Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work
Flooding produces debris and waste including liquids, dead animal bodies and hazardous
materials such as hospital waste. Debris causes serious threats to people's health and can …
materials such as hospital waste. Debris causes serious threats to people's health and can …
Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
Over the past 15 years, artificial neural networks (ANNs) have been used increasingly for
prediction and forecasting in water resources and environmental engineering. However …
prediction and forecasting in water resources and environmental engineering. However …
A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
Develo** a hydrological forecasting model based on past records is crucial to effective
hydropower reservoir management and scheduling. Traditionally, time series analysis and …
hydropower reservoir management and scheduling. Traditionally, time series analysis and …
Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds
In the past decade, machine learning methods for empirical rainfall–runoff modeling have
seen extensive development and been proposed as a useful complement to physical …
seen extensive development and been proposed as a useful complement to physical …
Suspended sediment load prediction of river systems: An artificial neural network approach
Information on suspended sediment load is crucial to water management and environmental
protection. Suspended sediment loads for three major rivers (Mississippi, Missouri and Rio …
protection. Suspended sediment loads for three major rivers (Mississippi, Missouri and Rio …
Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting
This paper traces two decades of neural network rainfall-runoff and streamflow modelling,
collectively termed 'river forecasting'. The field is now firmly established and the research …
collectively termed 'river forecasting'. The field is now firmly established and the research …
A comparative analysis of training methods for artificial neural network rainfall–runoff models
This paper compares various training methods available for training multi-layer perceptron
(MLP) type of artificial neural networks (ANNs) for modelling the rainfall–runoff process. The …
(MLP) type of artificial neural networks (ANNs) for modelling the rainfall–runoff process. The …
Short‐term flood forecasting with a neurofuzzy model
This study explores the potential of the neurofuzzy computing paradigm to model the rainfall‐
runoff process for forecasting the river flow of Kolar basin in India. The neurofuzzy computing …
runoff process for forecasting the river flow of Kolar basin in India. The neurofuzzy computing …
A framework for modeling flood depth using a hybrid of hydraulics and machine learning
Solving river engineering problems typically requires river flow characterization, including
the prediction of flow depth, flow velocity, and flood extent. Hydraulic models use governing …
the prediction of flow depth, flow velocity, and flood extent. Hydraulic models use governing …
Flood estimation at ungauged sites using artificial neural networks
Artificial neural networks (ANNs) have been applied within the field of hydrological
modelling for over a decade but relatively little attention has been paid to the use of these …
modelling for over a decade but relatively little attention has been paid to the use of these …