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 …
[HTML][HTML] Data-driven approaches to built environment flood resilience: a scientometric and critical review
Environmental hazards such as floods significantly frustrate the functionality of built assets.
In addressing flood-induced challenges, data usage has become important. Despite existing …
In addressing flood-induced challenges, data usage has become important. Despite existing …
Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration
Recently, it has been demonstrated that the use of lagged discharge in long short-term
memory (LSTM) networks represents an effective method for streamflow prediction, so …
memory (LSTM) networks represents an effective method for streamflow prediction, so …
The quantitative assessment of impact of pumping capacity and LID on urban flood susceptibility based on machine learning
Y Wu, D She, J Xia, J Song, T Xiao, Y Zhou - Journal of Hydrology, 2023 - Elsevier
Drainage facilities such as drainage pumping systems and Low Impact Development (LID)
practices are effective measures to reduce urban flood risk. The quantitative identification of …
practices are effective measures to reduce urban flood risk. The quantitative identification of …
Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany
Identifying urban pluvial flood-prone areas is necessary but the application of two-
dimensional hydrodynamic models is limited to small areas. Data-driven models have been …
dimensional hydrodynamic models is limited to small areas. Data-driven models have been …
Matrix scenario-based urban flooding damage prediction via convolutional neural network
This study introduces a cutting-edge, high-resolution tool leveraging the predictive prowess
of convolutional neural networks to advance the field of hazard assessment in urban pluvial …
of convolutional neural networks to advance the field of hazard assessment in urban pluvial …
[HTML][HTML] Unsupervised active–transfer learning for automated landslide mapping
Z Wang, A Brenning - Computers & Geosciences, 2023 - Elsevier
Detailed landslide inventories are required for multiple purposes including disaster damage
assessments, susceptibility mapping for spatial planning, and disaster risk reduction. Active …
assessments, susceptibility mapping for spatial planning, and disaster risk reduction. Active …
Flood risk evaluation of the coastal city by the EWM-TOPSIS and machine learning hybrid method
Z Luo, J Tian, J Zeng, F Pilla - International Journal of Disaster Risk …, 2024 - Elsevier
The frequent occurrence of floods and waterlogging has significantly impacted coastal cities.
Effective mapping of flood risk can enhance the precision of disaster risk reduction …
Effective mapping of flood risk can enhance the precision of disaster risk reduction …
[HTML][HTML] Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments
Constrained by the sparsity of observational streamflow data, large-scale catchments face
pressing challenges in streamflow prediction and flood management amid climate change …
pressing challenges in streamflow prediction and flood management amid climate change …
Unlocking Online Insights: LSTM Exploration and Transfer Learning Prospects
Abstract Machine learning algorithms can improve the time series data analysis as
compared to the traditional methods such as moving averages or auto-regressive …
compared to the traditional methods such as moving averages or auto-regressive …