A comparative study of daily streamflow forecasting using firefly, artificial bee colony, and genetic algorithm-based artificial neural network

The management of water resources and the modeling of river flow have a prominent position within environmental research. They form a critical bridge between human societies and the delicate ecosystems they inhabit. Scholars have focused on benefiting more efficient methods based on the use of artif...

Full description

Saved in:
Bibliographic Details
Published in:Acta geophysica 2024, Vol.72 (6), p.4575-4595
Main Authors: Kilinc, Huseyin Cagan, Haznedar, Bulent, Katipoğlu, Okan Mert, Ozkan, Furkan
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Bees
Comparative studies
Daily forecasts
Earth and Environmental Science
Earth Sciences
Environmental management
Environmental research
Forecasting
Genetic algorithms
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Heuristic methods
Hydrologic models
Machine learning
Modelling
Neural networks
Research Article - Hydrology and Hydraulics
River flow
River forecasting
Rivers
Stream discharge
Stream flow
Streamflow forecasting
Structural basins
Structural Geology
Swarm intelligence
Water resources management
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The management of water resources and the modeling of river flow have a prominent position within environmental research. They form a critical bridge between human societies and the delicate ecosystems they inhabit. Scholars have focused on benefiting more efficient methods based on the use of artificial intelligence for river flow forecasting, notably because modeling hydrological systems is quite challenging. This study primarily centered on exploring the predictive capacities of hybrid models in establishing a link between daily flow data and prospective data. In the study, the mentioned algorithms, firefly algorithm (FFA), artificial bee colony (ABC), genetic algorithm (GA), were hybridized with the artificial neural network (ANN) model and data analyzes were examined with the stations in the Konya Closed Basin. A comparative analysis of FFA–ANN, GA–ANN, ABC–ANN, and long short-term memory (LSTM) models was conducted for daily flow forecasting for daily flow forecasting according to a range of graphical and statistical metrics. The outcomes indicate that the FFA–ANN hybrid model generally performed better than other models and the deep learning algorithm.
ISSN:1895-7455
1895-6572
1895-7455
DOI:10.1007/s11600-024-01362-y