2026-10-19T00:33:51Zhttps://ebyu-rdlopen.rawdatalibrary.net/oai/request

oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/115032026-10-18T20:41:30Zopenairedrivercom_ebyu_10000col_ebyu_10001

A comparative study of daily streamflow forecasting using firefly, artificial bee colony, and genetic algorithm-based artificial neural networkKilinc, Huseyin CaganHaznedar, BulentKatipoğlu, Okan MertOzkan, FurkanArtificial bee colony algorithmDeep learningFirefly algorithmForecastingStreamflowKonya BasinKonya [Turkey]TurkeyBioluminescenceGenetic algorithmsLearning algorithmsLong short-term memoryStream flowWater managementArtificial beesBee colony algorithmsComparatives studiesFirefly algorithmsFlow forecastingHybrid modelStreamflow forecastingartificial neural networkcomparative studyforecasting methodgenetic algorithmmachine learningriver flowThe 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. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2024.Springer Science and Business Media Deutschland GmbH2024Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/11503https://doi.org/10.1007/s11600-024-01362-y1895-6572enActa Geophysicainfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/11503