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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">vmait</journal-id><journal-title-group><journal-title xml:lang="ru">Computational Mathematics and Information Technologies</journal-title><trans-title-group xml:lang="en"><trans-title>Computational Mathematics and Information Technologies</trans-title></trans-title-group></journal-title-group><issn pub-type="epub">2587-8999</issn><publisher><publisher-name>Донской государственный технический университет</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">2587-8999-2026-10-3-31-40</article-id><article-id custom-type="elpub" pub-id-type="custom">vmait-245</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>INFORMATION TECHNOLOGIES</subject></subj-group></article-categories><title-group><article-title>Математическое моделирование береговой линии Черного моря с учетом ее фрактальной структуры и построение сеток</article-title><trans-title-group xml:lang="en"><trans-title>Mathematical Modelling of the Black Sea Coastline Considering its Fractal Structure and Grid Generation</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0001-3726-0178</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Кодацкий</surname><given-names>Н. М.</given-names></name><name name-style="western" xml:lang="en"><surname>Kodatsk</surname><given-names>N. M.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Никита Максимович Кодацкий, аспирант кафедры математики и информатики  </p><p>344003, г. Ростов-на-Дону, пл. Гагарина, 1</p></bio><bio xml:lang="en"><p>Nikita M. Kodatsky, PhD student, Department of Mathematics and Informatics </p><p>1, Gagarin Sq., Rostov-on-Don, 344003 </p></bio><email xlink:type="simple">nickitadatsky@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2639-7451</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Белова</surname><given-names>Ю. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Belova</surname><given-names>Yu. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Юлия Валериевна Белова, кандидат физико-математических наук, доцент кафедры математики и информатики </p><p> </p></bio><bio xml:lang="en"><p>Yulia V. Belova, Candidate of Physical and Mathematical Sciences, Associate Professor, Department of Mathematics and Informatics </p><p> 1, Gagarin Sq., Rostov-on-Don, 344003 </p></bio><email xlink:type="simple">yvbelova@yandex.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Донской государственный технический университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Don State Technical University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2026</year></pub-date><pub-date pub-type="epub"><day>04</day><month>10</month><year>2026</year></pub-date><volume>10</volume><issue>3</issue><fpage>31</fpage><lpage>40</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Кодацкий Н.М., Белова Ю.В., 2026</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="ru">Кодацкий Н.М., Белова Ю.В.</copyright-holder><copyright-holder xml:lang="en">Kodatsk N.M., Belova Y.V.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.cmit-journal.ru/jour/article/view/245">https://www.cmit-journal.ru/jour/article/view/245</self-uri><abstract><p>Введение. Береговая линия водоема представляет собой сложный природный объект, геометрия которого проявляет нерегулярность и признаки самоподобия, а длина зависит от масштаба измерения. Несмотря на широкое изучение её фрактальных свойств, переход к цифровому представлению береговой линии представляет собой комплексную процедуру и является актуальной темой исследования. В настоящей работе описана созданная математико-алгоритмическая схема анализа береговой линии, включающая валидацию геометрии, геодезическое измерение, анализ масштабной зависимости, оценку фрактальной размерности и выбор адаптивной четырёхугольной сетки на примере Чёрного моря. Материалы и методы. Для береговой линии, представленной конечной последовательностью географических точек, проводятся процедуры нормализации и валидации, что позволяет получить промежуточный геометрически правдоподобный контур. Длина контура вычисляется на сфере с использованием формулы гаверсинуса. Для работы с многомасштабностью и фрактальными характеристиками береговой линии применяется метод «подсчета ящиков». Для перехода от линии к 2D-представлению акватории применяется равноплощадная проекция Lambert azimuthal equal-area (LAEA), а затем строится поле требуемого размера ячейки. Далее строятся подробные и укрупненные полноквадратные сетки с помощью генераторов Delaunay, Frontal-Delaunay for Quads и Packing of Parallelograms. Производится сравнение генераторов сеток. Результаты исследования. Проведен вычислительный эксперимент на данных береговой линии Чёрного моря. Сравнение двух наборов сеток показало, что лучшим генератором по заданному многокритериальному показателю является Delaunay. Для сохранения существенных береговых особенностей на масштабе всей акватории следует использовать Delaunay с диапазоном 125–250 м. Подробный Delaunay с диапазоном 50–250 м следует выбирать, когда в приоритете точность восстановления рельефа дна. Обсуждение. Практическая значимость работы состоит в возможности подготовки верифицированных контуров и сеток для моделирования рельефа дна, геоинформационного анализа и последующих гидродинамических расчётов. Заключение. Перспективы дальнейших исследований связаны с расширением класса анализируемых береговых систем, сопоставлением различных методов оценки фрактальной размерности, исследованием влияния пространственного разрешения исходных геоданных на устойчивость вычисляемых метрик, а также с адаптацией подхода к задачам многомасштабного мониторинга береговой динамики.</p></abstract><trans-abstract xml:lang="en"><p>Introduction. The coastline of a reservoir is a complex natural object, which geometry has irregularity and signs of selfsimilarity, and the length depends on the scale of measurement. Despite the widespread study of its fractal properties, the transition to a digital representation of the coastline is a complex procedure and is a current topic of research. The purpose of the article is to develop a mathematical-algorithmic scheme for analyzing the coastline, including geometry validation, geodetic measurement, scale dependence analysis, fractal dimension assessment and selection of an adaptive quadrilateral grid using the example of the Black Sea. Materials and Methods. Normalization and validation procedures are applied to the coastline represented as a finite sequence of geographic points to obtain an intermediate, geometrically plausible contour. The contour length is calculated on a sphere using the haversine formula. The “box-counting” method is used to address the coastline՚s multi-scale nature and fractal characteristics. To transition from the line to a 2D representation of the water area, the Lambert azimuthal equal-area (LAEA) projection is used, followed by the construction of a grid with the required cell size. Subsequently, detailed and coarse full-quadrilateral grids are generated using the Delaunay, Frontal-Delaunay for Quads, and Packing of Parallelograms algorithms. A comparison of the grid generators is then conducted. Results. A computational experiment was conducted using Black Sea coastline data. A comparison of two sets of grids demonstrated that Delaunay is the superior generator based on the specified multi-criteria metric. The Delaunay method with a range of 125–250 m should be used to preserve significant coastal features across the entire water area. A highresolution Delaunay configuration with a range of 50–250 m should be selected when accurate reconstruction of the seabed topography is the priority. Discussion. The practical significance lies in the ability to prepare verified contours and grids for seabed topography modelling, geoinformation analysis, and subsequent hydrodynamic calculations. Conclusions. Future research prospects involve expanding the range of coastal systems analyzed, comparing various methods for estimating fractal dimension, investigating the impact of source geodata spatial resolution on the stability of calculated metrics, and adapting the approach for multi-scale monitoring of coastal dynamics.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>береговая линия</kwd><kwd>адаптивная четырёхугольная сетка</kwd><kwd>математическое моделирование</kwd><kwd>фрактальная геометрия</kwd><kwd>подсчёт покрывающих ячеек</kwd><kwd>батиметрия</kwd><kwd>масштабная зависимость</kwd></kwd-group><kwd-group xml:lang="en"><kwd>coastline</kwd><kwd>adaptive quadrilateral grid</kwd><kwd>mathematical modelling</kwd><kwd>fractal geometry</kwd><kwd>box-counting</kwd><kwd>bathymetry</kwd><kwd>scale dependence</kwd></kwd-group><funding-group><funding-statement xml:lang="ru">Исследование выполнено за счет гранта Российского научного фонда № 22–11–00295–П</funding-statement><funding-statement xml:lang="en">The study was supported by the Russian Science Foundation grant No. 22–11–00295–П</funding-statement></funding-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Mandelbrot B.B. 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