Deep learning has emerged as a powerful tool for streamflow forecasting and its
applications have garnered significant interest in the hydrological community. Despite the …

The community's well-being and economic livelihoods are heavily influenced by the water
level of watersheds. The changes in water levels directly affect the circulation processes of …

This study investigates the effectiveness of three deep learning methods, Long Short-Term
Memory (LSTM), Convolutional Neural Network (CNN), and Convolutional Long Short-Term …

Urban flooding is a major problem for cities around the world, with significant socio-
economic consequences. Conventional real-time flood forecasting models rely on …

Accurate streamflow estimation is crucial for proper water management for irrigation,
hydropower, drinking and industrial purposes. The main aim of this study to adopt new data …

Water level (WL) forecasting has become a difficult undertaking due to spatiotemporal
fluctuations in climatic factors and complex physical processes. This paper proposes a novel …

Streamflow forecasting is the most well studied hydrological science but still portray
numerous unknown knowledge. The conventional physical-based model is unable to yield a …

Runoff prediction with high accuracy is vital to protect people's properties and lives. In this
study, a Short-Term Streamflow Forecasting Framework combining Long Short-Term …

The present scenario of global climatic change challenges the sustainability and existence
of water bodies around the globe. Due to which, it is always important and necessary to …

The Tongtian River is the source of the Yangtze River and is a national key ecological
reserve in China. Monitoring and predicting the changes and mechanisms of the Tongtian …