Real-time and short-term prediction of river flow is essential for efficient flood management. To obtain accurate flow predictions, a reliable rainfall-runoff model must be used. This study proposes the application of two evolutionary algorithms, particle swarm optimization (PSO) and genetic algorithm (GA), to train the artificial neural network (ANN) parameters in order to overcome the ANN drawbacks, such as slow learning speed and frequent trapping at local optimum. These hybrid ANN-PSO and ANN-GA approaches were validated to equip natural hazard decision makers with a robust tool for forecasting real-time streamflow as a function of combinations of different lagged rainfall and streamflow in a small catchment in Southeast Queensland, Australia. Different input combinations of lagged rainfall and streamflow (delays of one, two and three days) were tested to investigate the sensitivity of the model to the number of delayed days, and to identify the effective model input combinations for the accurate prediction of real-time streamflow, which has not yet been recognized in other studies. The results indicated that the ANN-PSO model significantly outperformed the ANN-GA model in terms of convergence speed, accuracy, and fitness function evaluation. Additionally, it was found that the rainfall and streamflow with 3-day lag time had less impact on the predicted streamflow of the studied basin, confirming that the flow of the studied river is significantly correlated with only 2-day lagged rainfall and streamflow.

实时和短期河流流量预报对于有效的洪水管理至关重要。为了获得准确的流量预测，必须使用可靠的降雨径流模型。这项研究提出了应用两种进化算法粒子群优化（PSO）和遗传算法（GA）来训练人工神经网络（ANN）参数的方法，以克服ANN的缺点，例如学习速度慢和频繁陷井。局部最优。这些混合的ANN-PSO和ANN-GA方法经过验证，可为自然灾害决策者配备一个强大的工具，用于预测澳大利亚昆士兰东南部小流域中不同的滞后降雨和水流的组合带来的实时水流。降雨和水流滞后的不同输入组合（延迟为一，（两天和三天）进行了测试，以调查模型对延迟天数的敏感性，并确定有效模型输入组合，以准确预测实时流量，这在其他研究中尚未得到认可。结果表明，在收敛速度，准确性和适应度函数评估方面，ANN-PSO模型明显优于ANN-GA模型。此外，发现滞后3天的降雨和水流对研究流域的预测水流影响较小，这证实了研究河流的流量仅与2天的滞后降雨和水流显着相关。并确定有效的模型输入组合，以准确预测实时流量，这在其他研究中尚未得到认可。结果表明，在收敛速度，准确性和适应度函数评估方面，ANN-PSO模型明显优于ANN-GA模型。此外，发现滞后3天的降雨和水流对研究流域的预测水流影响较小，这证实了研究河流的流量仅与2天的滞后降雨和水流显着相关。并确定有效的模型输入组合，以准确预测实时流量，这在其他研究中尚未得到认可。结果表明，在收敛速度，准确性和适应度函数评估方面，ANN-PSO模型明显优于ANN-GA模型。此外，发现滞后3天的降雨和水流对研究流域的预测水流影响较小，这证实了研究河流的流量仅与2天的滞后降雨和水流显着相关。