Глубокие нейронные сети в гидрологии: новое поколение универсальных и эффективных моделей

  • Георгий Владимирович Айзель Государственный гидрологический институт, Российская Федерация, 199004, Санкт-Петербург, 2-я линия В. О., 23; Потсдамский университет, Институт наук об окружающей среде и географии, Германия, 14476, Потсдам, ул. Карла Либкнехта, 24–25 https://orcid.org/0000-0001-5608-9110

Аннотация

В течение последнего десятилетия глубокое обучение - область машинного обучения, относящаяся к искусственным нейронным сетям, состоящим из множества вычислительных слоев, - изменяет ландшафт развития статистических моделей во многих областях исследований, таких как классификация изображений, машинный перевод, распознавание речи. Географические науки, а также входящая в их состав область исследования гидрологии суши, не стоят в стороне от этого движения. В последнее время применение современных технологий и методов глубокого обучения активно набирает популярность для решения широкого спектра гидрологических задач: моделирования и прогнозирования речного стока, районирования модельных параметров, оценки располагаемых водных ресурсов, идентификации факторов, влияющих на современные изменения водного режима. Такой рост популярности глубоких нейронных сетей продиктован прежде всего их высокой универсальностью и эффективностью. Представленные качества в совокупности с быстрорастущим количеством накопленной информации о состоянии окружающей среды, а также ростом доступности вычислительных средств и ресурсов, позволяют говорить о глубоких нейронных сетях как о новом поколении математических моделей, призванных если не заменить существующие решения, то значительно обогатить область моделирования геофизических процессов. В данной работе представлен краткий обзор текущего состояния области разработки и применения глубоких нейронных сетей в гидрологии. Также в работе предложен качественный долгосрочный прогноз развития технологии глубокого обучения для решения задач гидрологического моделирования на основе использования «кривой ажиотажа Гартнера», в общих чертах описывающей жизненный цикл современных технологий.

Ключевые слова:

глубокие нейронные сети, глубокое обучение, машинное обучение, гидрология, моделирование

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Опубликован
2021-01-28
Как цитировать
Айзель, Г. В. (2021) «Глубокие нейронные сети в гидрологии: новое поколение универсальных и эффективных моделей», Вестник Санкт-Петербургского университета. Науки о Земле, 66(1). doi: 10.21638/spbu07.2021.101.