Το έργο με τίτλο Neuro-fuzzy techniques and Natural Risk Management. Applications of ANFIS models in floods and comparison with other models από τον/τους δημιουργό/ούς Tairidis Georgios, Stojanovic Nikola, Stamenkovic Dusan, Stavroulakis Georgios διατίθεται με την άδεια Creative Commons Αναφορά Δημιουργού 4.0 Διεθνές
Βιβλιογραφική Αναφορά
G. K. Tairidis, N. Stojanovic, D. Stamenkovic, and G. E. Stavroulakis, “Neuro-fuzzy techniques and Natural Risk Management. Applications of ANFIS models in floods and comparison with other models,” in Natural Risk Management and Engineering: NatRisk Project, Springer Tracts in Civil Engineering, M. Gocić, G. T. Aronica, G. E. Stavroulakis, S. Trajković, Eds., Cham, Switzerland: Springer Nature, 2020, pp. 169–189, doi: 10.1007/978-3-030-39391-5_8.
https://doi.org/10.1007/978-3-030-39391-5_8
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.