Deep Learning and Machine Learning in Hydrological Processes Climate Change and Earth Systems a Systematic Review

Sina Ardabili, Amir Mosavi, Majid Dehghani, Annamária R. Várkonyi-Kóczy

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Artificial intelligence methods and application have recently shown great contribution in modeling and prediction of the hydrological processes, climate change, and earth systems. Among them, deep learning and machine learning methods mainly have reported being essential for achieving higher accuracy, robustness, efficiency, computation cost, and overall model performance. This paper presents the state of the art of machine learning and deep learning methods and applications in this realm and the current state, and future trends are discussed. The survey of the advances in machine learning and deep learning are presented through a novel classification of methods. The paper concludes that deep learning is still in the first stages of development, and the research is still progressing. On the other hand, machine learning methods are already established in the fields, and novel methods with higher performance are emerging through ensemble techniques and hybridization.

Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
PublisherSpringer
Pages52-62
Number of pages11
DOIs
Publication statusPublished - Jan 1 2020

Publication series

NameLecture Notes in Networks and Systems
Volume101
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

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Keywords

  • Big data
  • Climate change
  • Deep learning
  • Earth systems
  • Global warming
  • Hydrological model
  • Hydrology
  • Machine learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

Cite this

Ardabili, S., Mosavi, A., Dehghani, M., & Várkonyi-Kóczy, A. R. (2020). Deep Learning and Machine Learning in Hydrological Processes Climate Change and Earth Systems a Systematic Review. In Lecture Notes in Networks and Systems (pp. 52-62). (Lecture Notes in Networks and Systems; Vol. 101). Springer. https://doi.org/10.1007/978-3-030-36841-8_5