Rainfall-runoff (r-r) modeling at different time scales is considered as a significant issue in hydro-environmental planning. As a first hydrological implementation, for one-time-ahead r-r modeling of two watersheds with totally distinct climatic conditions, Genetic Algorithm (GA, as a global search technique) and Emotional Artificial Neural Network (EANN, as a new production of Artificial Intelligence (AI) based methods that simulated based on the brain neurophysiological structure) was combined. Determining the optimal architecture of AI-based networks is vital for increasing the accuracy of prediction by the network and also to reduce run-time. In the current study, GA has been implemented to choose the important features candidate as EANN input and automatically diagnose the optimal number of hidden nodes and hormones simultaneously. The acquired results indicated a better representation of the proposed hybrid GA-EANN model compared to the sole ANN and EANN. Numerical identification of obtained results revealed that the proposed hybrid GA-EANN model might enhance the better results than the EANN model up to 19% and 35% in terms of testing suitability criteria for Aji Chai and Murrumbidgee catchments, respectively.

在不同的时间尺度上，降雨径流（rr）模型被认为是水环境规划中的重要问题。作为第一个水文实施，对于两个具有完全不同气候条件的流域的一次性rr建模，遗传算法（GA，作为一种全球搜索技术）和情感人工神经网络（EANN，作为一种人工智能的新产物（结合了基于AI）的，基于大脑神经生理结构进行模拟的方法。确定基于AI的网络的最佳体系结构对于提高网络预测的准确性并减少运行时间至关重要。在当前的研究中，已经实施了遗传算法以选择重要特征候选者作为EANN输入，并同时自动诊断隐藏节点和激素的最佳数量。获得的结果表明，与唯一的ANN和EANN相比，该混合GA-EANN模型具有更好的表示能力。对获得的结果进行数值鉴定，结果表明，在针对阿吉柴和穆鲁姆比第集水区的适用性测试标准方面，所提出的混合GA-EANN模型可能比EANN模型获得更好的结果，分别高达19％和35％。