One of the frequently adopted hybridizations within the scope of rainfall-runoff modeling rests on directing various outputs simulated from the conceptual rainfall-runoff (CRR) models to machine learning (ML) techniques. In those coupled model exercises, after the parameter calibrations of the CRR models are made, their specific outputs constitute auxiliary inputs for the ML model training. However, in this parallel hybridization comprising two consecutive processes, performing the cascade calibration of CRR and ML models increases the computational complexity. Moreover, the mutual interaction between the parameters governing CRR and ML models is also not considered. In this study, to cope with the handicaps mentioned, artificial neural networks (ANN) and support vector regression (SVR) were separately embedded into a monthly lumped CRR model. The dynamic water balance model (dynwbm) was preferred as the CRR model. Then, all free parameters within these nested hybrid models were calibrated simultaneously. The ML parts within the nested schemes manipulate various output variants derived with three conceptual parameters for monthly runoff simulation. These new hybrid models equipped with an automatic calibration algorithm were applied at several locations in the Gediz River Basin of western Turkey. The performance measures regarding mean and high flows indicated that the nested hybrid models outperformed the standalone models (i.e., dynwbm, ANN, and SVR) and coupled model variants. Thus, the credibility of a novel modeling strategy, which takes advantage of the supplementary strengths of a conceptual model and different ML techniques, was demonstrated.

降雨-径流模型范围内经常采用的混合方法之一是将从概念降雨-径流（CRR）模型模拟​​的各种输出引导到机器学习（ML）技术。在那些耦合的模型练习中，在对CRR模型进行参数校准之后，它们的特定输出构成了ML模型训练的辅助输入。但是，在包括两个连续过程的并行杂交中，执行CRR和ML模型的级联校准会增加计算复杂性。此外，也没有考虑控制CRR和ML模型的参数之间的相互影响。在这项研究中，为应对上述障碍，将人工神经网络（ANN）和支持向量回归（SVR）分别嵌入到每月集总CRR模型中。dynwbm）优选作为CRR模型。然后，同时校准这些嵌套混合模型中的所有自由参数。嵌套方案中的ML部分可操纵由三个概念参数得出的各种输出变量，以进行月度径流模拟。这些配备了自动校准算法的新型混合模型已在土耳其西部的吉迪兹河流域的多个地点使用。关于平均流量和高流量的性能度量表明，嵌套混合模型优于独立模型（即dynwbm，ANN和SVR）和耦合模型变体。因此，证明了利用概念模型和不同机器学习技术的补充优势的新型建模策略的可靠性。