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Book of Abstracts of the International Conference “Data Intensive System Analisys for Geohazard Studies”. Poster Sessionстатья Тезисы
Информация о цитировании статьи получена из
Web of Science
Дата последнего поиска статьи во внешних источниках: 8 февраля 2017 г.
- Авторы: Nikolay Bilev, Nikolay Nikiforov
- Журнал: Geoinformatics Research Papers
- Год издания: 2016
- Первая страница: 45
- Последняя страница: 45
- DOI: 10.2205/2016bs08sochi
- Аннотация: During the last ten years, machine learning approach for data analysis has been a topic of growing interest for the scientific community. Not surprisingly so, since the last decade it has brought about a true revolution in the world of technology with new and extremely advanced approaches to computer modeling. As far as engineering depends on data processing it is very important to provide it with modern and reliable analytical systems. Nowadays, implementation of geo-information systems within the field of Earth Sciences and affiliated parts of industry is highly relevant. Particularly for engineering surveys it gives a tremendous potential for data analysis. In addition, rapidly growing open source community of data science provides us a chance to implement new and flexible software for data mining. The present study makes an attempt to combine basic principles from GIS modeling and machine learning techniques in order to develop sustainable prediction systems for hazardous events. The models are based on the results of engineering surveys and remote scanning within the Taman peninsular. Particularly, they describe distribution of the most hazardous processes for the buildings construction: collapsibility and landslides. The final model is aimed to determine suitable sites for civil development based on geological safety and resource economy perspective. We explore nonlinear connections between different variables using the most up-to-date methods of computational mathematics in order to realize full potential of our data.
- Добавил в систему: Билев Николай Александрович