There has been an unsettling rise in the intensity and frequency of natural disasters due to
climate change and anthropogenic activities. Artificial intelligence (AI) models have shown …

Flood prediction has advanced significantly in terms of technique and capacity to achieve
policymakers' objectives of accurate forecast and identification of flood-prone and impacted …

Abstract Study region Lower Baro River, Ethiopia. Study focus This paper presents the
novelty of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo …

Urban pluvial flooding is a threatening natural hazard in urban areas all over the world,
especially in recent years given its increasing frequency of occurrence. In order to prevent …

Flood is one of the most disruptive natural hazards, responsible for loss of lives and damage
to properties. A number of cities are subject to monsoons influences and hence face the …

In this study, the applicability of machine learning models was investigated for real-time
flood forecasting of a small river basin with a short time of concentration and the modes were …

To study the Dongting Lake water level variation and its relationship with the upstream
Three Gorges Dam (TGD), a deep learning method based on a Long Short-Term Memory …

Water | Free Full-Text | Flood Stage Forecasting Using Machine-Learning Methods: A Case
Study on the Parma River (Italy) Next Article in Journal An Empirical Seasonal Rainfall …

To effectively prevent land subsidence over abandoned coal mines, it is necessary to
quantitatively identify vulnerable areas. In this study, we evaluated the performance of …

Hydrologic soil groups play an important role in the determination of surface runoff, which, in
turn, is crucial for soil and water conservation efforts. Traditionally, placement of soil into …