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Comparison of machine learning algorithms for flood susceptibility map**
Floods are one of the most destructive natural disasters, causing financial and human losses
every year. As a result, reliable Flood Susceptibility Map** (FSM) is required for effective …
every year. As a result, reliable Flood Susceptibility Map** (FSM) is required for effective …
[HTML][HTML] Intelligent flood forecasting and warning: A survey
Accurately predicting the magnitude and timing of floods is an extremely challenging
problem for watershed management, as it aims to provide early warning and save lives …
problem for watershed management, as it aims to provide early warning and save lives …
[HTML][HTML] Early flood monitoring and forecasting system using a hybrid machine learning-based approach
The occurrence of flash floods in urban catchments within the Mediterranean climate zone
has witnessed a substantial rise due to climate change, underscoring the urgent need for …
has witnessed a substantial rise due to climate change, underscoring the urgent need for …
Leveraging GIS-based AHP, remote sensing, and machine learning for susceptibility assessment of different flood types in peshawar, Pakistan
M Tayyab, M Hussain, J Zhang, S Ullah, Z Tong… - Journal of …, 2024 - Elsevier
Due to its diverse topography, Pakistan faces different types of floods each year, which
cause substantial physical, environmental, and socioeconomic damage. However, the …
cause substantial physical, environmental, and socioeconomic damage. However, the …
Scientometric analysis of flood forecasting for Asia region and discussion on machine learning methods
Flood forecast models have become better through research as they led to a lower risk of
flooding, policy ideas, less human death, and less destruction of property, so this study uses …
flooding, policy ideas, less human death, and less destruction of property, so this study uses …
Rainfall-driven machine learning models for accurate flood inundation map** in Karachi, Pakistan
Urban pluvial flooding (UPF) has emerged as a serious natural hazard, especially in recent
years. Previous research on UPF prediction has mainly focused on hydrological models …
years. Previous research on UPF prediction has mainly focused on hydrological models …
[HTML][HTML] Estimating flooding at river spree floodplain using HEC-RAS simulation
River renaturation can be an effective management method for restoring a floodplain's
natural capacity and minimizing the effects during high flow periods. A 1D-2D Hydrologic …
natural capacity and minimizing the effects during high flow periods. A 1D-2D Hydrologic …
Multivariate multi-step long short-term memory neural network for simultaneous stream-water variable prediction
Implementing multivariate predictive analysis to ascertain stream-water (SW) parameters
including dissolved oxygen, specific conductance, discharge, water level, temperature, pH …
including dissolved oxygen, specific conductance, discharge, water level, temperature, pH …
[HTML][HTML] Automated parameter estimation for geothermal reservoir modeling using machine learning
In geothermal developments, characterizing hydrothermal flow is essential for predicting
future production and designing effective development strategies. Numerical simulation …
future production and designing effective development strategies. Numerical simulation …
[HTML][HTML] Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction
T **e, L Chen, B Yi, S Li, Z Leng, X Gan, Z Mei - Water, 2024 - mdpi.com
Hydrological forecasting plays a crucial role in mitigating flood risks and managing water
resources. Data-driven hydrological models demonstrate exceptional fitting capabilities and …
resources. Data-driven hydrological models demonstrate exceptional fitting capabilities and …