Comparison of different heuristic and decomposition techniques for river stage modeling.

Abstract

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.