Enhancing hydrological time series forecasting with a hybrid Bayesian-ConvLSTM model optimized by particle swarm optimization

Hydrological time series forecasting often relies on addressing the inherent uncertainties and complex temporal dependencies embedded in the data. This study presents an innovative hybrid framework, the Bayesian-ConvLSTM-PSO model, specifically designed to tackle these challenges. The framework syne...

Full description

Saved in:
Bibliographic Details
Published in:Acta geophysica Vol. 73; no. 4; pp. 3549 - 3566
Main Authors: Kilinc, Huseyin Cagan, Apak, Sina, Ergin, Mahmut Esad, Ozkan, Furkan, Katipoğlu, Okan Mert, Yurtsever, Adem
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.08.2025
Springer Nature B.V
Subjects:
Accuracy
Algorithms
Artificial intelligence
Artificial neural networks
Bayesian analysis
Bias
Decision making
Earth and Environmental Science
Earth Sciences
Energy consumption
Flow measurement
Forecasting
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Hydrologic data
Hydrology
Neural networks
Optimization
Particle swarm optimization
Rain
Research Article - Hydrology and Hydraulics
River engineering
Root-mean-square errors
Stream discharge
Stream flow
Streamflow data
Structural Geology
Time series
Trends
Uncertainty
Water quality
Water resources
Water shortages
Turkey
Deep learning
Bayesian method
Particle swarm optimization
Forecasting
Multi-head attention
ISSN:1895-7455, 1895-6572, 1895-7455
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Hydrological time series forecasting often relies on addressing the inherent uncertainties and complex temporal dependencies embedded in the data. This study presents an innovative hybrid framework, the Bayesian-ConvLSTM-PSO model, specifically designed to tackle these challenges. The framework synergistically combines 1D convolutional neural networks (CNNs), a convolutional Bayesian network, multi-head attention, and long short-term memory (LSTM) networks, with parameters optimized through particle swarm optimization (PSO). The fusion of the convolutional Bayesian network and 1D convolutional neural networks enhances feature robustness by capturing both probabilistic uncertainties and spatial patterns effectively. The multi-head attention model further amplifies this by focusing on the most relevant features, improving the learning process and ensuring better representation of complex temporal dependencies. The proposed model is rigorously tested on daily streamflow data from three flow measurement stations (FMS): Ahullu (D14A014), Kızıllı (D14A080), and Erenkaya (D14A127). Experimental results reveal that the Bayesian-ConvLSTM-PSO model achieves significant performance gains across various evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), determination coefficient ( R 2 ), Kling–Gupta efficiency (KGE), and bias factor (BF). Notably, the model demonstrates exceptional accuracy with an R 2 of 0.9950, a KGE of 0.9950, and a bias factor of 0.0003, surpassing the results of PSO-1D CNN-LSTM and benchmark models, such as DNN, DNN-LSTM, and 1D ConvLSTM. These compelling findings underscore the potential of the Bayesian-ConvLSTM-PSO framework as a robust and effective tool for applications in river engineering and hydrological time series forecasting.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1895-7455
1895-6572
1895-7455
DOI:10.1007/s11600-025-01570-0