One Day Ahead Stream Flow Forecasting
- https://doi.org/10.2991/ifsa-eusflat-15.2015.165How to use a DOI?
- Stream flow forecasting, One step-ahead forecasting, ANFIS, Artificial Neural Networks, Support Vector Machines.
Short-term stream flow forecasts are required for simulation, optimization, and decision-making purposes in applications ranging from hydropower planning to flood prevention. The particular case of one-day ahead stream flow forecasting is an important but difficult problem that has been increasingly studied using hybrid computational intelligence and machine learning techniques. However, these studies present several limitations. In this work we attempt to address those limitations by (1) replicating and validating previous works; (2) using more objective evaluation criteria; (3) applying several computational intelligence techniques to datasets representative of diverse geographic areas; (4) preprocessing data and performing an extensive parameter optimization in order to improve previous results.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Joao P. Carvalho AU - Filipe V. Camelo PY - 2015/06 DA - 2015/06 TI - One Day Ahead Stream Flow Forecasting BT - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 1168 EP - 1175 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.165 DO - https://doi.org/10.2991/ifsa-eusflat-15.2015.165 ID - Carvalho2015/06 ER -