In this paper, the precipitation and runoff time‐series data of the Shaharchay River basin from 2000 to 2017 are simulated by a modern hybrid artificial intelligence technique. In order to develop the mentioned artificial intelligence model, the extreme learning machine (ELM), differential evolution and wavelet transform are combined and the SAELM and WASAELM hybrid models are provided. Initially, the most effective lags of the time‐series data are distinguished using an autocorrelation function. Using the lags, seven artificial intelligence models are defined for each of the SAELM and WSAELM models. To simulate precipitation and runoff, the sym and coif mother wavelets are chosen as the optimal ones, respectively. For the best model, the values of R2, the scatter index and the Nash–Sutcliffe efficiency coefficient (NSC) for simulating precipitation yielded 0.967, 0.208 and 0.965, respectively. Furthermore, a sensitivity analysis shows that the lags (t‐1), (t‐2) and (t‐12) are regarded as the most effective input lags. Ultimately, an uncertainty analysis is carried out for the superior model that the performance of this model in simulating precipitation and runoff is over‐ and underestimated, respectively.

中文翻译：

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使用强大的机器学习模拟降雨和径流时间序列

本文采用现代混合人工智能技术对2000年至2017年Shaharchay流域的降水和径流时间序列数据进行了模拟。为了开发上述人工智能模型，将极限学习机（ELM），差分进化和小波变换相结合，并提供了SAELM和WASAELM混合模型。最初，使用自相关函数来区分时间序列数据中最有效的滞后。利用滞后，为SAELM和WSAELM模型中的每一个定义了七个人工智能模型。为了模拟降水和径流，分别选择了sym和coif母波作为最优子波。对于最佳模型，R2的值 散射指数和纳什-萨特克利夫效率系数（NSC）用于模拟降水分别产生0.967、0.208和0.965。此外，敏感性分析显示，滞后（t-1），（t-2）和（t-12）被认为是最有效的输入滞后。最终，对上级模型进行了不确定性分析，该模型在模拟降水和径流方面的性能分别被高估和低估了。