Accurate discharge and water level forecasting using ensemble learning with genetic algorithm and singular spectrum analysis-based denoising

Anh Duy Nguyen, Phi Le Nguyen, Hung Viet Vu, Viet Quoc Pham, Huy Viet Nguyen, Hieu Minh Nguyen, Hung Thanh Nguyen, Kien Nguyen

Abstract:

Forecasting discharge (Q) and water level (H) are essential factors in hydrological research and food
prediction. In recent years, deep learning has emerged as a viable technique for capturing the nonlinear relationship of historical data to generate highly accurate prediction results. Despite the success
in various domains, applying deep learning in Q and H prediction is hampered by three critical issues: a
shortage of training data, the occurrence of noise in the collected data, and the difculty in adjusting
the model’s hyper-parameters. This work proposes a novel deep learning-based Q–H prediction model
that overcomes all the shortcomings encountered by existing approaches. Specifcally, to address
data scarcity and increase prediction accuracy, we design an ensemble learning architecture that takes
advantage of multiple deep learning techniques. Furthermore, we leverage the Singular-Spectrum
Analysis (SSA) to remove noise and outliers from the original data. Besides, we exploit the Genetic
Algorithm (GA) to propose a novel mechanism that can automatically determine the prediction
model’s optimal hyper-parameters. We conducted extensive experiments on two datasets collected
from Vietnam’s Red and Dakbla rivers. The results show that our proposed solution outperforms
current techniques across a wide range of metrics, including NSE, MSE, MAE, and MAPE. Specifcally,
by exploiting the ensemble learning technique, we can improve the NSE by at least 2%. Moreover,
with the aid of the SSA-based data preprocessing technique, the NSE is further enhanced by more
than 5%. Finally, thanks to GA-based optimization, our proposed model increases the NSE by at least
6% and up to 40% in the best case.