Saad Sh. SammenMohammad EhteramS. I. AbbaR. A. AbdulkadirAli Najah AhmedAhmed El-Shafie2024-05-202024-05-202021Sammen, S. S., Ehteram, M., Abba, S. I., Abdulkadir, R. A., Ahmed, A. N., & El-Shafie, A. (2021). A new soft computing model for daily streamflow forecasting. Stochastic Environmental Research and Risk Assessment, 35(12), 2479-2491.https://repoemc.ukm.my/handle/123456789/1516The ability to forecast streamflow is crucial, as it can help mitigate flood risks. Long-term stream flow data records are needed for hydropower plant construction, flood prediction, watershed management, and long-term water supply use. An accurate assessment of streamflow is considered as very challenging and critical tasks. A new predicting model is developed in this research, combining the technique of sunflower optimization (SFA) as an evolutionary algorithm with the multi-layer perceptron (MLP) algorithm to predict streamflow in Malaysia's Jam Seyed Omar (JSO) and Muda Di Jeniang (MDJ) stations.enMLPStreamflowSunflower optimizationPrincipal component analysisJournal