Novel hybrid model to improve the monthly streamflow prediction: Integrating ANN and PSO

Precise streamflow forecasting is crucial when designing water resource planning and management, predicting flooding, reducing flood threats. This study invented a novel approach for the monthly of Tigris River in Amarah City, Iraq, by integrating an artificial neural network (ANN) with particle swarm optimisation algorithm (PSO), depending on data preprocessing. Historical were utilised from (2010 to 2020). The primary conclusions this are that preprocessing enhances quality identifies optimal predictor scenario. In addition, it was revealed PSO effectively forecasts parameters suggested model. Also, outcomes indicated successfully simulated according multiple statistical criteria, including R2, RMSE, MAE.