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

Part of the Springer Tracts in Civil Engineering book series (SPRTRCIENG)


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.


Adaptive neuro-fuzzy inference system ANFIS Natural disasters Floods 



Adaptive neuro-fuzzy inference system


Adaptive neuro genetic algorithms integrated systems


Artificial neural network


Autoregressive integrated moving average


Autoregressive moving average


Back-propagation neural network


Coefficient of efficiency


Conjugate descent algorithm


Coefficient of correlation


Discrepancy ratio


Fuzzy inference system


Gradient descent algorithm


Geographic information system


Generalized neural network


Hybrid neuro-fuzzy inference system


Levenberg–Marquardt algorithm


Mean absolute error


Mean absolute percentage error


Multi-layer perceptron


Modified ANFIS


Metaheuristic optimization neuro-fuzzy


Particle swarm optimization


Coefficient of determination


Regional flood frequency analyses


Root mean square error


Sacramento technique



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.


  1. Ahmed, K., Ewees, A. A., Hassanien, A. E. (2017). Prediction and management system for forest fires based on hybrid flower pollination optimization algorithm and adaptive neuro-fuzzy inference system. In Eighth International Conference on Intelligent Computing and Information Systems (ICICIS) Proceedings, Cairo (pp. 305–310).Google Scholar
  2. Aissa, B. C., & Fatima, C. (2015). Adaptive neuro-fuzzy control for trajectory tracking of a wheeled mobile robot. In 4th International Conference on Electrical Engineering (ICEE), Boumerdes (pp. 1–4).Google Scholar
  3. Aqil, M., Kita, I., Yano, A., & Nishiyama, S. (2006). Prediction of flood abnormalities for improved public safety using a modified adaptive neuro-fuzzy inference system. Water Science and Technology, 54(11–12), 11–19.CrossRefGoogle Scholar
  4. Boyacioglu, M. A., & Avci, D. (2010). An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange. Expert Systems with Applications, 37, 7908–7912.CrossRefGoogle Scholar
  5. Bui, D. T., Bui, Q.-T., Nguyen, Q.-P., Pradhan, B., Nampak, H., & Trinh, P. T. (2017). A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agricultural and Forest Meteorology, 233, 32–44.CrossRefGoogle Scholar
  6. Bui, D. T., Pradhan, B., Nampak, H., & Tran, Q. (2016). Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibility modeling in a high-frequency tropical cyclone area using GIS. Journal of Hydrology, 540, 317–330.CrossRefGoogle Scholar
  7. Chen, S. H., Lin, Y. H., Chang, L. C., & Chang, F. J. (2006). The strategy of building a flood forecast model by neuro-fuzzy network. Hydrological Processes, 20, 1525–1540.CrossRefGoogle Scholar
  8. Chiu, S. (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems, 2(3), 267–278.CrossRefGoogle Scholar
  9. Choi, C., Ji, J., Yu, M., Lee, T., Kang, M., & Yi, J. (2012). Urban flood forecasting using a neuro-fuzzy technique. WIT Transactions on The Built Environment, 122, 249–259.CrossRefGoogle Scholar
  10. Duong, T. H., Nguyen, D. C., Nguyen, S. D., & Hoang, M. H. (2013). An adaptive neuro-fuzzy inference system for seasonal forecasting of tropical cyclones making landfall along the Vietnam coast. In N. Nguyen, T. van Do, H. le Thi (Eds.), Advanced computational methods for knowledge engineering. Studies in computational intelligence (Vol. 479, pp. 225–236). Heidelberg: Springer.Google Scholar
  11. Hakim, S. J. S., & Razak, H. A. (2013). Adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) for structural damage identification. Structural Engineering and Mechanics, 45(6), 779–802.CrossRefGoogle Scholar
  12. Hossain, E., Turna, T. N., Soheli, S. J., & Kaiser, M. S. (2014). Neuro-fuzzy (NF)-based adaptive flood warning system for Bangladesh. In 3rd International Conference on Informatics, Electronics & Vision.Google Scholar
  13. Jang, J.-S. R. (1991). Fuzzy modeling using generalized neural networks and Kalman filter algorithm. In AAAI-91 Proceedings (pp. 762–767).Google Scholar
  14. Jang, J.-S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685.CrossRefGoogle Scholar
  15. Jang, J.-S. R., & Sun, C.-T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83(3), 378–406.CrossRefGoogle Scholar
  16. Khasiya, R. B. (2017). Flood forecasting using adaptive neuro-fuzzy inference system. International Journal of Advance Engineering and Research Development, 4(4), 210–213.Google Scholar
  17. le Cun, Y. (1988). A theoretical framework for back-propagation. In D. Touretzky, G. Hinton & T. Sejnowski (Eds.), Proceedings of the 1988 Connectionist Models Summer School, CMU, Pittsburg, PA (pp. 21–28).Google Scholar
  18. Mirrashid, M. (2014). Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm. Natural Hazards, 74(3), 1577–1593.CrossRefGoogle Scholar
  19. Mukerji, A., Chatterjee, C., & Raghuwanshi, N. S. (2009). Flood forecasting using ANN, neuro-fuzzy, and neuro-GA Models. Journal of Hydrologic Engineering, 14(6), 647–652.CrossRefGoogle Scholar
  20. Muradova, A. D., Tairidis, G. K., & Stavroulakis, G. Ε. (2017). Adaptive neuro-fuzzy vibration control of a smart plate. Numerical Algebra, Control and Optimization, 7(3), 251–271.MathSciNetCrossRefGoogle Scholar
  21. Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2004). A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, 291, 52–66.CrossRefGoogle Scholar
  22. Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2005). Short-term flood forecasting with a neuro-fuzzy model. Water Resource Research, 41, 1–16.CrossRefGoogle Scholar
  23. Patel, D., & Parekh, F. (2014). Flood forecasting using adaptive neuro-fuzzy inference system (ANFIS). International Journal of Engineering Trends and Technology (IJETT), 12(10), 510–514.CrossRefGoogle Scholar
  24. Pramanik, N., & Panda, R. K. (2009). Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction. Hydrological Sciences–Journal–des Sciences Hydrologiques, 54(2), 247–260.CrossRefGoogle Scholar
  25. Roodsari, B. K., Chandler, D. G., Kelleher, C., & Kroll, C. N. (2018). A comparison of SAC-SMA and adaptive neuro-fuzzy inference system for real-time flood forecasting in small urban catchments. Journal of Flood Risk Management, 12492, 1–12.Google Scholar
  26. Seckin, N. (2011). Modeling flood discharge at ungauged sites across Turkey using neuro-fuzzy and neural networks. Journal of Hydroinformatics, 13(4), 842–849.CrossRefGoogle Scholar
  27. Stavroulakis, G., Papachristou, I., Salonikidis, S., & Tairidis, G. K. (2011). Neuro-fuzzy control for smart structures. In Y. Tsompanakis & B. Topping (Eds.), Soft computing methods for civil and structural engineering (pp. 149–172). Stirlingshire: Saxe-Coburg Publications.CrossRefGoogle Scholar
  28. Supatmi, S., Hou, R., & Sumitra, I. D. (2019). Study of hybrid neuro-fuzzy inference system for forecasting flood event vulnerability in indonesia. Hindawi Computational Intelligence and Neuroscience, 2019, 1–13.CrossRefGoogle Scholar
  29. Tairidis, G. K. (2016). Optimal design of smart structures with intelligent control. Ph.D. Dissertation, Technical University of Crete.Google Scholar
  30. Tairidis, G. K. (2019). Vibration control of smart composite structures using shunted piezoelectric systems and neuro-fuzzy techniques. Journal of Vibration and Control.
  31. Tairidis, G. K., Muradova, A. D., & Stavroulakis, G. E. (2019). Dynamic morphing of smart trusses and mechanisms using fuzzy and neuro-fuzzy techniques. Frontiers in Built Environment—Computational Methods in Structural Engineering, 5, 32 (10 p).Google Scholar
  32. Tairidis, G., Papachristou, I., Katagas, M., & Stavroulakis, G. E. (2013). Neuro-fuzzy control of smart structures. In 10th HSTAM International Congress on Mechanics, Chania, 25–27 May 2013.Google Scholar
  33. Tairidis, G. K., & Stavroulakis, G. E. (2019). Fuzzy and neuro-fuzzy control for smart structures. In M. Khakifirooz, M. Fathi, P. Pardalos (Eds.), Computational intelligence and optimization methods for control engineering (in press).Google Scholar
  34. Ullah, N. (2013). Flood flow modeling in a river system using adaptive neuro-fuzzy inference system. Environmental Management and Sustainable Development, 2(2), 54–68.CrossRefGoogle Scholar
  35. Wang, L. X. (1994). Adaptive fuzzy systems and control: design and stability analysis. Upper Saddle River: Prentice Hall.Google Scholar
  36. Wijayanto, A. K., Sani, O., Kartika, N. D., & Herdiyeni, Y. (2017). Classification model for forest fire hotspot occurrences prediction using ANFIS algorithm. Earth and Environmental Science, 54, 012059.Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

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

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