Research on the long-term and short-term forecasts of navigable river’s water-level fluctuation based on the adaptive multilayer perceptron

Abstract

Water-level fluctuation (WLF) forecast plays an essential role in the regulations and management of the Yangtze River’s navigable rivers. Traditional methods for navigable rivers’ forecasts are often time-consuming and cost-inefficient, often requiring constant updates and are univariate in nature. This study utilized one of the Yangtze River’s navigable rivers, Nanjing navigable river, to perform forecasts employing deep learning techniques. For the first time, Nanjing navigable river’s WLF data was exploited to make multi-step univariate and multivariate time series forecasts, employing a deep learning technique, multilayer perceptron (MLP). The data consisted of 3545 days worth of WLF measurements from ten different navigable rivers and was allocated properly without data leakage into training and testing. MLP was built to take time series sequence inputs from up to ten navigable rivers on a daily basis and output fast, accurate, stable, and reliable short-term forecasts and undeviating trends for long-term forecasts. The models were also constructed in an adaptive way so they can self-update daily based on the daily measurements. The naive model was constructed as a baseline model to measure the improvement and the validity of MLP models. Univariate and multivariate adaptive models were then constructed based on the augmented data by a popular data-augmentation method, rolling windows. Hyperparameters of MLP models were optimized based on relevant testing and large scale grid-search. Both short-term and long-term forecasts showed promises of MLP model in time series forecast for navigable river water-level problems and they achieved at least 27.6% percent lower in root mean square error (RMSE) compared to the baseline. Since the total time required for the trained models to produce forecasts and self-update is at most 15 min on a local quad-core Intel Core i5 10th generation (2.0 GHz) and because of the nonnegotiable performance of the models, it was concluded that MLP is a fast, cost-efficient, and accurate alternative technique for navigable rivers WLF forecasts.