Decision support: using machine learning through MATLAB to analyze environmental data

Nadler, David W.

doi:10.1007/s13412-019-00558-9

Cited by 5 publications

(5 citation statements)

References 32 publications

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“…We automatically performed all trained models and compared all of them (i.e., nonlinear regression models such as Decision/regression Trees, Support Vector Machines, Gaussian Progress Regression models, Kernel Approximation Regression Models, Ensembles of Trees, and Neural Networks) presented in Table 1. Then, we can nd the best model that is not under/over tted 23,54 . Our predictors/input parameters are…”

Section: Methodsmentioning

confidence: 99%

“…The machine learning or deep learning technique has two main parts (i.e., classi cation and regression model). There are many types of classi cation and regression models 23 and so far they have been applied to many elds of studies, e.g., for environmental forecasting (e.g., weather, res, ood and water contamination, landslides) 23 ; for facial, voice recognition, or tra c classi cation 24,25 ; for predicting reservoir properties (e.g., saturation or pore pressure) 26,27 . In this study, we considered using limited data (seismometer and well logs) to estimate the spatial geothermal structure (i.e., velocity and temperature).…”

Section: Introductionmentioning

confidence: 99%

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Ambient Noise Tomography and Machine Learning Models to Reveal Geothermal Structure in the Taupo Volcanic Zone

Chhun,

Tsuji

2023

Preprint

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To reveal potential geothermal fluid flows and temperature distribution in the Taupo Volcanic Zone, North Island of the New Zealand Hikurangi subduction zone, we analyzed seismometer and well log data. First, we extracted Rayleigh phase velocity dispersion curves from ambient noise cross-correlation analysis and then estimated S-wave velocity models through 3D surface wave tomography. Second, we constructed the 3D temperature model derived from our machine learning models using 3D velocity and temperature log data. Faulted/fractured zones, which can host fluids or magma, led to significant reductions in S-wave velocity within the subsurface. As a result, our S-wave velocities were lower in existing geothermal reservoirs and through flow pathways (i.e., active fault zones), particularly within the NE-SW directional structure toward Lake Taupo. The most suitable 3D temperature model (or others) was obtained based on the Gaussian process regression model, compared to other models in all machine learning algorithms. High-temperature areas ranging up to 300 °C or more are consistent with flow paths through the structure. Our approach could contribute to the unrevealed geothermal structures and fluid flow pathways in this region.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ambient Noise Tomography and Machine Learning Models to Reveal Geothermal Structure in the Taupo Volcanic Zone

Chhun,

Tsuji

2023

Preprint

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“…The study [12] shows the mental health analysis based on human stress issues in COVID'19 pandemic. The research study of [15] [16] is dedicated to finding MBTI personality types of social media users with reference to their content and posts.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Approach for Personality Type Identification using MBTI Framework

Shafi¹,

Sikander²,

Jamal³

et al. 2021

JISR-C

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In recent times, the prediction of personality traits with automated and programmed systems has caught human attention. Specifically, the use of multimodal data to predict personality types is the most considerable talk in artificial intelligence. There are a variety of techniques and methods available for personality type identification. The most popular and highly used personality type identifier is the Myers Briggs Type Indicator (MBTI) type indicator among all methods. In this paper, an exhaustive comparative analysis of all machine learning classical algorithms implementing the MBTI framework will be presented by giving a numerical and graphical representation of performance measures. To experience this study, a supervised machine learning approach is used to perform and analyze different classifiers using the phenomena of MBTI. The models are learned from a dataset to make predictions. The results show that the Ensemble Bagged Trees algorithm gives an overall good training accuracy of 98.4% and test accuracy of 70.75% at a moderate prediction speed of 11 K - Obs/sec by taking a training time of 14 sec. Other than that Coarse Tree algorithm in training time is 0.94009/sec and prediction speed 390 (K - Obs/sec), Fine KNN and Weighted KNN algorithm in training accuracy of 99.20% and Ensemble Boosted Trees algorithm in testing accuracy of 75.51% shows the efficient outcome respectively.

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“…MATLAB was used for development, study, and design through simulation, application development, data processing, analysis, and mathematical computations [30], [31]. Classification Learner App from this software was used to train the data gathered from the samples.…”

Section: Classificationmentioning

confidence: 99%

Discrimination of civet coffee using visible spectroscopy

Adier

Reyes

Arboleda

2020

Jurnal Teknologi dan Sistem Komputer

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Civet coffee is considered as highly marketable and rare. This specialty coffee has a special flavor and higher price relative to regular coffee, and it is restricted in supply. Establishing a straightforward and efficient approach to distinguish Civet coffee for quality; likewise, consumer protection is fundamental. This study utilized visible spectroscopy as a non-destructive and quick technique to obtain the absorbance, ranging from 450 nm to 650 nm, of the civet coffee and non-civet coffee samples. Overall, 160 samples were analyzed, and the total spectra accumulated was 960. The data gathered from the first 120 samples were fed to the classification learner application and were used as a training data set. The remaining samples were used for testing the classification algorithm. The study shows that civet coffee bean samples have lower absorbance values in visible spectra than non-civet coffee bean samples. The process yields 96.7 % to 100 % classification scores for quadratic discriminant analysis and logistic regression. Among the two classification algorithms, logistic regression generated the fastest training time of 14.050 seconds. The application of visible spectroscopy combined with data mining algorithms is effective in discriminating civet coffee from non-civet coffee.

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Decision support: using machine learning through MATLAB to analyze environmental data

Cited by 5 publications

References 32 publications

Ambient Noise Tomography and Machine Learning Models to Reveal Geothermal Structure in the Taupo Volcanic Zone

Ambient Noise Tomography and Machine Learning Models to Reveal Geothermal Structure in the Taupo Volcanic Zone

A Machine Learning Approach for Personality Type Identification using MBTI Framework

Discrimination of civet coffee using visible spectroscopy

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