TermPicks: A century of Greenland glacier terminus data for use in machine learning applications

Abstract. Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of time scales. Numerous studies have digitized termini manually, but this process is labor-intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and SAR satellite sensors. These techniques rely on high quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39,060 individual terminus traces for 278 glaciers with a mean and median number of traces per glacier of 136 ± 190 and 93, respectively. Across all glaciers, 32,567 dates have been picked, of which 4,467 have traces from more than one author (duplication of 14 %). We find a median error of ∼100 m among manually-traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of The Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet.

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Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200904164539 ◽

2020 ◽

Vol 13 ◽

Author(s):

Ritu Khandelwal ◽

Hemlata Goyal ◽

Rajveer Singh Shekhawat

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Data Science ◽

Training Data ◽

Machine Learning Techniques ◽

Future Trends ◽

Data Set ◽

Learning Stage ◽

Learning Techniques ◽

Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

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Synthetic Sonic Log Generation With Machine Learning: A Contest Summary From Five Methods

Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description ◽

10.30632/pjv62n4-2021a4 ◽

2021 ◽

Vol 62 (4) ◽

pp. 393-406

Author(s):

Yanxiang Yu ◽

◽

Chicheng Xu ◽

Siddharth Misra ◽

Weichang Li ◽

...

Keyword(s):

Machine Learning ◽

Test Data ◽

Short Term Memory ◽

Rock Physics ◽

Training Data ◽

Machine Learning Techniques ◽

Blind Test ◽

Data Set ◽

Benchmark Model ◽

Sonic Log

Compressional and shear sonic traveltime logs (DTC and DTS, respectively) are crucial for subsurface characterization and seismic-well tie. However, these two logs are often missing or incomplete in many oil and gas wells. Therefore, many petrophysical and geophysical workflows include sonic log synthetization or pseudo-log generation based on multivariate regression or rock physics relations. Started on March 1, 2020, and concluded on May 7, 2020, the SPWLA PDDA SIG hosted a contest aiming to predict the DTC and DTS logs from seven “easy-to-acquire” conventional logs using machine-learning methods (GitHub, 2020). In the contest, a total number of 20,525 data points with half-foot resolution from three wells was collected to train regression models using machine-learning techniques. Each data point had seven features, consisting of the conventional “easy-to-acquire” logs: caliper, neutron porosity, gamma ray (GR), deep resistivity, medium resistivity, photoelectric factor, and bulk density, respectively, as well as two sonic logs (DTC and DTS) as the target. The separate data set of 11,089 samples from a fourth well was then used as the blind test data set. The prediction performance of the model was evaluated using root mean square error (RMSE) as the metric, shown in the equation below: RMSE=sqrt(1/2*1/m* [∑_(i=1)^m▒〖(〖DTC〗_pred^i-〖DTC〗_true^i)〗^2 + 〖(〖DTS〗_pred^i-〖DTS〗_true^i)〗^2 ] In the benchmark model, (Yu et al., 2020), we used a Random Forest regressor and conducted minimal preprocessing to the training data set; an RMSE score of 17.93 was achieved on the test data set. The top five models from the contest, on average, beat the performance of our benchmark model by 27% in the RMSE score. In the paper, we will review these five solutions, including preprocess techniques and different machine-learning models, including neural network, long short-term memory (LSTM), and ensemble trees. We found that data cleaning and clustering were critical for improving the performance in all models.

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A sentiment analysis system for social media using machine learning techniques: Social enablement

Digital Scholarship in the Humanities ◽

10.1093/llc/fqy037 ◽

2018 ◽

Vol 34 (3) ◽

pp. 569-581 ◽

Cited By ~ 1

Author(s):

Sujata Rani ◽

Parteek Kumar

Keyword(s):

Machine Learning ◽

Social Media ◽

Sentiment Analysis ◽

Media Analysis ◽

Training Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Analysis Tool ◽

Data Set ◽

Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

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Water Quality Prediction Using Statistical Tool and Machine Learning Algorithm

Waste Management ◽

10.4018/978-1-7998-1210-4.ch029 ◽

2020 ◽

pp. 609-623

Author(s):

Arun Kumar Beerala ◽

Gobinath R. ◽

Shyamala G. ◽

Siribommala Manvitha

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Training Data ◽

Machine Learning Techniques ◽

Statistical Tool ◽

Data Set ◽

Water Quality Prediction ◽

Living Things ◽

Sampling Locations ◽

Different Seasons

Water is the most valuable natural resource for all living things and the ecosystem. The quality of groundwater is changed due to change in ecosystem, industrialisation, and urbanisation, etc. In the study, 60 samples were taken and analysed for various physio-chemical parameters. The sampling locations were located using global positioning system (GPS) and were taken for two consecutive years for two different seasons, monsoon (Nov-Dec) and post-monsoon (Jan-Mar). In 2016-2017 and 2017-2018 pH, EC, and TDS were obtained in the field. Hardness and Chloride are determined using titration method. Nitrate and Sulphate were determined using Spectrophotometer. Machine learning techniques were used to train the data set and to predict the unknown values. The dominant elements of groundwater are as follows: Ca2, Mg2 for cation and Cl-, SO42, NO3− for anions. The regression value for the training data set was found to be 0.90596, and for the entire network, it was found to be 0.81729. The best performance was observed as 0.0022605 at epoch 223.

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Rotor Unbalance Kind and Severity Identification by Current Signature Analysis with Adaptative Update to Multiclass Machine Learning Algorithms

Studies in Engineering and Technology ◽

10.11114/set.v8i1.5213 ◽

2021 ◽

Vol 8 (1) ◽

pp. 28

Author(s):

S. L. Ávila ◽

H. M. Schaberle ◽

S. Youssef ◽

F. S. Pacheco ◽

C. A. Penz

Keyword(s):

Machine Learning ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Signature Analysis ◽

Data Set ◽

Learning Techniques ◽

Environmental Variations ◽

Current Signature

The health of a rotating electric machine can be evaluated by monitoring electrical and mechanical parameters. As more information is available, it easier can become the diagnosis of the machine operational condition. We built a laboratory test bench to study rotor unbalance issues according to ISO standards. Using the electric stator current harmonic analysis, this paper presents a comparison study among Support-Vector Machines, Decision Tree classifies, and One-vs-One strategy to identify rotor unbalance kind and severity problem – a nonlinear multiclass task. Moreover, we propose a methodology to update the classifier for dealing better with changes produced by environmental variations and natural machinery usage. The adaptative update means to update the training data set with an amount of recent data, saving the entire original historical data. It is relevant for engineering maintenance. Our results show that the current signature analysis is appropriate to identify the type and severity of the rotor unbalance problem. Moreover, we show that machine learning techniques can be effective for an industrial application.

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Application of Machine Learning Techniques As a Means of Mooring Integrity Monitoring

Volume 3: Structures, Safety, and Reliability ◽

10.1115/omae2019-96411 ◽

2019 ◽

Author(s):

Jonathan M. Gumley ◽

Hayden Marcollo ◽

Stuart Wales ◽

Andrew E. Potts ◽

Christopher J. Carra

Keyword(s):

Machine Learning ◽

Data Science ◽

Single Point ◽

Original System ◽

Training Data ◽

Machine Learning Techniques ◽

Mooring Line ◽

Artificial Noise ◽

Data Set ◽

Learning Techniques

Abstract There is growing importance in the offshore floating production sector to develop reliable and robust means of continuously monitoring the integrity of mooring systems for FPSOs and FPUs, particularly in light of the upcoming introduction of API-RP-2MIM. Here, the limitations of the current range of monitoring techniques are discussed, including well established technologies such as load cells, sonar, or visual inspection, within the context of the growing mainstream acceptance of data science and machine learning. Due to the large fleet of floating production platforms currently in service, there is a need for a readily deployable solution that can be retrofitted to existing platforms to passively monitor the performance of floating assets on their moorings, for which machine learning based systems have particular advantages. An earlier investigation conducted in 2016 on a shallow water, single point moored FPSO employed host facility data from in-service field measurements before and after a single mooring line failure event. This paper presents how the same machine learning techniques were applied to a deep water, semi taut, spread moored system where there was no host facility data available, therefore requiring a calibrated hydrodynamic numerical model to be used as the basis for the training data set. The machine learning techniques applied to both real and synthetically generated data were successful in replicating the response of the original system, even with the latter subjected to different variations of artificial noise. Furthermore, utilizing a probability-based approach, it was demonstrated that replicating the response of the underlying system was a powerful technique for predicting changes in the mooring system.

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IcePicks: a collaborative database of Greenland outlet glacier termini

10.5194/egusphere-egu2020-11237 ◽

2020 ◽

Author(s):

Sophie Goliber ◽

Taryn Black ◽

Ginny Catania

Keyword(s):

Training Data ◽

Aerial Imagery ◽

Machine Learning Techniques ◽

Time Machine ◽

Outlet Glacier ◽

Glacier Terminus ◽

Learning Techniques ◽

Automatic Picking ◽

Satellite Sensors ◽

Box Method

<p>Marine-terminating outlet glacier terminus change mapped from satellite and aerial imagery in Greenland is used extensively in understanding how outlet glaciers adjust to climatic changes over a range of time scales. Numerous studies have digitized termini manually, but this process is labor-intensive and may lead to duplication of efforts. Additionally, these studies use different methods to pick the front (e.g. centerline pick, whole delineation, box method), which makes them difficult to compare. At the same time, machine learning techniques are rapidly making progress in their ability to accurately automate the extraction of glacier termini, with promising developments across a number of satellite sensors. However, limitations still exist: in particular, further high-quality manually-digitized training data are needed to make robust automatic picks. Here we present efforts to produce a database of manually digitized terminus picks and an intercomparison of picking techniques to determine errors and best practices for future efforts in digitization. These data will be cleaned, associated with appropriate metadata, and compiled so they can be easily accessed by scientists. Ultimately, these data will be used to create training data for further automatic picking efforts. We hope to solicit further collaboration with members of EGU and encourage those interested to email the authors.</p>

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Auroral Classification Ergonomics and the Implications for Machine Learning

10.5194/gi-2019-41 ◽

2020 ◽

Author(s):

Derek McKay ◽

Andreas Kvammen

Keyword(s):

Machine Learning ◽

Large Data ◽

Research Community ◽

Training Data ◽

Machine Learning Techniques ◽

Data Set ◽

Potential Source ◽

Learning Techniques ◽

Training Samples ◽

Learning Research

Abstract. The machine learning research community has focused greatly on bias in algorithms and have identified different manifestations of it. Bias in the training samples is recognised as a potential source of prejudice in machine learning. It can be introduced by human experts who define the training sets. As machine learning techniques are being applied to auroral classification, it is important to identify and address potential sources of expert-injected bias. In an ongoing study, 13 947 auroral images were manually classified with significant differences between classifications. This large data set allowed identification of some of these biases, especially those originating as a result of the ergonomics of the classification process. These findings are presented in this paper, to serve as a checklist for improving training data integrity, not just for expert classifications, but also for crowd-sourced, citizen science projects. As the application of machine learning techniques to auroral research is relatively new, it is important that biases are identified and addressed before they become endemic in the corpus of training data.

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Significance of Artificial Intelligence and Machine Learning Techniques in Smart Cloud Computing: A Review

International Journal of Soft Computing and Engineering - Regular Issue ◽

10.35940/ijsce.c3265.099319 ◽

2019 ◽

Vol 9 (3) ◽

pp. 1-7

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Cloud Computing ◽

Reinforcement Learning ◽

Heuristic Algorithms ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Techniques ◽

Data Set ◽

Learning Techniques

Realization of the tremendous features and facilities provided by Cloud Computing by the geniuses in the world of digital marketing increases its demand. As customer satisfaction is the manifest of this ever shining field, balancing its load becomes a major issue. Various heuristic and meta-heuristic algorithms were applied to get optimum solutions. The current era is much attracted with the provisioning of self-manageable, self-learnable, self-healable, and self-configurable smart systems. To get self-manageable Smart Cloud, various Artificial Intelligence and Machine Learning (AI-ML) techniques and algorithms are revived. In this review, recent trend in the utilization of AI-ML techniques, their applied areas, purpose, their merits and demerits are highlighted. These techniques are further categorized as instance-based machine learning algorithms and reinforcement learning techniques based on their ability of learning. Reinforcement learning is preferred when there is no training data set. It leads the system to learn by its own experience itself even in dynamic environment.

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