Department of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.
Department of Water Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran.
Environ Sci Pollut Res Int. 2022 Mar;29(15):21935-21954. doi: 10.1007/s11356-021-17443-0. Epub 2021 Nov 13.
River flow variations directly affect the hydro-climatological, environmental, and ecological characteristics of a region. Therefore, an accurate prediction of river flow can critically be important for water managers and planners. The present study aims to compare different data-driven models in predicting monthly flow. Two river catchments located in the Guilan province in Iran, where rivers play an essential role in agricultural productions (mainly rice), are studied. The monthly river flow dataset was provided by Guilan Regional Water Authority during 1986-2015. The models are derived from two different numerical types of stochastic and machine learning (ML) models. The stochastic model is seasonal autoregressive integrated moving average (SARIMA), and the MLs are least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), and group method of data handling (GMDH). The inputs were selected by autocorrelation and partial autocorrelation functions (ACF and PACF) from the flow rates of the previous months. The data was divided into 75% of training and 25% of testing phases, and then the mentioned models were implemented. Predictions were evaluated by the criteria of root mean square error (RMSE), normalized RMSE (NRMSE), and Nash Sutcliff (NS) coefficient. According to the calculated values of different criteria during the test phase, RMSE = 1.138 cms, NRMSE = 0.109, and NS = 0.826, it can be concluded that the SARIMA model was superior to its ML competitors. Among the ML models, GMDH had the best performance (by RMSE = 1.290 cms, NRMSE = 0.124, and NS = 0.777) because it has more optimization parameters and sample space for network make-up. The models were also evaluated in hydrological drought conditions of both rivers. It was resulted that the rivers' flow can be well predicted in drought conditions by using these models, especially the SARIMA stochastic model. According to the NRMSE values (ranged between 0.1 and 0.2), the accuracy of predictions is evaluated in the appropriate range, and the present study shows promising results of the current approaches. Consequently, a comparison between the performance of linear stochastic models and complex black-box MLs, reveals that linear stochastic models are more suitable for the current region's monthly river flow prediction.
河流流量的变化直接影响到一个地区的水文气候、环境和生态特征。因此，对河流流量进行准确预测对水资源管理者和规划者来说至关重要。本研究旨在比较不同的数据驱动模型在预测月流量方面的性能。选择了伊朗吉兰省的两个河流流域进行研究，这些流域在农业生产（主要是水稻）中起着重要作用。月河流流量数据集由吉兰地区水务局在 1986-2015 年期间提供。模型来自两种不同的数值类型，即随机和机器学习（ML）模型。随机模型是季节性自回归综合移动平均（SARIMA），ML 模型是最小二乘支持向量机（LSSVM）、自适应神经模糊推理系统（ANFIS）和数据处理组方法（GMDH）。输入是通过流量的自相关和偏自相关函数（ACF 和 PACF）从前几个月的流量中选择的。数据被分为 75%的训练数据和 25%的测试数据，然后实施了上述模型。通过均方根误差（RMSE）、归一化 RMSE（NRMSE）和纳什-苏特克里夫（NS）系数对预测进行了评估。根据测试阶段不同标准的计算值，RMSE=1.138 cms，NRMSE=0.109，NS=0.826，可以得出 SARIMA 模型优于其 ML 竞争对手的结论。在 ML 模型中，GMDH 表现最好（RMSE=1.290 cms，NRMSE=0.124，NS=0.777），因为它具有更多的网络组成优化参数和样本空间。还评估了这两个模型在两条河流的水文干旱条件下的性能。结果表明，这些模型可以很好地预测河流在干旱条件下的流量，尤其是 SARIMA 随机模型。根据 NRMSE 值（在 0.1 到 0.2 之间），预测的准确性在适当的范围内进行评估，本研究显示了当前方法的有前景的结果。因此，线性随机模型和复杂的黑盒 ML 之间的性能比较表明，线性随机模型更适合当前地区的月河流流量预测。
