MODIS-FIRMS and ground-truthing based wildfire likelihood mapping of Sikkim Himalaya using machine learning algorithms.

Tree Cover ◽

Anthropogenic Factors ◽

Gradient Boosting ◽

Support Vector ◽

Learning Methods ◽

Sikkim Himalaya ◽

Environmental Features ◽

Abstract Wildfires in limited extent and intensity can be a boon for the forest ecosystem. However, recent episodes of wildfires of 2019 in Australia and Brazil are sad reminders of their heavy ecological and economical costs. Understanding the role of environmental factors in the likelihood of wildfires in a spatial context would be instrumental in mitigating it. In this study, 14 environmental features encompassing meteorological, topographical, ecological, in situ and anthropogenic factors have been considered for preparing the wildfire likelihood map of Sikkim Himalaya. A comparative study on the efficiency of machine learning methods like Generalized Linear Model (GLM), Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting Model (GBM) has been performed to identify the best performing algorithm in wildfire prediction. The study indicates that all the machine learning methods are good at predicting wildfires. However, RF has outperformed, followed by GBM in the prediction. Also, environmental features like average temperature, average wind speed, proximity to roadways and tree cover percentage are the most important determinants of wildfires in Sikkim Himalaya. This study can be considered as a decision support tool for preparedness, efficient resource allocation and sensitization of people towards mitigation of wildfires in Sikkim.

Landslide susceptibility mapping using machine learning for Wenchuan County, Sichuan province, China

E3S Web of Conferences ◽

10.1051/e3sconf/202019803023 ◽

2020 ◽

Vol 198 ◽

pp. 03023

Author(s):

Xin Yang ◽

Rui Liu ◽

Luyao Li ◽

Mei Yang ◽

Yuantao Yang

Keyword(s):

Machine Learning ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Support Vector ◽

Roc Curve Analysis ◽

Learning Methods ◽

Boosted Decision Tree

Landslide susceptibility mapping is a method used to assess the probability and spatial distribution of landslide occurrences. Machine learning methods have been widely used in landslide susceptibility in recent years. In this paper, six popular machine learning algorithms namely logistic regression, multi-layer perceptron, random forests, support vector machine, Adaboost, and gradient boosted decision tree were leveraged to construct landslide susceptibility models with a total of 1365 landslide points and 14 predisposing factors. Subsequently, the landslide susceptibility maps (LSM) were generated by the trained models. LSM shows the main landslide zone is concentrated in the southeastern area of Wenchuan County. The result of ROC curve analysis shows that all models fitted the training datasets and achieved satisfactory results on validation datasets. The results of this paper reveal that machine learning methods are feasible to build robust landslide susceptibility models.

Science and Transport Progress Bulletin of Dnipropetrovsk National University of Railway Transport ◽

APPLICATION OF MACHINE LEARNING ALGORITHMS FOR PROCESSING COMMENTS FROM THE YOUTUBE VIDEO HOSTING UNDER TRAINING VIDEOS

10.15802/stp2020/225264 ◽

2021 ◽

pp. 33-42

Author(s):

L. S. Koriashkina ◽

H. V. Symonets

Keyword(s):

Machine Learning ◽

Gradient Descent ◽

Russian Language ◽

Stochastic Gradient ◽

Stochastic Gradient Descent ◽

Support Vector ◽

Learning Methods ◽

Gradient Enhancement

Purpose. Detecting toxic comments on YouTube video hosting under training videos by classifying unstructured text using a combination of machine learning methods. Methodology. To work with the specified type of data, machine learning methods were used for cleaning, normalizing, and presenting textual data in a form acceptable for processing on a computer. Directly to classify comments as “toxic”, we used a logistic regression classifier, a linear support vector classification method without and with a learning method – stochastic gradient descent, a random forest classifier and a gradient enhancement classifier. In order to assess the work of the classifiers, the methods of calculating the matrix of errors, accuracy, completeness and F-measure were used. For a more generalized assessment, a cross-validation method was used. Python programming language. Findings. Based on the assessment indicators, the most optimal methods were selected – support vector machine (Linear SVM), without and with the training method using stochastic gradient descent. The described technologies can be used to analyze the textual comments under any training videos to detect toxic reviews. Also, the approach can be useful for identifying unwanted or even aggressive information on social networks or services where reviews are provided. Originality. It consists in a combination of methods for preprocessing a specific type of text, taking into account such features as the possibility of having a timecode, emoji, links, and the like, as well as in the adaptation of classification methods of machine learning for the analysis of Russian-language comments. Practical value. It is about optimizing (simplification) the comment analysis process. The need for this processing is due to the growing volumes of text data, especially in the field of education through quarantine conditions and the transition to distance learning. The volume of educational Internet content already needs to automate the processing and analysis of feedback, over time this need will only grow.

Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam

Energies ◽

10.3390/en14227714 ◽

2021 ◽

Vol 14 (22) ◽

pp. 7714

Author(s):

Ha Quang Man ◽

Doan Huy Hien ◽

Kieu Duy Thong ◽

Bui Viet Dung ◽

Nguyen Minh Hoa ◽

...

Keyword(s):

Machine Learning ◽

Flow Unit ◽

Supervised Machine Learning ◽

Support Vector ◽

Learning Methods ◽

Log Data ◽

Hydraulic Flow ◽

Core Data ◽

The test study area is the Miocene reservoir of Nam Con Son Basin, offshore Vietnam. In the study we used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset. Then we applied supervised learning to predict HU by combining core and well log data. We tested several machine learning algorithms. In the first phase, we derived hydraulic flow unit clustering of porosity and permeability of core data using unsupervised machine learning methods such as Ward’s, K mean, Self-Organize Map (SOM) and Fuzzy C mean (FCM). Then we applied supervised machine learning methods including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF). We combined both core and log data to predict HU logs for the full well section of the wells without core data. We used four wells with six logs (GR, DT, NPHI, LLD, LSS and RHOB) and 578 cores from the Miocene reservoir to train, validate and test the data. Our goal was to show that the correct combination of cores and well logs data would provide reservoir engineers with a tool for HU classification and estimation of permeability in a continuous geological profile. Our research showed that machine learning effectively boosts the prediction of permeability, reduces uncertainty in reservoir modeling, and improves project economics.

Comparative Analysis of Machine Learning Algorithms for Multi-Syndrome Classification of Neurodegenerative Syndromes

10.21203/rs.3.rs-178965/v1 ◽

2021 ◽

Author(s):

Leonie Lampe ◽

Sebastian Niehaus ◽

Hans-Jürgen Huppertz ◽

Alberto Merola ◽

Janis Reinelt ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Performance Measures ◽

Careful Consideration ◽

Classification Task ◽

Gradient Boosting ◽

Support Vector ◽

Learning Methods

Abstract Importance The entry of artificial intelligence into medicine is pending. Several methods have been used for predictions of structured neuroimaging data, yet nobody compared them in this context.Objective Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.Design, Setting, and Participants Atlas-based volumetry was performed on multi-centric T1weighted MRI data from 940 subjects, i.e. 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.Interventions n.a.Main Outcomes and Measures Cohen’s Kappa, Accuracy, and F1-score to assess model performance.Results Over all, the neural network produced both the best performance measures as well as the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with wide-spread and rather weak atrophy.Conclusions and Relevance Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.Trial Registration n.a.

Using Machine Learning Methods To Identify Coal Pay Zones from Drilling and Logging-While-Drilling (LWD) Data

SPE Journal ◽

10.2118/198288-pa ◽

2020 ◽

Vol 25 (03) ◽

pp. 1241-1258 ◽

Cited By ~ 2

Author(s):

Ruizhi Zhong ◽

Raymond L. Johnson ◽

Zhongwei Chen

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Training Data ◽

Support Vector ◽

Learning Methods ◽

Well Completion ◽

Extreme Gradient Boosting ◽

Logging While Drilling

Summary Accurate coal identification is critical in coal seam gas (CSG) (also known as coalbed methane or CBM) developments because it determines well completion design and directly affects gas production. Density logging using radioactive source tools is the primary tool for coal identification, adding well trips to condition the hole and additional well costs for logging runs. In this paper, machine learning methods are applied to identify coals from drilling and logging-while-drilling (LWD) data to reduce overall well costs. Machine learning algorithms include logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGBoost). The precision, recall, and F1 score are used as evaluation metrics. Because coal identification is an imbalanced data problem, the performance on the minority class (i.e., coals) is limited. To enhance the performance on coal prediction, two data manipulation techniques [naive random oversampling (NROS) technique and synthetic minority oversampling technique (SMOTE)] are separately coupled with machine learning algorithms. Case studies are performed with data from six wells in the Surat Basin, Australia. For the first set of experiments (single-well experiments), both the training data and test data are in the same well. The machine learning methods can identify coal pay zones for sections with poor or missing logs. It is found that rate of penetration (ROP) is the most important feature. The second set of experiments (multiple-well experiments) uses the training data from multiple nearby wells, which can predict coal pay zones in a new well. The most important feature is gamma ray. After placing slotted casings, all wells have coal identification rates greater than 90%, and three wells have coal identification rates greater than 99%. This indicates that machine learning methods (either XGBoost or ANN/RF with NROS/SMOTE) can be an effective way to identify coal pay zones and reduce coring or logging costs in CSG developments.

ML-Based Analysis of Particle Distributions in High-Intensity Laser Experiments: Role of Binning Strategy

Entropy ◽

10.3390/e23010021 ◽

2020 ◽

Vol 23 (1) ◽

pp. 21

Author(s):

Yury Rodimkov ◽

Evgeny Efimenko ◽

Valentin Volokitin ◽

Elena Panova ◽

Alexey Polovinkin ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Quantum Electrodynamics ◽

Strong Field ◽

Experimental Studies ◽

Research Area ◽

Gradient Boosting ◽

Support Vector ◽

Learning Methods ◽

When entering the phase of big data processing and statistical inferences in experimental physics, the efficient use of machine learning methods may require optimal data preprocessing methods and, in particular, optimal balance between details and noise. In experimental studies of strong-field quantum electrodynamics with intense lasers, this balance concerns data binning for the observed distributions of particles and photons. Here we analyze the aspect of binning with respect to different machine learning methods (Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Fully-Connected Neural Network (FCNN), Convolutional Neural Network (CNN)) using numerical simulations that mimic expected properties of upcoming experiments. We see that binning can crucially affect the performance of SVM and GBT, and, to a less extent, FCNN and CNN. This can be interpreted as the latter methods being able to effectively learn the optimal binning, discarding unnecessary information. Nevertheless, given limited training sets, the results indicate that the efficiency can be increased by optimizing the binning scale along with other hyperparameters. We present specific measurements of accuracy that can be useful for planning of experiments in the specified research area.

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

Result and Performance Analysis of Rainfall Prediction System Based on Deep Neural Network

10.32628/cseit2063165 ◽

2020 ◽

pp. 633-638

Author(s):

Akshay Rajendra Naik ◽

A. V. Deorankar ◽

P. B. Ambhore

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Neural Network ◽

Support Vector ◽

Learning Methods ◽

Rainfall Prediction ◽

Vector Machines ◽

And Performance

Rainfall prediction is useful for all people for decision making in all fields, such as out door gamming, farming, traveling, and factory and for other activities. We studied various methods for rainfall prediction such as machine learning and neural networks. There is various machine learning algorithms are used in previous existing methods such as naïve byes, support vector machines, random forest, decision trees, and ensemble learning methods. We used deep neural network for rainfall prediction, and for optimization of deep neural network Adam optimizer is used for setting modal parameters, as a result our method gives better results as compare to other machine learning methods.

A Very Large-Scale Bioactivity Comparison of Deep Learning and Multiple Machine Learning Algorithms for Drug Discovery

10.26434/chemrxiv.12781241 ◽

2020 ◽

Author(s):

Thomas R. Lane ◽

Daniel H. Foil ◽

Eni Minerali ◽

Fabio Urbina ◽

Kimberley M. Zorn ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Drug Discovery ◽

Deep Neural Networks ◽

Learning Algorithms ◽

Support Vector ◽

Learning Methods ◽

Machine learning methods are attracting considerable attention from the pharmaceutical industry for use in drug discovery and applications beyond. In recent studies we have applied multiple machine learning algorithms, modeling metrics and in some cases compared molecular descriptors to build models for individual targets or properties on a relatively small scale. Several research groups have used large numbers of datasets from public databases such as ChEMBL in order to evaluate machine learning methods of interest to them. The largest of these types of studies used on the order of 1400 datasets. We have now extracted well over 5000 datasets from CHEMBL for use with the ECFP6 fingerprint and comparison of our proprietary software Assay CentralTM with random forest, k-Nearest Neighbors, support vector classification, naïve Bayesian, AdaBoosted decision trees, and deep neural networks (3 levels). Model performance <a>was</a> assessed using an array of five-fold cross-validation metrics including area-under-the-curve, F1 score, Cohen’s kappa and Matthews correlation coefficient. <a>Based on ranked normalized scores for the metrics or datasets all methods appeared comparable while the distance from the top indicated Assay CentralTM and support vector classification were comparable. </a>Unlike prior studies which have placed considerable emphasis on deep neural networks (deep learning), no advantage was seen in this case where minimal tuning was performed of any of the methods. If anything, Assay CentralTM may have been at a slight advantage as the activity cutoff for each of the over 5000 datasets representing over 570,000 unique compounds was based on Assay CentralTMperformance, but support vector classification seems to be a strong competitor. We also apply Assay CentralTM to prospective predictions for PXR and hERG to further validate these models. This work currently appears to be the largest comparison of machine learning algorithms to date. Future studies will likely evaluate additional databases, descriptors and algorithms, as well as further refining methods for evaluating and comparing models.

Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods

Energies ◽

10.3390/en12091680 ◽

2019 ◽

Vol 12 (9) ◽

pp. 1680 ◽

Cited By ~ 7

Author(s):

Moting Su ◽

Zongyi Zhang ◽

Ye Zhu ◽

Donglan Zha ◽

Wenying Wen

Keyword(s):

Machine Learning ◽

Natural Gas ◽

Data Driven ◽

Spot Price ◽

Support Vector ◽

Price Forecasting ◽

Learning Methods ◽

Gas Price

Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.

Estimating Surface Downward Longwave Radiation Using Machine Learning Methods

Atmosphere ◽

10.3390/atmos11111147 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1147 ◽

Cited By ~ 1

Author(s):

Chunjie Feng ◽

Xiaotong Zhang ◽

Yu Wei ◽

Weiyu Zhang ◽

Ning Hou ◽

...

Keyword(s):

Machine Learning ◽

Land Cover ◽

Time Scale ◽

Longwave Radiation ◽

Surface Radiation ◽

Gradient Boosting ◽

Support Vector ◽

Learning Methods ◽

Downward Longwave Radiation

The downward longwave radiation (Ld, 4–100 μm) is a major component of research for the surface radiation energy budget and balance. In this study, we applied five machine learning methods, namely artificial neural network (ANN), support vector regression (SVR), gradient boosting regression tree (GBRT), random forest (RF), and multivariate adaptive regression spline (MARS), to estimate Ld using ground measurements collected from 27 Baseline Surface Radiation Network (BSRN) stations. Ld measurements in situ were used to validate the accuracy of Ld estimation models on daily and monthly time scales. A comparison of the results demonstrated that the estimates on the basis of the GBRT method had the highest accuracy, with an overall root-mean-square error (RMSE) of 17.50 W m−2 and an R value of 0.96 for the test dataset on a daily time scale. These values were 11.19 W m−2 and 0.98, respectively, on a monthly time scale. The effects of land cover and elevation were further studied to comprehensively evaluate the performance of each machine learning method. All machine learning methods achieved better results over the grass land cover type but relatively worse results over the tundra. GBRT, RF, and MARS methods were found to show good performance at both the high- and low-altitude sites.