This paper presents development of an artificial intelligence (AI)‐based model, genetic expression programming (GEP) to predict long‐term streamflow using large‐scale climate drivers as predictors. GEP is chosen over artificial neural networks (ANNs) model, as ANN is a black‐box model, whereas GEP is able to explain the developed forecast models with mathematical expressions. As a case study, 12 streamflow measuring stations were selected from four different regions of New South Wales (NSW) in eastern Australia. A number of climate indices, Pacific Decadal Oscillation (PDO), Indian Ocean Dipole (IOD), El Nino Southern Oscillation (ENSO) and ENSO Modoki index (EMI), were selected as candidate predictors based on the findings of some preliminary studies. Higher predictabilities of the GEP‐based models are evident from the Pearson correlation (r) values ranging between 0.57 and 0.97, which are mostly about twice the values achieved by multiple linear regression (MLR) models in the preliminary study. Performances of the developed models were assessed using standard statistical measures such as root relative squared error (RRSE), relative absolute error (RAE), root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE) and Pearson correlation (r) values. The developed models are able to predict spring streamflow up to 5 months in advance with significantly high correlation values.

中文翻译：

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利用大型气候驱动程序，先驱利用基因表达程序来预测澳大利亚的季节性流量

本文介绍了基于人工智能（AI）的模型，基因表达编程（GEP）的开发，该模型使用大规模气候驱动器作为预测因子来预测长期流量。与人工神经网络（ANN）模型相比，选择了GEP，因为ANN是黑盒模型，而GEP能够用数学表达式解释已开发的预测模型。作为案例研究，从澳大利亚东部新南威尔士州（NSW）的四个不同区域中选择了12个流量测量站。根据一些初步研究的结果，选择了多个气候指数，如太平洋十年涛动（PDO），印度洋偶极子（IOD），厄尔尼诺南方涛动（ENSO）和ENSO Modoki指数（EMI）作为候选预测指标。从Pearson相关性可以明显看出，基于GEP的模型具有更高的可预测性（r）值介于0.57至0.97之间，这大约是初步研究中通过多元线性回归（MLR）模型获得的值的两倍。使用标准统计量度（例如，根相对平方误差（RRSE），相对绝对误差（RAE），均方根误差（RMSE），平均绝对误差（MAE），纳什–萨特克利夫效率（NSE））评估开发模型的性能和皮尔逊相关（r）值。所开发的模型能够以非常高的相关性值提前5个月预测弹簧流量。