Abstract Skilful forecasting of monthly streamflow in intermittent rivers is a challenging task in stochastic hydrology. In this study, genetic algorithm (GA) was combined with gene expression programming (GEP) as a new hybrid model for month ahead streamflow forecasting in an intermittent stream. The hybrid model was named GEP-GA in which sub-expression trees of the best evolved GEP model were rescaled by appropriate weighting coefficients through the use of GA optimizer. Auto-correlation and partial auto-correlation functions of the streamflow records as well as evolutionary search of GEP were used to identify the optimum predictors (i.e., number of lags) for the model. The proposed methodology was demonstrated using monthly streamflow data from the Shavir Creek in Iran. Performance of the GEP-GA was compared to that of classic genetic programming (GP), GEP, multiple linear regression and GEP-linear regression models developed in the present study as the benchmarks. The results showed that the GEP-GA outperforms all the benchmarks and motivated to be used in practice.

中文翻译：

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一种改进的间歇性河流径流预测基因表达编程模型

摘要 对间歇性河流的月流量进行熟练的预测是随机水文学中的一项具有挑战性的任务。在这项研究中，遗传算法 (GA) 与基因表达编程 (GEP) 相结合，作为一种新的混合模型，用于间歇性河流中的一个月前流量预测。混合模型被命名为 GEP-GA，其中最佳进化 GEP 模型的子表达式树通过使用 GA 优化器通过适当的加权系数重新缩放。流记录的自相关和偏自相关函数以及 GEP 的进化搜索被用来确定模型的最佳预测因子（即滞后数）。使用来自伊朗 Shavir Creek 的月流量数据演示了提议的方法。GEP-GA 的性能与作为基准的本研究中开发的经典遗传编程 (GP)、GEP、多元线性回归和 GEP-线性回归模型的性能进行了比较。结果表明，GEP-GA 优于所有基准，并有动力在实践中使用。