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 …
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 …
Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea
Landslides are a geological hazard that can pose a serious threat to human health and the
environment of highlands or mountain slopes. Landslide susceptibility mapping is an …
environment of highlands or mountain slopes. Landslide susceptibility mapping is an …
Flash-flood hazard using deep learning based on H2O R package and fuzzy-multicriteria decision-making analysis
The present study was done in order to simulate the flash-flood susceptibility across the
Suha river basin in Romania using a number of 3 hybrid models and fuzzy-AHP multicriteria …
Suha river basin in Romania using a number of 3 hybrid models and fuzzy-AHP multicriteria …
Artificial neural network approaches for disaster management: A literature review
Disaster management (DM) is one of the leading fields that deal with the humanitarian
aspects of emergencies. The field has attracted researchers because of its ever-increasing …
aspects of emergencies. The field has attracted researchers because of its ever-increasing …
[HTML][HTML] A review of recent advances in urban flood research
Due to a changing climate and increased urbanization, an escalation of urban flooding
occurrences and its aftereffects are ever more dire. Notably, the frequency of extreme storms …
occurrences and its aftereffects are ever more dire. Notably, the frequency of extreme storms …
Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards
Ö Ekmekcioğlu, K Koc - Catena, 2022 - Elsevier
This research proposes a novel step-wise binary prediction framework for the susceptibility
assessment of geo-hydrological hazards specific to floods and landslides. The framework of …
assessment of geo-hydrological hazards specific to floods and landslides. The framework of …
[HTML][HTML] Convolutional neural network and long short-term memory algorithms for groundwater potential mapping in Anseong, South Korea
Study region The study area was the Anseong-si area that located in the southernmost part
of Gyeonggi-do Province at 127° 19′ E, 36° 82′ N. Anseong has a transitional climate …
of Gyeonggi-do Province at 127° 19′ E, 36° 82′ N. Anseong has a transitional climate …
Exploring the additional value of class imbalance distributions on interpretable flash flood susceptibility prediction in the Black Warrior River basin, Alabama, United …
This study proposes a novel flash flood susceptibility prediction framework with a particular
emphasis on the extent of imbalance between the number of flooding and non-flooding …
emphasis on the extent of imbalance between the number of flooding and non-flooding …
Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) …
A Mohammadifar, H Gholami, S Golzari - Journal of Environmental …, 2023 - Elsevier
Flood risk assessment is a key step in flood management and mitigation, and flood risk
maps provide a quantitative measure of flood risk. Therefore, integration of deep learning …
maps provide a quantitative measure of flood risk. Therefore, integration of deep learning …