Quantitative improvement of streamflow forecasting accuracy in the Atlantic zones of Canada based on hydro-meteorological signals: A multi-level advanced intelligent expert framework

• Published in: Ecological informatics 2024-05, Vol.80, p.102455, Article 102455
• Main Authors: Jamei, Mozhdeh, Jamei, Mehdi, Ali, Mumtaz, Karbasi, Masoud, Farooque, Aitazaz A., Malik, Anurag, Cheema, Saad Javed, Esau, Travis J., Yaseen, Zaher Mundher
• Format: Article
• Language: English
• Subjects: Boruta-CART; CNN-BiGRU; Hydro-meteorological drivers; Multi-temporal; Multivariate variational mode decomposition; Streamflow forecasting
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2023.102455

Developing reliable streamflow forecasting models is critical for hydrological tasks such as improving water resource management, analyzing river patterns, and flood forecasting. In this research, for the first time, an emerging multi-level TOPSIS (technique for order preference by similarity to the ideal solution) -based hybridization comprised of the Boruta classification and regression tree (Boruta-CART) feature selection, multivariate variational mode decomposition (MVMD), and a hybrid Convolutional Neural Network (CNN) Bidirectional Gated Recurrent Unit (CNN-BiGRU) deep learning was adopted to multi-temporal (one and three days ahead) forecast the daily streamflow in the Rivers of Prince Edward Island, Canada. For this aim, in the first step, the Boruta-CART feature selection technique determines the most effective lagged components among all the antecedent two-day information (i.e., t-1 and t-2) of hydro-meteorological features (from 2015 to 2020), including the water level, mean air temperature, heat degree days, total precipitation, dew point temperature, and relative humidity in the Bear and Winter Rivers of Prince Edward Island, Canada. Afterwards, a multivariate variational mode decomposition (MVMD) decomposes the input time series to decrease the complexity and non-linearity of the non-stationary ones before feeding the deep learning (DL) models. Here, the CNN-GRU was employed as the primary DL model, along with the kernel extreme machine method (KELM), random variational function link (RVFL), and hybrid CNN bidirectional recurrent neural network (CNN-BiRNN) as the comparative models. A TOPSIS scheme applying several performance measures like the correlation coefficient (R), root mean square error (RMSE), and reliability was designed for the robustness assessment of the hybrid (MVM-CNN-BiGRU, MVM-CNN-BiRNN, MVM-RVFL, and MVM-KELM) and standalone models. The computational outcomes revealed that in the Bear River, the MVM-CNN-BiGRU, owing to its best forecasting performance (one day ahead: TOPSIS score 1, R = 0.960, RMSE = 0.098, and reliability = 65.082; three days ahead: TOPSIS score = 0.999, R = 0.924, and RMSE = 0.33) outperformed the other hybrid models, followed by the MVM-CNN-BiRNN, MVM-RVFL, and MVM-KELM, respectively. Moreover, in the Winter River, the MVM-CNN-BiGRU in terms of (one-day ahead: TOPSIS score = 0.890, R = 0.955, RMSE = 0.274, and reliability = 34.004; three-days ahead: TOPSIS score = 0.686, R = 0.924, and RMSE = 0.330) was