ABSTRACT

This paper proposes runoff models based on machine learning to estimate daily streamflows in Oued Sebaou watershed, a Mediterranean coastal basin located in northern Algeria. Therefore, we applied random forest (RF), artificial neural networks (ANN – under different training algorithms), and locally weighted linear regression (LWLR) using as input combinations of current and past rainfall amounts and previous values of streamflow. We selected streamflow and rainfall records to calibrate and validate the stated approaches. We used root mean square error (RMSE) and correlation coefficient (R) to evaluate the accuracy of the models. Analyses of the results show that RF provided the best outcomes for both training (RMSE = 4.7458 and R = 0.9834) and validation (RMSE = 2.3617 and R = 0.9719). The ANN calibrated with the Levenberg-Marquardt algorithm presented the second-best result, outperforming its counterparts and LWLR.

摘要

本文提出了基于机器学习的径流模型来估计位于阿尔及利亚北部的地中海沿岸盆地 Oued Sebaou 流域的每日流量。因此，我们应用了随机森林 (RF)、人工神经网络 (ANN – 在不同的训练算法下) 和局部加权线性回归 (LWLR)，将当前和过去的降雨量以及之前的流量值作为输入组合。我们选择了流量和降雨记录来校准和验证所述方法。我们使用均方根误差 (RMSE) 和相关系数 (R) 来评估模型的准确性。结果分析表明，RF 为训练（RMSE = 4.7458 和 R = 0.9834）和验证（RMSE = 2.3617 和 R = 0.9719）提供了最佳结果。