Ali Danandeh MehrMir Jafar Sadegh SafariDanandeh Mehr, AliSafari, Mir Jafar Sadegh2025-10-062021978012820673710.1016/B978-0-12-820673-7.00007-X2-s2.0-85125224610https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125224610&doi=10.1016%2FB978-0-12-820673-7.00007-X&partnerID=40&md5=ee6330a943bbb7055c3e09ad705db8b2https://gcris.yasar.edu.tr/handle/123456789/9041https://doi.org/10.1016/B978-0-12-820673-7.00007-XThe state-of-the-art genetic programming (GP) has received a great deal of attention over the past few decades and has been applied to many research areas of water resources engineering including prediction of hydrometeorological variables design of hydraulic structures and recognition of hidden patterns in hydrological phenomena such as rainfall-runoff interaction between surface water and groundwater and time series modeling of streamflow. A fundamental advantage of this technique is the automatic generation of explicit solutions for a given problem which may offer new insights into the problem at hand. Considering the importance of accurate streamflow forecasts in water resources management this chapter presents a brief review on the recent applications of classical GP and its advanced versions in univariate streamflow modeling. The representative papers were selected from web of science database published in the current decade 2011-19. This chapter also includes a case study that compares two GP variants namely classical GP and gene expression programming for 1-month ahead forecasts of the mean monthly streamflow in the Sedre Stream a mountainous river in Antalya Basin Turkey. © 2022 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessGene Expression Programming, Genetic Programming, Sedre Stream, Streamflow, Time Series ModelingGene Expression ProgrammingGenetic ProgrammingTime Series ModelingSedre StreamStreamflowBook Part