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

  • Sina Ardabili
  • Amir MosaviEmail author
  • Majid Dehghani
  • Annamária R. Várkonyi-Kóczy
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 101)

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.

Keywords

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

Nomenclatures

ANN

Artificial neural network

ELM

Extreme learning machine

ML

Machine learning

SVM

Support vector machine

WNN

Wavelet neural networks

DL

Deep learning

ARIMA

Autoregressive integrated moving average

FFNN

Feed-forward neural networks

MLP

Multi layered perceptron

DT

Decision tree

RSM

Response surface methodology

BPNN

Back propagation neural network

GBDT

Gradient boosting decision tree

ANFIS

Adaptive neuro fuzzy inference system

CPU

Central processing unit

FA

Fire-fly algorithm

DNN

Deep neural network

RF

Random forest

DFNN

Deep feedforward neural network

RNN

Recurrent neural network

PLS

Partial least squares

DA

Discriminant analysis

PCA

Principal component analysis

LDA

Linear discriminant analysis

SVR

Support vector regression

LS

Least-squares

SB

Sparse Bayesian

SPEI

Standard precipitation evapotranspiration index

GP

Genetic programming

MLR

Multi linear regression

MODIS

Moderate Resolution Imaging Spectroradiometer

ROM

Reduced order model

WSSFA

Wise step fire-fly algorithm

DBN

Deep belief networks.

Notes

Acknowledgments

This publication has been supported by the Project: “Support of research and development activities of the J. Selye University in the field of Digital Slovakia and creative industry” of the Research & Innovation Operational Programme (ITMS code: NFP313010T504) co-funded by the European Regional Development Fund.

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Advanced Studies KoszegKoszegHungary
  2. 2.Kalman Kando, Faculty of Electrical EngineeringObuda UniversityBudapestHungary
  3. 3.School of the Built EnvironmentOxford Brookes UniversityOxfordUK
  4. 4.Technical and Engineering Department, Faculty of Civil EngineeringVali-e-Asr University of RafsanjanRafsanjanIran
  5. 5.Department of Mathematics and InformaticsJ. Selye UniversityKomarnoSlovakia

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