of IVS has contributed to reduce the number of input variables and to increase the performance of the model, especially in the case of variance inflation factor (VIF) analysis where the estimation of multicollinearity among variables and the elimination of excessive variables significantly influenced the prediction abilities of the ANN model, r – 0.91.

Wave downscaling using machine learning

2020 ◽  
Author(s):  
Sara Santamaria Aguilar ◽  
Thomas Wahl
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Wave Propagation ◽  
Artificial Neural Networks ◽  
Random Forest ◽  
Linear Regression ◽  
Significant Wave Height ◽  
Wave Climate ◽  
Learning Models ◽  
Machine Learning Models

<p>Future changes in the wind wave climate due to atmospheric changes can intensify present erosion and flood risk. Knowledge on both mean and extreme wave climate is necessary for understanding changes in sediment dynamics and flood events at the coastline. In order to assess potential wave changes, ensemble nearshore wave projections are required for covering   the entire range of wave conditions and also the large uncertainties related to future climate states. However, nearshore wave projections are not available for most coastal regions due to the excessive computational effort required for dynamically downscaling ensemble offshore wave data. As a result, the large relative contribution of waves to coastal flooding and erosion is commonly omitted in the assessment of those hazards. In this context, machine learning models can be an efficient tool for downscaling ensemble global wave projections if they are able to accurately simulate the non-linear processes of wave propagation due to their low computational requirements. Here, we analyse the performance of three machine learning methods, namely random forest, multivariate adaptive regression splines and artificial neural networks, for downscaling the wave climate along the coast of Florida. We further compare the performance of these three models to the multiple linear regression, which is a statistical model frequently used, although it does not account for the non-linearities associated with wave propagation processes. We find that the three machine learning models perform better than the multiple linear regression for all wave parameters (significant wave height, peak and mean periods, direction) along the entire coastline of Florida, which highlights the ability of these models to reproduce the non-linear wave propagation processes. Specifically, random forest shows the best performance and the lowest computational training times. In addition, this model shows a remarkably good performance in simulating the wave extreme events compared to the other models. By following a tree bagging approach, random forest can also provide confidence intervals and reduce the tuning process. The latter is one of the main disadvantages of the artificial neural networks, which also show a high performance for wave downscaling but require more training and tuning effort. Although the significant wave height and the periods can be simulated with very high accuracy (R<sup>2</sup> higher than 0.9 and 0.8 respectively), the wave direction is poorly simulated by all models due to its circular behaviour. We find that a transformation of the direction into sine and cosine can improve the model performance. Finally, we downscale an ensemble of global wave projections along the coast of Florida and assess potential changes in the wave climate of this region.   </p>

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

414 Deep Neural Networks: A Survey Tool for Obstructive Sleep Apnea Prediction

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A164-A164
Author(s):  
Pahnwat Taweesedt ◽  
JungYoon Kim ◽  
Jaehyun Park ◽  
Jangwoon Park ◽  
Munish Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Deep Neural Networks ◽  
Support Vector ◽  
Learning Models ◽  
Obstructive Sleep ◽  
Screening Questionnaires ◽  
Machine Learning Models

Abstract Introduction Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder with an estimation of one billion people. Full-night polysomnography is considered the gold standard for OSA diagnosis. However, it is time-consuming, expensive and is not readily available in many parts of the world. Many screening questionnaires and scores have been proposed for OSA prediction with high sensitivity and low specificity. The present study is intended to develop models with various machine learning techniques to predict the severity of OSA by incorporating features from multiple questionnaires. Methods Subjects who underwent full-night polysomnography in Torr sleep center, Texas and completed 5 OSA screening questionnaires/scores were included. OSA was diagnosed by using Apnea-Hypopnea Index ≥ 5. We trained five different machine learning models including Deep Neural Networks with the scaled principal component analysis (DNN-PCA), Random Forest (RF), Adaptive Boosting classifier (ABC), and K-Nearest Neighbors classifier (KNC) and Support Vector Machine Classifier (SVMC). Training:Testing subject ratio of 65:35 was used. All features including demographic data, body measurement, snoring and sleepiness history were obtained from 5 OSA screening questionnaires/scores (STOP-BANG questionnaires, Berlin questionnaires, NoSAS score, NAMES score and No-Apnea score). Performance parametrics were used to compare between machine learning models. Results Of 180 subjects, 51.5 % of subjects were male with mean (SD) age of 53.6 (15.1). One hundred and nineteen subjects were diagnosed with OSA. Area Under the Receiver Operating Characteristic Curve (AUROC) of DNN-PCA, RF, ABC, KNC, SVMC, STOP-BANG questionnaire, Berlin questionnaire, NoSAS score, NAMES score, and No-Apnea score were 0.85, 0.68, 0.52, 0.74, 0.75, 0.61, 0.63, 0,61, 0.58 and 0,58 respectively. DNN-PCA showed the highest AUROC with sensitivity of 0.79, specificity of 0.67, positive-predictivity of 0.93, F1 score of 0.86, and accuracy of 0.77. Conclusion Our result showed that DNN-PCA outperforms OSA screening questionnaires, scores and other machine learning models. Support (if any):

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