Department of Constructional and Environmental Engineering, Kyungpook National University, Sangju, 37224, South Korea.
Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, 36040, South Korea.
Environ Monit Assess. 2018 Jun 12;190(7):392. doi: 10.1007/s10661-018-6768-2.
This paper proposes hybrid soft computing models for daily river stage modeling. The models combine variational mode decomposition (VMD) with different soft computing models, including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF). The performances of VMD-based models (VMD-ANN, VMD-ANFIS, and VMD-RF) are assessed by model efficiency indices and graphical comparison, and compared with those of single models (ANN, ANFIS, and RF) and ensemble empirical mode decomposition (EEMD)-based models (EEMD-ANN, EEMD-ANFIS, and EEMD-RF). Results show that VMD-ANN, VMD-ANFIS, and VMD-RF models are more efficient and accurate than ANN, ANFIS, and RF models, respectively, and slightly better than EEMD-ANN, EEMD-ANFIS, and EEMD-RF models, respectively. In terms of model efficiency and accuracy, the top five models are VMD-ANFIS, EEMD-ANFIS, VMD-ANN, VMD-RF, and ANFIS and the VMD-ANFIS model is the best. It is found that VMD can enhance the performance of conventional single soft computing models; VMD is more effective than EEMD for hybrid model development; and the ANFIS model combined with VMD and EEMD can yield better efficiency and accuracy than other models. Therefore, VMD-based hybrid modeling is a more effective method for reliable daily river stage modeling.
本文提出了基于混合软计算模型的日尺度河流水位建模方法。该模型将变分模态分解（VMD）与不同的软计算模型（包括人工神经网络（ANN）、自适应神经模糊推理系统（ANFIS）和随机森林（RF））相结合。通过模型效率指标和图形比较评估了基于 VMD 的模型（VMD-ANN、VMD-ANFIS 和 VMD-RF）的性能，并与单一模型（ANN、ANFIS 和 RF）和基于集合经验模态分解（EEMD）的模型（EEMD-ANN、EEMD-ANFIS 和 EEMD-RF）进行了比较。结果表明，VMD-ANN、VMD-ANFIS 和 VMD-RF 模型分别比 ANN、ANFIS 和 RF 模型更有效和准确，且略优于 EEMD-ANN、EEMD-ANFIS 和 EEMD-RF 模型。就模型效率和准确性而言，排名前五的模型分别为 VMD-ANFIS、EEMD-ANFIS、VMD-ANN、VMD-RF 和 ANFIS，其中 VMD-ANFIS 模型表现最佳。研究结果表明，VMD 可以增强传统单一软计算模型的性能；与 EEMD 相比，VMD 更适合于混合模型的开发；而将 VMD 和 EEMD 与 ANFIS 模型相结合，可获得比其他模型更好的效率和准确性。因此，基于 VMD 的混合建模是一种更有效的可靠日尺度河流水位建模方法。
