Floods are among the most destructive natural disasters, which are highly complex to model.
The research on the advancement of flood prediction models contributed to risk reduction …

Over the past decades, the scientific community has made significant efforts to simulate
flooding conditions using a variety of complex physically based models. Despite all …

In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …

Efficient simulation of rainfall-runoff relationships is one of the most complex problems owing
to the high number of interrelated hydrological processes. It is well-known that machine …

Ensemble flood forecasting has gained significant momentum over the past decade due to
the growth of ensemble numerical weather and climate prediction, expansion in high …

Accurate monthly runoff prediction is still challenging work regardless of the accessibility of
different modelling techniques, like the knowledge-driven or data-driven models, and human …

This study compares LSTM neural network and wavelet neural network (WNN) for spatio-
temporal prediction of rainfall and runoff time-series trends in scarcely gauged hydrologic …

This study proposes a new ensemble deep learning approach called LSTM-B by integrating
long-short term memory (LSTM) neural network and bagging ensemble learning strategy in …

Scour is a major issue which impacts the life of a hydraulic structure. In this work, we have
considered a bridge as an example of a hydraulic structure. Scour depth increases or …

Accurate storm surge forecasting is vital for saving lives and avoiding economic and
infrastructural damage. Failure to accurately predict storm surge can have catastrophic …