scholarly journals Flash floods in Moravia and Silesia during the nineteenth and twentieth centuries

Geografie ◽  
2020 ◽  
Vol 125 (2) ◽  
pp. 117-137 ◽  
Author(s):  
Olga Halásová ◽  
Rudolf Brázdil
Keyword(s):  
Flash Flood ◽  
Flash Floods ◽  
Spatiotemporal Data ◽  
Data Uncertainty ◽  
Spatiotemporal Variability ◽  
Documentary Evidence ◽  
Central European ◽  
Different Types ◽  
Nineteenth And Twentieth Centuries

A range of documentary evidence and systematic meteorological/hydrological observations were employed to create a database of flash floods for Moravia and Silesia (the eastern part of Czechia) in the 19th and 20th centuries. The data extracted were used for an analysis of the spatiotemporal variability of flash floods, based on the frequency of days with flash floods and the number of municipalities affected. The dynamic climatology of flash floods was interpreted using the Czech Hydrometeorological Institute classification of synoptic types. Descriptions of flash-flood-related damage enabled their further division into six different types. Examples of three outstanding flash floods are described in more detail. All interpreted results are discussed with respect to spatiotemporal data uncertainty and their national and broader central European context. Flash floods constitute significant extreme natural events in Moravia and Silesia; knowledge of them, and more detailed investigation, are important to risk reduction.

scholarly journals Potential of Documentary Evidence to Study Fatalities of Hydrological and Meteorological Events in the Czech Republic

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2014 ◽  
Author(s):  
Brázdil ◽  
Chromá ◽  
Řehoř ◽  
Zahradníček ◽  
Dolák ◽  
...  
Keyword(s):  
Czech Republic ◽  
Cause Of Death ◽  
Flash Flood ◽  
Data Uncertainty ◽  
Spatiotemporal Variability ◽  
Documentary Evidence ◽  
Convective Storm ◽  
The Czech Republic ◽  
Epigraphic Evidence

This paper presents the potential of documentary evidence for enhancing the study of fatalities taking place in the course of hydrological and meteorological events (HMEs). Chronicles, “books of memory”, weather diaries, newspapers (media), parliamentary proposals, epigraphic evidence, systematic meteorological/hydrological observations, and professional papers provide a broad base for gathering such information in the Czech Republic, especially since 1901. The spatiotemporal variability of 269 fatalities in the Czech Republic arising out of 103 HMEs (flood, flash flood, windstorm, convective storm, lightning, frost, snow/glaze-ice calamity, heat, and other events) in the 1981–2018 period is presented, with particular attention to closer characterisation of fatalities (gender, age, cause of death, place, type of death, and behaviour). Examples of three outstanding events with the highest numbers of fatalities (severe frosts in the extremely cold winter of 1928/1929, a flash flood on 9 June 1970, and a rain flood in July 1997) are described in detail. Discussion of results includes the problem of data uncertainty, factors influencing the numbers of fatalities, and the broader context. Since floods are responsible for the highest proportion of HME-related deaths, places with fatalities are located mainly around rivers and drowning appears as the main cause of death. In the further classification of fatalities, males and adults clearly prevail, while indirect victims and hazardous behaviour are strongly represented.

Documentary data in the study of fatalities caused by meteorological and hydrological events: the Czech Republic, 1964–2019

2020 ◽  
Author(s):  
Kateřina Chromá ◽  
Rudolf Brázdil ◽  
Lukáš Dolák ◽  
Jan Řehoř ◽  
Ladislava Řezníčková
Keyword(s):  
Czech Republic ◽  
Flash Flood ◽  
Data Uncertainty ◽  
Spatiotemporal Variability ◽  
The Czech Republic ◽  
Uncertainty Factors ◽  
Convective Storms ◽  
Documentary Data ◽  
Epigraphic Evidence

<p>Reports from the newspaper “Rudé právo/Právo”, complemented by chronicles, epigraphic evidence, systematic meteorological/hydrological observations, media (including internet), professional reports and papers were used to create a database of fatalities taking place in the course of hydrological and meteorological events over the territory of the Czech Republic during the 1964–2019 period. The spatiotemporal variability of fatalities arising out of floods, flash floods, windstorms, convective storms, lightning, frosts, snow/glaze-ice calamities, avalanches, heats and other events is shown, with particular attention to closer characterisation of fatalities (gender, age, cause of death, place, type of death and behaviour). In the classification of fatalities, males and adults clearly prevail, while indirect victims and hazardous behaviour are strongly represented. Examples of two outstanding events with the highest numbers of fatalities during a flash flood on 9 June 1970 (34 fatalities) and a rain-induced flood in July 1997 (60 fatalities) are described in detail. Discussion of results includes the problem of data uncertainty, factors influencing the numbers of fatalities, and the broader context. The study emphasises the significance of documentary data and reveals its new utilisation in the study of fatalities in the Czech Republic.</p>

From Flash Flood Vulnerability and Risk Assessment to Property Damage Prediction: the Value of Machine Learning

2020 ◽  
Author(s):  
Atieh Alipour ◽  
Peyman Abbaszadeh ◽  
Ali Ahmadalipour ◽  
Hamid Moradkhani
Keyword(s):  
Machine Learning ◽  
Flash Flood ◽  
Rapid Onset ◽  
Flash Floods ◽  
Flood Damage ◽  
Flood Events ◽  
Property Damage ◽  
Damage Prediction ◽  
Financial Consequences

<p>Flash floods, as a result of frequent torrential rainfalls caused by tropical storms, thunderstorms,<br>and hurricanes, are a prevalent natural disaster in the southeast U.S. (SEUS), which frequently<br>threaten human lives and properties in the region. According to the U.S. National Weather<br>Service (NWS), flash floods generally initiate within less than six hours of an intense rainfall<br>onset. Therefore, there is a limited chance for effective and timely decision-making. Due to the<br>rapid onset of flash floods, they are costly events, such that only during 1996 to 2017 flash<br>floods imposed 7.5 billion dollars property damage to the SEUS. Therefore, estimating the<br>potential economic damages as a result of flash floods are crucial for flood risk management and<br>financial appraisals for decision makers. A multitude of studies have focused on flood damage<br>modeling, few of which investigated the issue on a large domain. Here, we propose a systematic<br>framework that considers a variety of factors that explain different risk components (i.e., hazard,<br>vulnerability, and exposure) and leverages Machine Learning (ML) for flood damage prediction.<br>Over 14,000 flash flood events during 1996 to 2017 were assessed to analyze their characteristics<br>including frequency, duration, and intensity. Also, different data sources were utilized to derive<br>information related to each event. The most influential features are then selected using a multi<br>criteria variable selection approach. Then, the ML model is implemented for not only binary<br>classification of damage (i.e., whether a flash flood event caused any damage or not), but also for<br>developing a model to predict the financial consequences associated with flash flood events. The<br>results indicate a high accuracy for the classifier, significant correlation and relatively low bias<br>between the predicted and observed property damages showing the effectiveness of proposed<br>methodology for flash flood damage modeling applicable to variety of flood prone regions.</p>

Chronology of strong winds based on documentary evidence in the Czech Republic from AD 1510

2020 ◽  
Author(s):  
Lukáš Dolák ◽  
Rudolf Brázdil ◽  
Petr Dobrovolný ◽  
Hubert Valášek ◽  
Ladislava Řezníčková ◽  
...  
Keyword(s):  
Czech Republic ◽  
Spatiotemporal Variability ◽  
Documentary Evidence ◽  
Convective Storms ◽  
Scientific Papers ◽  
Strong Winds ◽  
Instrumental Period ◽  
Czech Lands ◽  
Wind Speed Measurement

<p>To develop an understanding of recent variability in strong winds, it is necessary to analyse their past behaviour. While relatively short series of wind-speed measurement in the Czech Lands (recent Czech Republic) started mostly in the first half of the 20<sup>th</sup> century, documentary evidence represents a valuable source of information helping extend the knowledge of strong winds to the pre-instrumental period. In this study, we analyse strong winds on the basis of chronicles, weather diaries, early journalism, economic and financial sources, as well as old academic journals, newspapers, professional papers and recent scientific papers. The created dataset presents a chronology of strong winds in the Czech Lands from AD 1510 to present. The dataset contains more than 5000 events, which are classified on duration, location, extent, severity and type of damage on squalls (convective storms), tornadoes, blizzards, gales and windstorms. Gales, often accompanied by loss of human lives, damage to buildings and forests (windthrows), are the most frequently recorded type of strong winds (44%), followed by blizzards (26%), squalls (18%), and tornadoes (7%). Strong winds detected are concentrated 1820s to late-1840s, 1900s to late-1930s and in the 2000s. Seasonal distribution of strong winds is relatively equal throughout the chronology with the highest frequency in July (10.0%), January (8.6%), and December (8.1%). Uncertainties in results emerge from a different spatiotemporal density of documentary data and from the ambiguous nature of some records in determining the classification of strong winds or attribution of damage caused to particular events. Our results highlight the importance of documentary evidence in the analysis of strong winds and contribute to a better understanding of their spatiotemporal variability in the past.</p>

Dual stain kit for the microscopic gram classification of ocular infection bacteria

1993 ◽  
Vol 51 ◽  
pp. 248-249
Author(s):  
Jacob S. Hanker ◽  
Dale N. Holdren ◽  
Kenneth L. Cohen ◽  
Beverly L. Giammara
Keyword(s):  
Contact Lenses ◽  
Ocular Infection ◽  
Gram Negative Bacteria ◽  
Gram Negative ◽  
Gram Staining ◽  
Ocular Infections ◽  
Different Types ◽  
Culture Positive ◽  
Increased Susceptibility

Keratitis and conjunctivitis (infections of the cornea or conjunctiva) are ocular infections caused by various bacteria, fungi, viruses or parasites; bacteria, however, are usually prominent. Systemic conditions such as alcoholism, diabetes, debilitating disease, AIDS and immunosuppressive therapy can lead to increased susceptibility but trauma and contact lens use are very important factors. Gram-negative bacteria are most frequently cultured in these situations and Pseudomonas aeruginosa is most usually isolated from culture-positive ulcers of patients using contact lenses. Smears for staining can be obtained with a special swab or spatula and Gram staining frequently guides choice of a therapeutic rinse prior to the report of the culture results upon which specific antibiotic therapy is based. In some cases staining of the direct smear may be diagnostic in situations where the culture will not grow. In these cases different types of stains occasionally assist in guiding therapy.

Explanatory Power for Medical Expert Systems: Studies in the Representation of Causal Relationships for Clinical Consultations

1982 ◽  
Vol 21 (03) ◽  
pp. 127-136 ◽  
Author(s):  
J. W. Wallis ◽  
E. H. Shortliffe
Keyword(s):  
Explanatory Power ◽  
Prototype System ◽  
Inference Rules ◽  
Medical Expert ◽  
Comprehensive Knowledge ◽  
Different Types ◽  
Medical Expert Systems ◽  
Explanation System ◽  
Systems Studies

This paper reports on experiments designed to identify and implement mechanisms for enhancing the explanation capabilities of reasoning programs for medical consultation. The goals of an explanation system are discussed, as is the additional knowledge needed to meet these goals in a medical domain. We have focussed on the generation of explanations that are appropriate for different types of system users. This task requires a knowledge of what is complex and what is important; it is further strengthened by a classification of the associations or causal mechanisms inherent in the inference rules. A causal representation can also be used to aid in refining a comprehensive knowledge base so that the reasoning and explanations are more adequate. We describe a prototype system which reasons from causal inference rules and generates explanations that are appropriate for the user.

scholarly journals A Survey of Remote Sensing and Geographic Information System Applications for Flash Floods

2021 ◽  
Vol 13 (9) ◽  
pp. 1818
Author(s):  
Lisha Ding ◽  
Lei Ma ◽  
Longguo Li ◽  
Chao Liu ◽  
Naiwen Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Flash Flood ◽  
Flash Floods ◽  
Geographic Information ◽  
Flood Disaster ◽  
Future Research ◽  
Time Zone ◽  
Gis Technologies

Flash floods are among the most dangerous natural disasters. As climate change and urbanization advance, an increasing number of people are at risk of flash floods. The application of remote sensing and geographic information system (GIS) technologies in the study of flash floods has increased significantly over the last 20 years. In this paper, more than 200 articles published in the last 20 years are summarized and analyzed. First, a visualization analysis of the literature is performed, including a keyword co-occurrence analysis, time zone chart analysis, keyword burst analysis, and literature co-citation analysis. Then, the application of remote sensing and GIS technologies to flash flood disasters is analyzed in terms of aspects such as flash flood forecasting, flash flood disaster impact assessments, flash flood susceptibility analyses, flash flood risk assessments, and the identification of flash flood disaster risk areas. Finally, the current research status is summarized, and the orientation of future research is also discussed.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals Preventing corneal blindness caused by keratitis using artificial intelligence

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhongwen Li ◽  
Jiewei Jiang ◽  
Kuan Chen ◽  
Qianqian Chen ◽  
Qinxiang Zheng ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Vision Loss ◽  
Learning System ◽  
Automated Classification ◽  
Slit Lamp ◽  
Resource Limited ◽  
The World ◽  
Different Types ◽  
Corneal Blindness

AbstractKeratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.

NEURAL CLASSIFICATION OF MASS ABNORMALITIES WITH DIFFERENT TYPES OF FEATURES IN DIGITAL MAMMOGRAPHY

2006 ◽  
Vol 06 (01) ◽  
pp. 61-75 ◽  
Author(s):  
R. PANCHAL ◽  
B. VERMA
Keyword(s):  
Breast Cancer ◽  
Research Work ◽  
Breast Cancer Diagnosis ◽  
Classification Rate ◽  
Test Dataset ◽  
Benchmark Database ◽  
Different Types ◽  
Breast Tissues ◽  
Mass Type

Early detection of breast abnormalities remains the primary prevention against breast cancer despite the advances in breast cancer diagnosis and treatment. Presence of mass in breast tissues is highly indicative of breast cancer. The research work presented in this paper investigates the significance of different types of features using proposed neural network based classification technique to classify mass type of breast abnormalities in digital mammograms into malignant and benign. 14 gray level based features, four BI-RADS features, patient age feature and subtlety value feature have been explored using the proposed research methodology to attain maximum classification on test dataset. The proposed research technique attained a 91% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.

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