Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
EN
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| URI: | http://purl.tuc.gr/dl/dias/652E1B23-4C2D-4F6D-9BAD-F61BD2F1A0EF | ||
| Έτος | 2020 | ||
| Τύπος | Κεφάλαιο σε Βιβλίο | ||
| Άδεια Χρήσης |
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| 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 | ||
| Εμφανίζεται στις Συλλογές |
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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