Flood prediction using machine learning models: Literature review
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 …
The research on the advancement of flood prediction models contributed to risk reduction …
Comprehensive review: Advancements in rainfall-runoff modelling for flood mitigation
Runoff plays an essential part in the hydrological cycle, as it regulates the quantity of water
which flows into streams and returns surplus water into the oceans. Runoff modelling may …
which flows into streams and returns surplus water into the oceans. Runoff modelling may …
Prediction of water level and water quality using a CNN-LSTM combined deep learning approach
A Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) combined with a
deep learning approach was created by combining CNN and LSTM networks simulated …
deep learning approach was created by combining CNN and LSTM networks simulated …
Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US
Incomplete representations of physical processes often lead to structural errors in process-
based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow …
based (PB) hydrologic models. Machine learning (ML) algorithms can reduce streamflow …
[HTML][HTML] Particle swarm optimization based LSTM networks for water level forecasting: A case study on Bangladesh river network
Floods are one of the most catastrophic natural disasters. Water level forecasting is an
essential method of avoiding floods and disaster preparedness. In recent years, models for …
essential method of avoiding floods and disaster preparedness. In recent years, models for …
Neurocomputing in surface water hydrology and hydraulics: A review of two decades retrospective, current status and future prospects
Neurocomputing methods have contributed significantly to the advancement of modelling
techniques in surface water hydrology and hydraulics in the last couple of decades, primarily …
techniques in surface water hydrology and hydraulics in the last couple of decades, primarily …
Ensemble modelling framework for groundwater level prediction in urban areas of India
India is facing the worst water crisis in its history and major Indian cities which accommodate
about 50% of its population will be among highly groundwater stressed cities by 2020. In …
about 50% of its population will be among highly groundwater stressed cities by 2020. In …
Water clarity mapping of global lakes using a novel hybrid deep-learning-based recurrent model with Landsat OLI images
Abstract Information regarding water clarity at large spatiotemporal scales is critical for
understanding comprehensive changes in the water quality and status of ecosystems …
understanding comprehensive changes in the water quality and status of ecosystems …
A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events
CC Young, WC Liu, MC Wu - Applied Soft Computing, 2017 - Elsevier
Accurate rainfall-runoff modeling during typhoon events is an essential task for natural
disaster reduction. In this study, a novel hybrid model which integrates the outputs of …
disaster reduction. In this study, a novel hybrid model which integrates the outputs of …
[HTML][HTML] Flood stage forecasting using machine-learning methods: a case study on the Parma River (Italy)
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 …
Study on the Parma River (Italy) Next Article in Journal An Empirical Seasonal Rainfall …