River flow prediction is a challenging problem due to highly nonlinear hydrological
processes and high spatio-temporal variability. Here we present a hybrid network of …

Runoff forecasting is important for water resource management. Although deep learning
models have substantially improved the accuracy of runoff prediction, the temporal and …

Recently, deep learning (DL) models, especially those based on long short-term memory
(LSTM), have demonstrated their superior ability in resolving sequential data problems. This …

Accurate streamflow prediction is crucial for effective water resource management. However,
reliable prediction remains a considerable challenge because of the highly complex, non …

Deep learning has made significant advances in methodologies and practical applications
in recent years. However, there is a lack of understanding on how the long short-term …

Process-based streamflow prediction is subjected to large uncertainties in model
parameters and parameterizations related to the complex processes involved in streamflow …

In this paper, a novel method is developed for day-ahead daily river-flow forecasting based
on convolutional neural network (CNN). The proposed method incorporates both spatial and …

Abstract Kelantan river (Sungai Kelantan in Malaysia) basin is one of the essential
catchments as it has a history of flood events. Numerous studies have been conducted in …

This paper presents a dynamical neural network framework to understand how catchment
systems respond to daily rainfall-runoff processes over time. We developed an interactive …

In recent years, the Mekong River Basin (MRB), one of the largest river basins in Southeast
Asia, has experienced severe impacts from extreme droughts and floods. Streamflow …