Determination of the number of clusters used in fuzzy inference systems by means of K-means and modeling of dam volume: Kestel dam example

Autor:	Tülay Suğra Küçükerdem, Murat Kilit, Kemal Saplioğlu
Rok vydání:	2019
Předmět:	Adaptive neuro fuzzy inference system Statistics 010501 environmental sciences Flow modeling 01 natural sciences 0105 earth and related environmental sciences Mathematics
Zdroj:	Pamukkale University Journal of Engineering Sciences. 25:962-967
ISSN:	1300-7009 1986-2008
DOI:	10.5505/pajes.2019.99223
Popis:	Correct planning of water resources is important for the efficient use of rapidly decreasing water resources in the future. Flow modeling and flow estimations in the planning of water resource are the basis of studies. In this study, it is aimed to estimate monthly volumes by using ANFIS model based on the data of 1986-2008 for Sandikli Kestel dam. In the system, the volume of the previous months, the volume of the incoming and outgoing volumes and the amount of evaporation were used as input variables. In ANFIS method, the number of clusters used for the inputs was obtained by the method of K-means. Different clusters formed by K-averages were modeled in ANFIS and the results were compared. The optimal number of clusters for each input value is determined. Models have been established in this way. As a result, it has been found that the models made according to the optimal number of clusters yield results with lower error percentage compared to randomly generated models.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3688df8bf9f8713411b020f33fa1d56d https://doi.org/10.5505/pajes.2019.99223 Zobrazit plný text záznamu Plný text ve formátu PDF