Abstract

The applicability of four machine learning (ML) methods, ANFIS-PSO, ANFIS-FCM, MARS and M5Tree, together with multi model simple averaging (MM-SA) ensemble method, is investigated in rainfall-runoff modeling at hourly timescale. The results are compared with the conceptual EBA4SUB model using rainfall and runoff data from Samoggia River basin, Italy. The capability of the methods is measured using five statistics, Nash–Sutcliffe efficiency, root mean squared error, mean absolute error, scatter index, and adjusted index of agreement. Comparison of single ML reveals that the ANFIS-PSO, ANFIS-FCM and MARS produce similar accuracy which is better than the M5Tree model. MM-SA ensemble model improves the accuracy of ANFIS-PSO, ANFIS-FCM, MARS and M5Tree models with respect to RMSE by 8.5%, 5%, 7.4% and 28.8%, respectively. Comparison with the conceptual event-based method indicates that the ML methods generally performs superior to the EBA4SUB; however, latter method provides better accuracy than the M5Tree and MARS in some cases.

Graphic abstract

中文翻译：

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使用几种机器学习方法和基于事件的概念模型进行短期降雨-径流建模

摘要

研究了每小时小时尺度下降雨径流建模中四种机器学习（ML）方法ANFIS-PSO，ANFIS-FCM，MARS和M5Tree的适用性，以及多模型简单平均（MM-SA）集成方法。使用意大利萨莫贾河流域的降雨和径流数据，将结果与概念性EBA4SUB模型进行了比较。该方法的能力使用五个统计量来衡量：纳什-萨特克利夫效率，均方根误差，平均绝对误差，散布指数和调整后的一致性指数。单个ML的比较显示ANFIS-PSO，ANFIS-FCM和MARS产生了相似的精度，这优于M5Tree模型。MM-SA集成模型相对于RMSE分别提高了ANFIS-PSO，ANFIS-FCM，MARS和M5Tree模型的准确性，分别为8.5％，5％，7.4％和28.8％。与基于事件的概念方法的比较表明，机器学习方法的性能通常优于EBA4SUB。但是，在某些情况下，后一种方法比M5Tree和MARS提供更好的准确性。

图形摘要