Abstract Despite of diverse progressions in hydrological modeling techniques, the necessity of a robust, accurate, reliable, and trusted expert system for real-time stream flow prediction still exists. The intention of the present study is to establish a new complementary data-intelligence (DI) model called wavelet extreme learning machine (WA-ELM) for forecasting river flow in a semi-arid environment. The monthly river flow data for the period 1991-2010 is used to calibrate and validate the applied predictive model, developed using antecedent flow data as predictor. The prediction efficiency of the developed WA-ELM model is validated against stand-alone ELM model. The performance of the models is diagnosed using multiple statistical metrics and graphical analysis visualization. The results reveal that incorporation of data pre-processing wavelet approach with ELM model enhances the river flow predictability. In quantitative term, the root-mean-square error (RMSE) and mean absolute error (MAE) measurements are reduced by 65% and 67% using WA-ELM over ELM model, respectively. The Taylor diagram reveals much closer proximity and the Violin plot shows similar distribution of WA-ELM simulated river flow to the observed river flow compared to stand-alone ELM simulated river flow. The hybridization of wavelet decomposition method with ELM model improves the ability of ELM model to extract the required information for modeling the non-stationary and high stochastic river flow pattern. Overall, the study reveals that WA-ELM can be a reliable methodology for modeling river flow in semi-arid environment and for different regimes (i.e., low-, medium- and high-flow).

中文翻译：

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用于河流流量模拟的互补数据智能模型

摘要 尽管水文建模技术取得了不同的进展，但仍然需要一个健壮、准确、可靠和值得信赖的实时流量预测专家系统。本研究的目的是建立一种新的互补数据智能 (DI) 模型，称为小波极限学习机 (WA-ELM)，用于预测半干旱环境中的河流流量。1991 年至 2010 年期间的每月河流流量数据用于校准和验证应用预测模型，该模型使用先行流量数据作为预测器开发。已开发的 WA-ELM 模型的预测效率已针对独立 ELM 模型进行了验证。使用多个统计指标和图形分析可视化来诊断模型的性能。结果表明，数据预处理小波方法与 ELM 模型的结合增强了河流流量的可预测性。在定量方面，使用 WA-ELM 优于 ELM 模型，均方根误差 (RMSE) 和平均绝对误差 (MAE) 测量值分别降低了 65% 和 67%。泰勒图显示了更接近的接近度，小提琴图显示了与独立 ELM 模拟河流流量相比，WA-ELM 模拟河流流量与观察到的河流流量的相似分布。小波分解方法与ELM模型的混合提高了ELM模型提取非平稳和高随机河流流型建模所需信息的能力。全面的，