The state of the art in deep learning applications, challenges, and future prospects: A comprehensive review of flood forecasting and management
Floods are a devastating natural calamity that may seriously harm both infrastructure and
people. Accurate flood forecasts and control are essential to lessen these effects and …
people. Accurate flood forecasts and control are essential to lessen these effects and …
Spatiotemporal forecasting in earth system science: Methods, uncertainties, predictability and future directions
Spatiotemporal forecasting (STF) extends traditional time series forecasting or spatial
interpolation problem to space and time dimensions. Here, we review the statistical, physical …
interpolation problem to space and time dimensions. Here, we review the statistical, physical …
[HTML][HTML] Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States
KMR Hunt, GR Matthews… - Hydrology and Earth …, 2022 - hess.copernicus.org
Accurate river streamflow forecasts are a vital tool in the fields of water security, flood
preparation and agriculture, as well as in industry more generally. Traditional physics-based …
preparation and agriculture, as well as in industry more generally. Traditional physics-based …
Comparative evaluation of LSTM, CNN, and ConvLSTM for hourly short-term streamflow forecasting using deep learning approaches
A Dehghani, HMZH Moazam, F Mortazavizadeh… - Ecological …, 2023 - Elsevier
This study investigates the effectiveness of three deep learning methods, Long Short-Term
Memory (LSTM), Convolutional Neural Network (CNN), and Convolutional Long Short-Term …
Memory (LSTM), Convolutional Neural Network (CNN), and Convolutional Long Short-Term …
An intelligent early flood forecasting and prediction leveraging machine and deep learning algorithms with advanced alert system
Flood disasters are a natural occurrence around the world, resulting in numerous casualties.
It is vital to develop an accurate flood forecasting and prediction model in order to curb …
It is vital to develop an accurate flood forecasting and prediction model in order to curb …
Deep learning approach with LSTM for daily streamflow prediction in a semi-arid area: a case study of Oum Er-Rbia river basin, Morocco
Daily hydrological modelling is among the most challenging tasks in water resource
management, particularly in terms of streamflow prediction in semi-arid areas. Various …
management, particularly in terms of streamflow prediction in semi-arid areas. Various …
Physics guided machine learning methods for hydrology
Streamflow prediction is one of the key challenges in the field of hydrology due to the
complex interplay between multiple non-linear physical mechanisms behind streamflow …
complex interplay between multiple non-linear physical mechanisms behind streamflow …
Short-and mid-term forecasts of actual evapotranspiration with deep learning
Evapotranspiration is a key component of the hydrologic cycle. Accurate short-, medium-,
and long-term forecasts of actual evapotranspiration (ET a) are crucial not only for …
and long-term forecasts of actual evapotranspiration (ET a) are crucial not only for …
Deep learning models to predict flood events in fast-flowing watersheds
This study aims to explore the reliability of flood warning forecasts based on deep learning
models, in particular Long-Short Term Memory (LSTM) architecture. We also wish to verify …
models, in particular Long-Short Term Memory (LSTM) architecture. We also wish to verify …
Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia
MNA Zakaria, AN Ahmed, MA Malek, AH Birima… - Heliyon, 2023 - cell.com
Accurate water level prediction for both lake and river is essential for flood warning and
freshwater resource management. In this study, three machine learning algorithms: multi …
freshwater resource management. In this study, three machine learning algorithms: multi …