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Neuro-fuzzy Techniques and Natural Risk Management. Applications of ANFIS Models in Floods and Comparison with Other Models

  • Georgios K. TairidisEmail author
  • Nikola Stojanovic
  • Dusan Stamenkovic
  • Georgios E. Stavroulakis
Chapter
  • 28 Downloads
Part of the Springer Tracts in Civil Engineering book series (SPRTRCIENG)

Abstract

During the last decades, floods are getting more and more dangerous and they cause a lot of destruction either for human lives and/or for people’s properties. Due to different climate conditions, some parts of the world present increased levels of danger from floods. For this reason, the development of a robust tool for the prediction of floods is essential for the protection of people who live in these areas. An adaptive neuro-fuzzy inference system is a hybrid fuzzy system, which is based on Sugeno fuzzy inference along with the use of artificial neural networks for training. In this work, the current literature on adaptive neuro-fuzzy inference system models, which are used for flood prediction, is reviewed. More specifically, the mode of operation of such decision-making systems, along with their major advantages and disadvantages are presented in detail. A comparison with other similar models is also carried out.

Keywords

Adaptive neuro-fuzzy inference system ANFIS Natural disasters Floods 

Abbreviations

ANFIS

Adaptive neuro-fuzzy inference system

ANGIS

Adaptive neuro genetic algorithms integrated systems

ANN

Artificial neural network

ARIMA

Autoregressive integrated moving average

ARMA

Autoregressive moving average

BPNN

Back-propagation neural network

CE

Coefficient of efficiency

CGF

Conjugate descent algorithm

CORR

Coefficient of correlation

D

Discrepancy ratio

FIS

Fuzzy inference system

GDX

Gradient descent algorithm

GIS

Geographic information system

GNN

Generalized neural network

HN-FIS

Hybrid neuro-fuzzy inference system

LM

Levenberg–Marquardt algorithm

MAE

Mean absolute error

MAPE

Mean absolute percentage error

MLP

Multi-layer perceptron

Mo-ANFIS

Modified ANFIS

MONF

Metaheuristic optimization neuro-fuzzy

PSO

Particle swarm optimization

R2

Coefficient of determination

RFFA

Regional flood frequency analyses

RMSE

Root mean square error

SAC-SMA

Sacramento technique

Notes

Acknowledgements

Nikola Stojanovic and Dusan Stamenkovic gratefully acknowledge the financial support for their visit at the Technical University of Crete, through the Special Mobility Strand action of the “Development of Master Curricula for Natural Disasters Risk Management in Western Balkan Countries/NatRisk” Erasmus+ Capacity Building program.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Technical University of CreteChaniaGreece
  2. 2.University of NišNišSerbia

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