[HTML][HTML] Flood susceptibility mapping: Integrating machine learning and GIS for enhanced risk assessment
Flooding presents a formidable challenge in the United States, endangering lives and
causing substantial economic damage, averaging around $5 billion annually. Addressing …
causing substantial economic damage, averaging around $5 billion annually. Addressing …
[HTML][HTML] Flood susceptibility mapping to improve models of species distributions
As significant ecosystem disturbances flooding events are expected to increase in both
frequency and severity due to climate change, underscoring the critical need to understand …
frequency and severity due to climate change, underscoring the critical need to understand …
Comparison of hydrological modeling, artificial neural networks and multi-criteria decision making approaches for determining flood source areas
Flood risk management is a critical task which necessitates flood forecasting and identifying
flood source areas for implementation of prevention measures. Hydrological models, multi …
flood source areas for implementation of prevention measures. Hydrological models, multi …
[HTML][HTML] Integrated evaluation and attribution of urban flood risk mitigation capacity: A case of Zhengzhou, China
K Dai, S Shen, C Cheng, Y Song - Journal of Hydrology: Regional Studies, 2023 - Elsevier
Abstract Study region The Zhengzhou City in China Study focus Urban flood risk mitigation
(UFRM) refers to the runoff retention capacity of land surface in urban regions. Previous …
(UFRM) refers to the runoff retention capacity of land surface in urban regions. Previous …
Novel optimized deep learning algorithms and explainable artificial intelligence for storm surge susceptibility modeling and management in a flood-prone island
Sagar Island, located in the Indian Sundarbans Delta, is extremely vulnerable to storm surge
flooding. Therefore, there is a need for a precise model to assess its susceptibility to storm …
flooding. Therefore, there is a need for a precise model to assess its susceptibility to storm …
Assessing critical flood-prone districts and optimal shelter zones in the Brahmaputra Valley: Strategies for effective flood risk management
Frequent flooding has become a persistent issue in floodplain regions, causing significant
disasters during each rainy season due to insufficient disaster management planning. This …
disasters during each rainy season due to insufficient disaster management planning. This …
Flood susceptibility mapping using ANNs: a case study in model generalization and accuracy from Ontario, Canada
R Khalid, UT Khan - Geocarto International, 2024 - Taylor & Francis
Accurate flood susceptibility mapping (FSM) is critical for mitigating the environmental, social
and economic consequences of floods. The influence of model generalizability onto new …
and economic consequences of floods. The influence of model generalizability onto new …
Enhancing real-time flood forecasting and warning system by integrating ensemble techniques and hydrologic model simulations
Flooding poses a severe threat to communities and infrastructure worldwide, which requires
advanced flood forecasting warning systems. In this research paper, a real-time flood …
advanced flood forecasting warning systems. In this research paper, a real-time flood …
Assessment of Flash Flood Risk Using Random Forest Regression Model Integrated with Binary Statistics
X Fang, M Liao, L Yang, X Jiang, J Jin… - IGARSS 2024-2024 …, 2024 - ieeexplore.ieee.org
Flash floods are one of natural disasters that result in significant economic losses and
human casualties. Previous studies on flash flood assessment generally applied binary or …
human casualties. Previous studies on flash flood assessment generally applied binary or …
Predicting flood-prone areas using generalized linear and maximum entropy machine learning models
A Hanifinia, H Abghari - Journal of Natural Environmental Hazards, 2024 - jneh.usb.ac.ir
The purpose of this study is to identify the effective factors, prepare flood risk prediction
maps using machine learning models, and finally evaluate the efficiency of these models in …
maps using machine learning models, and finally evaluate the efficiency of these models in …