@inproceedings{061e7c1de002428c95c77125c6a0f912,
title = "B{\"o}lgesel veriler {\"u}zerinde yapilan kisa d{\"o}nem su talep tahmini",
abstract = "Limited water resources and changing climatic conditions make water one of the critical natural resources. In order to manage this limited resource in the most effective way, real-time monitoring and automatic control systems are becoming increasingly popular. Water demand forecasting is one of the important subjects in these studies. Accurate water demand forecasting increases efficiency in the management of water networks and also allows for leak/fraud detection. In this work, we carry out short term water demand forecasting using water consumption data collected from water meters in a regional area. For forecasting, we first clean water consumption data, extract various features and apply machine learning methods for forecasting. After giving the experimental results we discuss future improvements.",
keywords = "Feature extraction, Machine learning, Water demand forecasting",
author = "{Zeynep Yildiz}, Tugba and Tevfik Aytekin",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 27th Signal Processing and Communications Applications Conference, SIU 2019 ; Conference date: 24-04-2019 Through 26-04-2019",
year = "2019",
month = apr,
doi = "10.1109/SIU.2019.8806415",
language = "T{\"u}rk{\c c}e",
series = "27th Signal Processing and Communications Applications Conference, SIU 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "27th Signal Processing and Communications Applications Conference, SIU 2019",
}
