Estimating the Volcanic Ash Fall Rate from the Mount Sinabung Eruption on February 19, 2018 Using Weather Radar

2019 ◽  
Vol 14 (1) ◽  
pp. 135-150 ◽  
Author(s):  
Magfira Syarifuddin ◽  
Satoru Oishi ◽  
Ratih Indri Hapsari ◽  
Jiro Shiokawa ◽  
Hanggar Ganara Mawandha ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Elevation Angle ◽  
Training Data ◽  
Classification Model ◽  
Column Height ◽  
Depth Information ◽  
Fall Rate ◽  
Angular Range ◽  
Ash Fall ◽  
Reflectivity Factor

This paper presents a theoretical method for estimating volcanic ash fall rate from the eruption of Sinabung Volcano on February 19, 2018 using an X-band multi-parameter radar (X-MP radar). The X-MP radar was run in a sectoral range height indicator (SRHI) scan mode for 6° angular range (azimuth of 221°–226°) and at an elevation angle of 7° to 40° angular range. The distance of the radar is approximately 8 km in the Southeastern direction of the vent of Mount Sinabung. Based on a three-dimensional (3-D) image of the radar reflectivity factor, the ash column height was established to be more than 7.7 km, and in-depth information on detectable tephra could be obtained. This paper aims to present the microphysical parameters of volcanic ash measured by X-MP radar, which are the tephra concentration and the fall-out rate. These parameters were calculated in a two-step stepwise approach microphysical model using the scaled gamma distribution. The first step was ash classification based on a set of training data on synthetic ash and its estimated reflectivity factor. Using a naïve Bayesian classification, the measured reflectivity factors from the eruption were classified into the classification model. The second step was estimating the volcanic ash concentration and the fall-out rate by power-law function. The model estimated a maximum of approximately 12.9 g·m-3of ash concentration from the coarse ash class (mean diameterDn= 0.1 mm) and a minimum of approximately 0.8 megatons of volcanic ash mass accumulation from the eruption.

Fully automated contrast and non-contrast cardiac view detection in echocardiography a multi-centre, multi-vendor study

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Gao ◽  
D Stojanovski ◽  
A Parker ◽  
P Marques ◽  
S Heitner ◽  
...  
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Classification Model ◽  
Validation Dataset ◽  
Funding Source ◽  
Private Company ◽  
Validation Data ◽  
Independent Test ◽  
Model Training ◽  
Confusion Matrices

Abstract Background Correctly identifying views acquired in a 2D echocardiographic examination is paramount to post-processing and quantification steps often performed as part of most clinical workflows. In many exams, particularly in stress echocardiography, microbubble contrast is used which greatly affects the appearance of the cardiac views. Here we present a bespoke, fully automated convolutional neural network (CNN) which identifies apical 2, 3, and 4 chamber, and short axis (SAX) views acquired with and without contrast. The CNN was tested in a completely independent, external dataset with the data acquired in a different country than that used to train the neural network. Methods Training data comprised of 2D echocardiograms was taken from 1014 subjects from a prospective multisite, multi-vendor, UK trial with the number of frames in each view greater than 17,500. Prior to view classification model training, images were processed using standard techniques to ensure homogenous and normalised image inputs to the training pipeline. A bespoke CNN was built using the minimum number of convolutional layers required with batch normalisation, and including dropout for reducing overfitting. Before processing, the data was split into 90% for model training (211,958 frames), and 10% used as a validation dataset (23,946 frames). Image frames from different subjects were separated out entirely amongst the training and validation datasets. Further, a separate trial dataset of 240 studies acquired in the USA was used as an independent test dataset (39,401 frames). Results Figure 1 shows the confusion matrices for both validation data (left) and independent test data (right), with an overall accuracy of 96% and 95% for the validation and test datasets respectively. The accuracy for the non-contrast cardiac views of >99% exceeds that seen in other works. The combined datasets included images acquired across ultrasound manufacturers and models from 12 clinical sites. Conclusion We have developed a CNN capable of automatically accurately identifying all relevant cardiac views used in “real world” echo exams, including views acquired with contrast. Use of the CNN in a routine clinical workflow could improve efficiency of quantification steps performed after image acquisition. This was tested on an independent dataset acquired in a different country to that used to train the model and was found to perform similarly thus indicating the generalisability of the model. Figure 1. Confusion matrices Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Ultromics Ltd.

scholarly journals Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

2021 ◽  
Vol 13 (12) ◽  
pp. 2301
Author(s):  
Zander Venter ◽  
Markus Sydenham
Keyword(s):  
Land Cover ◽  
Atmospheric Correction ◽  
Google Earth ◽  
Tree Planting ◽  
Training Data ◽  
Classification Model ◽  
Night Time ◽  
Continental Scale ◽  
Land Cover Maps ◽  
Sentinel 2

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (< 1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86 vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.

Ensemble-based data assimilation of volcanic aerosols using FALL3D+PDAF

2021 ◽  
Author(s):  
Leonardo Mingari ◽  
Andrew Prata ◽  
Federica Pardini
Keyword(s):  
Data Assimilation ◽  
Information Transfer ◽  
High Performance ◽  
Volcanic Ash ◽  
Numerical Models ◽  
Volcanic Eruptions ◽  
Column Height ◽  
Eruption Column ◽  
Operational Environment ◽  
Assimilation System

&lt;p&gt;Modelling atmospheric dispersion and deposition of volcanic ash is becoming increasingly valuable for understanding the potential impacts of explosive volcanic eruptions on infrastructures, air quality and aviation. The generation of high-resolution forecasts depends on the accuracy and reliability of the input data for models. Uncertainties in key parameters such as eruption column height injection, physical properties of particles or meteorological fields, represent a major source of error in forecasting airborne volcanic ash. The availability of nearly real time geostationary satellite observations with high spatial and temporal resolutions provides the opportunity to improve forecasts in an operational context. Data assimilation (DA) is one of the most effective ways to reduce the error associated with the forecasts through the incorporation of available observations into numerical models. Here we present a new implementation of an ensemble-based data assimilation system based on the coupling between the FALL3D dispersal model and the Parallel Data Assimilation Framework (PDAF). The implementation is based on the last version release of FALL3D (versions 8.x) tailored to the extreme-scale computing requirements, which has been redesigned and rewritten from scratch in the framework of the EU Center of Excellence for Exascale in Solid Earth (ChEESE). The proposed methodology can be efficiently implemented in an operational environment by exploiting high-performance computing (HPC) resources. The FALL3D+PDAF system can be run in parallel and supports online-coupled DA, which allows an efficient information transfer through parallel communication. Satellite-retrieved data from recent volcanic eruptions were considered as input observations for the assimilation system.&lt;/p&gt;

Semi-Supervised Classification and its Application to Filtering IDS False Positives

2013 ◽  
Vol 427-429 ◽  
pp. 2309-2312
Author(s):  
Hai Bin Mei ◽  
Ming Hua Zhang
Keyword(s):  
Supervised Learning ◽  
Supervised Classification ◽  
Classification Performance ◽  
False Positives ◽  
Training Data ◽  
Classification Model ◽  
Classification Technique

Alert classifiers built with the supervised classification technique require large amounts of labeled training alerts. Preparing for such training data is very difficult and expensive. Thus accuracy and feasibility of current classifiers are greatly restricted. This paper employs semi-supervised learning to build alert classification model to reduce the number of needed labeled training alerts. Alert context properties are also introduced to improve the classification performance. Experiments have demonstrated the accuracy and feasibility of our approach.

scholarly journals Estimating building vulnerability to volcanic ash fall for insurance and other purposes

2017 ◽  
Vol 6 (1) ◽  
Author(s):  
R. J. Blong ◽  
P. Grasso ◽  
S. F. Jenkins ◽  
C. R. Magill ◽  
T. M. Wilson ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Building Vulnerability ◽  
Ash Fall

scholarly journals Analisis Sentimen Twitter untuk Teks Berbahasa Indonesia dengan Maximum Entropy dan Support Vector Machine

2014 ◽  
Vol 8 (1) ◽  
pp. 91 ◽  
Author(s):  
Noviah Dwi Putranti ◽  
Edi Winarko
Keyword(s):  
Support Vector Machine ◽  
Maximum Entropy ◽  
Social Networking Site ◽  
Training Data ◽  
Classification Model ◽  
Support Vector ◽  
Public Sentiment ◽  
Pos Tagger ◽  
Negative Sentiment ◽  
Bahasa Indonesia

AbstrakAnalisis sentimen dalam penelitian ini merupakan proses klasifikasi dokumen tekstual ke dalam dua kelas, yaitu kelas sentimen positif dan negatif.  Data opini diperoleh dari jejaring sosial Twitter berdasarkan query dalam Bahasa Indonesia. Penelitian ini bertujuan untuk menentukan sentimen publik terhadap objek tertentu yang disampaikan di Twitter dalam bahasa Indonesia, sehingga membantu usaha untuk melakukan riset pasar atas opini publik. Data yang sudah terkumpul dilakukan proses preprocessing dan POS tagger untuk menghasilkan model klasifikasi melalui proses pelatihan. Teknik pengumpulan kata yang memiliki sentimen dilakukan dengan pendekatan berdasarkan kamus, yang dihasilkan dalam penelitian ini berjumlah 18.069 kata. Algoritma Maximum Entropy digunakan untuk POS tagger dan algoritma yang digunakan untuk membangun model klasifikasi atas data pelatihan dalam penelitian ini adalah Support Vector Machine. Fitur yang digunakan adalah unigram dengan fitur pembobotan TFIDF. Implementasi klasifikasi diperoleh akurasi 86,81 %  pada pengujian 7 fold cross validation untuk tipe kernel Sigmoid. Pelabelan kelas secara manual dengan POS tagger menghasilkan akurasi 81,67%.  Kata kunci—analisis sentimen, klasifikasi, maximum entropy POS tagger, support vector machine, twitter.  AbstractSentiment analysis in this research classified textual documents into two classes, positive and negative sentiment. Opinion data obtained a query from social networking site Twitter of Indonesian tweet. This research uses  Indonesian tweets. This study aims to determine public sentiment toward a particular object presented in Twitter businesses conduct market. Collected data then prepocessed to help POS tagged to generate classification models through the training process. Sentiment word collection has done the dictionary based approach, which is generated in this study consists 18.069 words. Maximum Entropy algorithm is used for POS tagger and the algorithms used to build the classification model on the training data is Support Vector Machine. The unigram features used are the features of TFIDF weighting.Classification implementation 86,81 % accuration at examination of 7 validation cross fold for the type of kernel of Sigmoid. Class labeling manually with POS tagger yield accuration 81,67 %. Keywords—sentiment analysis, classification, maximum entropy POS tagger, support vector machine, twitter.

scholarly journals LEARNING THE 3D POSE OF VEHICLES FROM 2D VEHICLE PATCHES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 683-688
Author(s):  
C. Koetsier ◽  
T. Peters ◽  
M. Sester
Keyword(s):  
Elevation Angle ◽  
Training Data ◽  
Training Dataset ◽  
Image Patch ◽  
Movement Trajectories ◽  
Surveillance Camera ◽  
Image Patches ◽  
Ground Point ◽  
Precise Movement ◽  
Real Time Applications

Abstract. Estimating vehicle poses is crucial for generating precise movement trajectories from (surveillance) camera data. Additionally for real time applications this task has to be solved in an efficient way. In this paper we introduce a deep convolutional neural network for pose estimation of vehicles from image patches. For a given 2D image patch our approach estimates the 2D coordinates of the image representing the exact center ground point (cx, cy) and the orientation of the vehicle - represented by the elevation angle (e) of the camera with respect to the vehicle’s center ground point and the azimuth rotation (a) of the vehicle with respect to the camera. To train a accurate model a large and diverse training dataset is needed. Collecting and labeling such large amount of data is very time consuming and expensive. Due to the lack of a sufficient amount of training data we show furthermore, that also rendered 3D vehicle models with artificial generated textures are nearly adequate for training.

Classification Methods

2011 ◽  
pp. 196-201 ◽  
Author(s):  
Aijun An
Keyword(s):  
Unsupervised Learning ◽  
Supervised Learning ◽  
Credit Card ◽  
A Priori ◽  
Training Data ◽  
Classification Model ◽  
Highly Active ◽  
Data Object ◽  
Patient Database ◽  
Data Objects

Generally speaking, classification is the action of assigning an object to a category according to the characteristics of the object. In data mining, classification refers to the task of analyzing a set of pre-classified data objects to learn a model (or a function) that can be used to classify an unseen data object into one of several predefined classes. A data object, referred to as an example, is described by a set of attributes or variables. One of the attributes describes the class that an example belongs to and is thus called the class attribute or class variable. Other attributes are often called independent or predictor attributes (or variables). The set of examples used to learn the classification model is called the training data set. Tasks related to classification include regression, which builds a model from training data to predict numerical values, and clustering, which groups examples to form categories. Classification belongs to the category of supervised learning, distinguished from unsupervised learning. In supervised learning, the training data consists of pairs of input data (typically vectors), and desired outputs, while in unsupervised learning there is no a priori output. Classification has various applications, such as learning from a patient database to diagnose a disease based on the symptoms of a patient, analyzing credit card transactions to identify fraudulent transactions, automatic recognition of letters or digits based on handwriting samples, and distinguishing highly active compounds from inactive ones based on the structures of compounds for drug discovery.

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