Work

Quasi-Optimized LSTM Approach for River Water Level Forecasting

Year: 2025

Type: article

Abstract: This study explores the application of a Long Short-Term Memory (LSTM) model for river water level forecasting, emphasizing the critical role of hyper-parameters optimization. Similar to physical and numerical rainfall-runoff models, LSTM relies on parameters to drive its data-driven modeling process. The performance of such models is highly sensitive to the chosen hyper-parameters, making their optimization essential. To address this, three algorithms—Grid Search, Random Search, and Bayesian Search—were applied to identify the most effective hyper-parameter combinations. Cross-correlation analysis revealed that average rainfall had a stronger influence on river water levels than upstream point rainfall, leading to its selection as the model input. The optimization focused on five key hyper-parameters: neuron units, learning rate, dropout rate, number of epochs, and batch size. Results showed that, while Grid Search required the most computational time, both Random and Bayesian Search were more efficient. Notably, Bayesian Search yielded the best predictive performance with minimal time cost, making it the preferred optimization method. Additionally, reproducible LSTM simulations were conducted to ensure the consistency and practical applicability of the forecasting in real-world scenarios. Overall, Bayesian Search is recommended for optimizing LSTM models due to its balance of accuracy and computational efficiency in hydrological forecasting.

Source: Water

DOI: 10.3390/w17142087

Authors: Chung-Soo Kim, Kah-Hoong Kok, Cho-Rong Kim

Institution: Korea Institute of Civil Engineering and Building Technology

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Estimated APC paid: $3,137