Federated learning for smart cities: A comprehensive survey
With the advent of new technologies such as the Artificial Intelligence of Things (AIoT), big
data, fog computing, and edge computing, smart city applications have suffered from issues …
data, fog computing, and edge computing, smart city applications have suffered from issues …
A review of machine learning applications in wildfire science and management
Artificial intelligence has been applied in wildfire science and management since the 1990s,
with early applications including neural networks and expert systems. Since then, the field …
with early applications including neural networks and expert systems. Since then, the field …
Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation
Runoff forecasting is an important approach for flood mitigation. Many machine learning
models have been proposed for runoff forecasting in recent years. To reconstruct the time …
models have been proposed for runoff forecasting in recent years. To reconstruct the time …
[HTML][HTML] Evaluating urban flood risk using hybrid method of TOPSIS and machine learning
With the growth of cities, urban flooding has increasingly become an issue for regional and
national governments. The destructive effects of floods are magnified in cities. Accurate …
national governments. The destructive effects of floods are magnified in cities. Accurate …
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 …
Flood hazard mapping methods: A review
Flood hazard mapping (FHM) has undergone significant development in terms of approach
and capacity of the result to meet the target of policymakers for accurate prediction and …
and capacity of the result to meet the target of policymakers for accurate prediction and …
[HTML][HTML] Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model
The computational limitations of complex numerical models have led to adoption of
statistical emulators across a variety of problems in science and engineering disciplines to …
statistical emulators across a variety of problems in science and engineering disciplines to …
The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review
Business analytics (BA) systems are considered significant investments for enterprises
because they have the potential to considerably improve firms' performance. With the value …
because they have the potential to considerably improve firms' performance. With the value …
A rainfall‐runoff model with LSTM‐based sequence‐to‐sequence learning
Rainfall‐runoff modeling is a complex nonlinear time series problem. While there is still
room for improvement, researchers have been developing physical and machine learning …
room for improvement, researchers have been developing physical and machine learning …
Deep learning methods for flood mapping: a review of existing applications and future research directions
Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …
the limitations of accurate, yet slow, numerical models, and to improve the results of …