title: Deep neural networks in hydrology: the new generation of universal and efficient models
metadata.name: dc.contributor.author
metadata.value: Ayzel, Georgy V.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2021-06-01T14:36:19Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2021-06-01T14:36:19Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2021-03
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Ayzel, G. V. (2021). Deep neural networks in hydrology: the new generation of universal and efficient models. Vestnik of Saint Petersburg University. Earth Sciences, 66 (1), 5–18.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/spbu07.2021.101
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/29079
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: For around a decade, deep learning — the sub-field of machine learning that refers to artificial
  neural networks comprised of many computational layers — has been modifying the landscape
  of statistical model development in many research areas, such as image classification,
  machine translation, and speech recognition. Geoscientific disciplines in general and the field
  of hydrology in particular, are no exception to this movement. Recently, the proliferation of
  modern deep learning-based techniques and methods has been actively gaining popularity
  for solving a wide range of hydrological problems: modeling and forecasting of river runoff,
  hydrological model parameters regionalization, assessment of available water resources, and
  identification of the main drivers of the recent change in water balance components. This
  growing popularity of deep neural networks is primarily due to their high universality and
  efficiency. The presented qualities, together with the rapidly growing amount of accumulated
  environmental information, as well as the increasing availability of computing facilities and
  resources, allow us to speak about deep neural networks as a new generation of mathematical
  models designed to, if not to replace existing solutions, then significantly enrich the field of
  geophysical processes modeling. This paper provides a brief overview of the current state of
  the field of development and application of deep neural networks in hydrology. Also in the
  following study, the qualitative long-term forecast regarding the development of deep learning
  technology for managing the corresponding hydrological modeling challenges is provided
  based on the use of the Gartner Hype Curve, which in the general details describes the life
  cycle of modern technologies.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: The reported study was funded by RFBR (project no. 19-35-60005).
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Earth Sciences;Volume 66; Issue 1
metadata.lang: -
metadata.name: dc.subject
metadata.value: deep neural networks
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: deep learning
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: machine learning
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: hydrology
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: modeling
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Deep neural networks in hydrology: the new generation of universal and efficient models
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 5-18.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/29079/1/5-18.pdf
resources.format: Adobe PDF
resources.size: 1,5 MB