Recent advances and new frontiers in riverine and coastal flood modeling
K Jafarzadegan, H Moradkhani… - Reviews of …, 2023 - Wiley Online Library
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 …
flooding conditions using a variety of complex physically based models. Despite all …
An overview of emotion in artificial intelligence
G Assunção, B Patrão… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
The field of artificial intelligence (AI) has gained immense traction over the past decade,
producing increasingly successful applications as research strives to understand and exploit …
producing increasingly successful applications as research strives to understand and exploit …
Short-term rainfall forecasting using machine learning-based approaches of PSO-SVR, LSTM and CNN
Short-term rainfall forecasting plays an important role in hydrologic modeling and water
resource management problems such as flood warning and real time control of urban …
resource management problems such as flood warning and real time control of urban …
Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting
Operational flood control systems depend on reliable and accurate forecasts with a suitable
lead time to take necessary actions against flooding. This study proposed a Long Short …
lead time to take necessary actions against flooding. This study proposed a Long Short …
Long lead-time daily and monthly streamflow forecasting using machine learning methods
Long lead-time streamflow forecasting is of great significance for water resources planning
and management in both the short and long terms. Despite of some studies using machine …
and management in both the short and long terms. Despite of some studies using machine …
IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling
As a complex hydrological problem, rainfall-runoff (RR) modeling is of importance in runoff
studies, water supply, irrigation issues, and environmental management. Among the variety …
studies, water supply, irrigation issues, and environmental management. Among the variety …
Wastewater treatment plant performance analysis using artificial intelligence–an ensemble approach
In the present study, three different artificial intelligence based non-linear models, ie feed
forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support …
forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support …
Simulation and forecasting of streamflows using machine learning models coupled with base flow separation
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 …
to the high number of interrelated hydrological processes. It is well-known that machine …
Spatial-temporal flood inundation nowcasts by fusing machine learning methods and principal component analysis
The frequency and severity of floods have noticeably increased worldwide in the last
decades due to climate change and urbanization. This study aims to build an urban flood …
decades due to climate change and urbanization. This study aims to build an urban flood …
A comprehensive assessment of water storage dynamics and hydroclimatic extremes in the Chao Phraya River Basin during 2002–2020
T Kinouchi, T Sayama - Journal of Hydrology, 2021 - Elsevier
A holistic assessment of the hydroclimatic extremes, which have caused tremendous
environmental, societal, and economic losses globally, is imperative for the highly …
environmental, societal, and economic losses globally, is imperative for the highly …