Comparative evaluation of attention-based and transformer deep learning models for multivariate daily streamflow prediction in rivers of West Azerbaijan, Iran

Beyond Traditional Hydrological Models: Transformer‐Based Deep Learning for Future Streamflow and Flood Risk Assessment in the Brahmaputra River Basin

This study presents an integrated framework combining process‐based hydrological modelling with advanced deep learning techniques to improve climate‐driven streamflow prediction and flood risk assessment in the Brahmaputra River Basin (BRB) at Bahadurabad. Unlike conventional comparative studies, this work explicit...

Peak-oriented one-day-ahead streamflow forecasting using hybrid deep learning: uncertainty quantification, SHAP-based interpretability, and MCDA-based model selection

Analysis and Comparative Prediction of Daily ET₀ Using FAO Penman-Monteith Integrated with LSTM and BiLSTM Models in Pinrang Regency

Accurate and adaptive irrigation water management is essential for maintaining rice productivity in regions with high climatic variability, such as Pinrang Regency, Indonesia. Conventional irrigation practices based on fixed schedules often fail to respond daily weather dynamics, resulting in inefficient water use. Thi...

A comparison of groundwater level forecast modelling using deep learning models combined with EMD and EEMD in Cangzhou, China

Accurate groundwater level forecasting is crucial for sustainable water resource management and environmental protection. The main objective of this study was to develop hybrid models combining deep learning models with data processing techniques for groundwater level forecasting. Two groundwater level observations in...

Evaluation Of CNN-LSTM with Attention for Forest Fire Prediction in Indonesia: Challenges of  Imbalanced Data

It is concluded that deep learning approaches for forest fire prediction in tropical regions face significant challenges related to class imbalance and temporal stability, necessitating the development of more adaptive modeling strategies.

Performance of deep learning based long-term CHIRPS rainfall prediction using CNN and LSTM models in Karnataka State, India