Estimation of Missing Streamflow Data Using Anfis Models and Determination of the Number of Datasets for Anfis: The Case of Yeşi̇lirmak River

Abstract

Good data analysis is required for the optimal design of water resources projects. However, data are not regularly collected due to material or technical reasons, which results in incomplete-data problems. Available data and data length are of great importance to solve those problems. Various studies have been conducted on missing data treatment. This study used data from the flow observation stations on Yeşilırmak River in Turkey. In the first part of the study, models were generated and compared in order to complete missing data using Artificial Neural Network Fuzzy Inference Systems (ANFIS), multiple regression and Normal Ratio Method. Thus, it is tried to define the usability besides the other model to complete the missing data. Likewise, in the study. It is aimed to define the minimum number of data necessary for the use age of ANFIS. For this purpose in the second part of the study, the minimum number of data required for ANFIS models was determined using the optimum ANFIS model. Of all methods compared in this study, ANFIS models yielded the most accurate results. A 10-year training set was also found to be sufficient as a data set.

Akarsulardaki̇ Sedi̇ment Taşiniminin Yapay Si̇ni̇r Ağlari Ve Anfis Yöntemleri̇ Kullanilarak Tespi̇ti̇

“…Son yıllarda bu karmaşık problemlerin çözümünde yapay zeka teknikleri kullanılmaktadır [1,2,3]. Bunlara örnek olarak yapay sinir ağları (YSA) [4,5,6] ve uyarlamalı ağ tabanlı bulanık çıkarım sistemi (ANFIS) [7,8,9,10,11] gibi yöntemlerdir.…”