scholarly journals Machine Learning to Estimate Surface Roughness from Satellite Images

2021 ◽  
Vol 13 (19) ◽  
pp. 3794
Author(s):  
Abhilash Singh ◽  
Kumar Gaurav ◽  
Atul Kumar Rai ◽  
Zafar Beg
Keyword(s):  
Machine Learning ◽  
Surface Roughness ◽  
Mean Square Error ◽  
Ensemble Learning ◽  
Satellite Images ◽  
Information Criterion ◽  
Learning Model ◽  
Mean Square ◽  
Radar Backscatter ◽  
Machine Learning Model

We apply the Support Vector Regression (SVR) machine learning model to estimate surface roughness on a large alluvial fan of the Kosi River in the Himalayan Foreland from satellite images. To train the model, we used input features such as radar backscatter values in Vertical–Vertical (VV) and Vertical–Horizontal (VH) polarisation, incidence angle from Sentinel-1, Normalised Difference Vegetation Index (NDVI) from Sentinel-2, and surface elevation from Shuttle Radar Topographic Mission (SRTM). We generated additional features (VH/VV and VH–VV) through a linear data fusion of the existing features. For the training and validation of our model, we conducted a field campaign during 11–20 December 2019. We measured surface roughness at 78 different locations over the entire fan surface using an in-house-developed mechanical pin-profiler. We used the regression tree ensemble approach to assess the relative importance of individual input feature to predict the surface soil roughness from SVR model. We eliminated the irrelevant input features using an iterative backward elimination approach. We then performed feature sensitivity to evaluate the riskiness of the selected features. Finally, we applied the dimension reduction and scaling to minimise the data redundancy and bring them to a similar level. Based on these, we proposed five SVR methods (PCA-NS-SVR, PCA-CM-SVR, PCA-ZM-SVR, PCA-MM-SVR, and PCA-S-SVR). We trained and evaluated the performance of all variants of SVR with a 60:40 ratio using the input features and the in-situ surface roughness. We compared the performance of SVR models with six different benchmark machine learning models (i.e., Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Binary Decision Tree (BDT), Bragging Ensemble Learning, Boosting Ensemble Learning, and Automated Machine Learning (AutoML)). We observed that the PCA-MM-SVR perform better with a coefficient of correlation (R = 0.74), Root Mean Square Error (RMSE = 0.16 cm), and Mean Square Error (MSE = 0.025 cm2). To ensure a fair selection of the machine learning model, we evaluated the Akaike’s Information Criterion (AIC), corrected AIC (AICc), and Bayesian Information Criterion (BIC). We observed that SVR exhibits the lowest values of AIC, corrected AIC, and BIC of all the other methods; this indicates the best goodness-of-fit. Eventually, we also compared the result of PCA-MM-SVR with the surface roughness estimated from different empirical and semi-empirical radar backscatter models. The accuracy of the PCA-MM-SVR model is better than the backscatter models. This study provides a robust approach to measure surface roughness at high spatial and temporal resolutions solely from the satellite data.

Machine learning to estimate surface roughness from satellite images

2021 ◽  
Author(s):  
Abhilash Singh ◽  
Kumar Gaurav
Keyword(s):  
Machine Learning ◽  
Surface Roughness ◽  
Ensemble Learning ◽  
Satellite Images ◽  
Surface Soil ◽  
Information Criterion ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Landsat 8 ◽  
Learning Techniques

<p>Soil surface attributes (mainly surface roughness and soil moisture) play a critical role in land-atmosphere interaction and have several applications in agriculture, hydrology, meteorology, and climate change studies. This study explores the potential of different machine learning algorithms (Support Vector Regression (SVR), Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Binary Decision Tree (BDT), Bragging Ensemble Learning, and Boosting Ensemble Learning) to estimate the surface soil roughness from Synthetic Aperture Radar (SAR) and optical satellite images in an alluvial megafan of the Kosi River in northern India. In a field campaign during 10-21 December 2019, we measured the surface soil roughness at 78 different locations using a mechanical pin-meter. The average value of the in-situ surface roughness is 1.8 cm. Further, at these locations, we extract the multiple features (backscattering coefficients, incidence angle, Normalised Difference Vegetation Index, and surface elevation) from Sentinel-1 A/B, LANDSAT-8 and SRTM data. We then trained and evaluated (in 60:40 ratio) the performance of all the regression-based machine learning techniques. </p><p>We found that SVR method performs exceptionally well over other methods with (R= 0.74, RMSE=0.16 cm, and MSE=0.025 cm<sup>2</sup>). To ensure a fair selection of machine learning techniques, we have calculated some additional criteria that include Akaike’s Information Criterion (AIC), corrected AIC and Bayesian Information Criterion (BIC). On comparing, we observed that SVR exhibits the lowest values of AIC, corrected AIC and BIC amongst all other methods, indicating best goodness-of-fit.</p>

scholarly journals Surface Roughness Prediction using Machine Learning Algorithms while Turning under Different Lubrication Conditions

2021 ◽  
Vol 2070 (1) ◽  
pp. 012243
Author(s):  
A Varun ◽  
Mechiri Sandeep Kumar ◽  
Karthik Murumulla ◽  
Tatiparthi Sathvik
Keyword(s):  
Machine Learning ◽  
Surface Roughness ◽  
Small Businesses ◽  
Cooling System ◽  
Gaussian Process Regression ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Nano Particles ◽  
Machining Parameters ◽  
Machine Learning Model

Abstract Lathe turning is one of the manufacturing sector’s most basic and important operations. From small businesses to large corporations, optimising machining operations is a key priority. Cooling systems in machining have an important role in determining surface roughness. The machine learning model under discussion assesses the surface roughness of lathe turned surfaces for a variety of materials. To forecast surface roughness, the machine learning model is trained using machining parameters, material characteristics, tool properties, and cooling conditions such as dry, MQL, and hybrid nano particle mixed MQL. Mixing with appropriate nano particles such as copper, aluminium, etc. may significantly improve cooling system heat absorption. To create a data collection for training and testing the model, many standard journals and publications are used. Surface roughness varies with work parameter combinations. In MATLAB, a Gaussian Process Regression (GPR) method will be utilised to construct a model and predict surface roughness. To improve prediction outcomes and make the model more flexible, data from a variety of publications was included. Some characteristics were omitted in order to minimise data noise. Different statistical factors will be explored to predict surface roughness.

scholarly journals Probable Forecasting of Epidemic COVID-19 in Using COCUDE Model for the State of Tamilnadu, India

2020 ◽  
Author(s):  
Prasannavenkatesan Theerthagiri
Keyword(s):  
Mean Square Error ◽  
Akaike Information Criterion ◽  
Virus Disease ◽  
Research Work ◽  
Information Criterion ◽  
Mean Square ◽  
Error Metrics ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Performance Error

Abstract The world has been struck due to the dangerous human threat called Corona Virus Disease 2019. This research work proposes a methodology to encounter the future infection rate, curing rate, and decease rate. This uses the artificial intelligence algorithm to design and develop the proposed confirmed, cured, deceased (COCUDE) model. A machine learning model has been developed with several iterations to design the proposed COCUDE model. The Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Correlated Akaike Information criterion (AICc) metrics are analyzed to check the stationary and quality for the proposed COCUDE model. The prediction results are evaluated by the performance error metrics such as mean square error (MSE) and root mean square error (RMSE), in which the errors are lower for the proposed model. Thus, the prediction results indicate the proposed COCUDE model might accurately predict future COVID-19 infection rates. It might support the corresponding authorities to take the precautious action on the required necessities for the medical and clinical infrastructures and equipment.

scholarly journals Comparing machine learning and ensemble learning in the field of football

2019 ◽  
Vol 9 (5) ◽  
pp. 4321
Author(s):  
Shuaib Khan ◽  
Kirubanand V. B
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Learning Model ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Model ◽  
Vector Machines ◽  
The Right ◽  
Enormous Number ◽  
Machine Learning Models

Football has been one of the most popular and loved sports since its birth on November 6th, 1869. The main reason for this is because it is highly unpredictable in nature. Predicting football matches results seems like the perfect problem for machine learning models. But there are various caveats such as picking the right features from an enormous number of available features.  There have been many models which have been applied to various football-related datasets. This paper aims to compare Support Vector Machines a machine learning model and XGBoost an Ensemble learning model and how Ensemble Learning can greatly improve the accuracy of the predictions.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

Smart-ML: A System for Machine Learning Model Exploration using Pipeline Graph

2020 ◽  
Author(s):  
Dhaval Patel ◽  
Shrey Shrivastava ◽  
Wesley Gifford ◽  
Stuart Siegel ◽  
Jayant Kalagnanam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Machine Learning Model
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