A brief review of random forests for water scientists and practitioners and their recent history in water resources
Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …
to gain prominence in water resources applications. However, existing applications are …
A review on applications of urban flood models in flood mitigation strategies
W Qi, C Ma, H Xu, Z Chen, K Zhao, H Han - Natural Hazards, 2021 - Springer
As a result of climate change, urban areas are increasingly vulnerable to flooding, which can
cause devastating effects, both in terms of loss of life and property. Therefore, an accurate …
cause devastating effects, both in terms of loss of life and property. Therefore, an accurate …
Coupling a hybrid CNN-LSTM deep learning model with a boundary corrected maximal overlap discrete wavelet transform for multiscale lake water level forecasting
Developing accurate lake water level (WL) forecasting models is important for flood control,
shoreline maintenance and sustainable water resources planning and management. In this …
shoreline maintenance and sustainable water resources planning and management. In this …
[HTML][HTML] DEM resolution effects on machine learning performance for flood probability mapping
Floods are among the devastating natural disasters that occurred very frequently in arid
regions during the last decades. Accurate assessment of the flood susceptibility mapping is …
regions during the last decades. Accurate assessment of the flood susceptibility mapping is …
An integrated statistical-machine learning approach for runoff prediction
Nowadays, great attention has been attributed to the study of runoff and its fluctuation over
space and time. There is a crucial need for a good soil and water management system to …
space and time. There is a crucial need for a good soil and water management system to …
[HTML][HTML] HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming
industry applications and generating new and improved capabilities for scientific discovery …
industry applications and generating new and improved capabilities for scientific discovery …
Depth prediction of urban flood under different rainfall return periods based on deep learning and data warehouse
Z Wu, Y Zhou, H Wang, Z Jiang - Science of The Total Environment, 2020 - Elsevier
With the global climate change and the rapid urbanization process, there is an increase in
the risk of urban floods. Therefore, undertaking risk studies of urban floods, especially the …
the risk of urban floods. Therefore, undertaking risk studies of urban floods, especially the …
Training machine learning surrogate models from a high‐fidelity physics‐based model: Application for real‐time street‐scale flood prediction in an urban coastal …
Mitigating the adverse impacts caused by increasing flood risks in urban coastal
communities requires effective flood prediction for prompt action. Typically, physics‐based 1 …
communities requires effective flood prediction for prompt action. Typically, physics‐based 1 …
Flood risk assessment and increased resilience for coastal urban watersheds under the combined impact of storm tide and heavy rainfall
Low-lying coastal cities are vulnerable to flooding under the combined impact of storm tide
and heavy rainfall. While storm tide or heavy rainfall alone is able to directly cause …
and heavy rainfall. While storm tide or heavy rainfall alone is able to directly cause …
[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 …