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

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Natural Risk Management and Engineering

Part of the book series: Springer Tracts in Civil Engineering ((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.

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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

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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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Authors and Affiliations

  1. Technical University of Crete, University Campus, 73100, Chania, Greece

    Georgios K. Tairidis & Georgios E. Stavroulakis

  2. University of Niš, Univerzitetski trg 2, 18000, Niš, Serbia

    Nikola Stojanovic & Dusan Stamenkovic

Authors
  1. Georgios K. Tairidis
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  2. Nikola Stojanovic
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  3. Dusan Stamenkovic
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  4. Georgios E. Stavroulakis
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Corresponding author

Correspondence to Georgios K. Tairidis .

Editor information

Editors and Affiliations

  1. University of Niš, Niš, Serbia

    Milan Gocić

  2. Department of Engineering, University of Messina, Messina, Italy

    Giuseppe Tito Aronica

  3. Department of Production Engineering and Management, Technical University of Crete, Chania, Greece

    Georgios E. Stavroulakis

  4. University of Niš, Niš, Serbia

    Slaviša Trajković

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Tairidis, G.K., Stojanovic, N., Stamenkovic, D., Stavroulakis, G.E. (2020). Neuro-fuzzy Techniques and Natural Risk Management. Applications of ANFIS Models in Floods and Comparison with Other Models. In: Gocić, M., Aronica, G., Stavroulakis, G., Trajković, S. (eds) Natural Risk Management and Engineering. Springer Tracts in Civil Engineering . Springer, Cham. https://doi.org/10.1007/978-3-030-39391-5_8

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  • DOI: https://doi.org/10.1007/978-3-030-39391-5_8

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