A review of hydrodynamic and machine learning approaches for flood inundation modeling
Machine learning (also called data-driven) methods have become popular in modeling flood
inundations across river basins. Among data-driven methods, traditional machine learning …
inundations across river basins. Among data-driven methods, traditional machine learning …
An overview of flood-induced transport disruptions on urban streets and roads in Chinese megacities: Lessons and future agendas
Urban road transport disruptions caused by urban floods have become severe in the
Chinese megacities due to climate change and urbanisation. Urban road planning, design …
Chinese megacities due to climate change and urbanisation. Urban road planning, design …
Modelling, map** and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote …
The present study is designed to monitor the spatio-temporal changes in forest cover using
Remote Sensing (RS) and Geographic Information system (GIS) techniques from 1990 to …
Remote Sensing (RS) and Geographic Information system (GIS) techniques from 1990 to …
Flash flood susceptibility assessment and zonation by integrating analytic hierarchy process and frequency ratio model with diverse spatial data
Flash floods are the most dangerous kinds of floods because they combine the destructive
power of a flood with incredible speed. They occur when heavy rainfall exceeds the ability of …
power of a flood with incredible speed. They occur when heavy rainfall exceeds the ability of …
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 …
A Google Earth Engine approach for wildfire susceptibility prediction fusion with remote sensing data of different spatial resolutions
The effects of the spatial resolution of remote sensing (RS) data on wildfire susceptibility
prediction are not fully understood. In this study, we evaluate the effects of coarse (Landsat 8 …
prediction are not fully understood. In this study, we evaluate the effects of coarse (Landsat 8 …
Flood susceptibility map** using machine learning boosting algorithms techniques in Idukki district of Kerala India
Kerala experiences a high rate of annual rainfall and flooding resulting in a frequent natural
disaster. The objective of this study is to develop flood susceptibility maps for the Idukki …
disaster. The objective of this study is to develop flood susceptibility maps for the Idukki …
A novel flood risk map** approach with machine learning considering geomorphic and socio-economic vulnerability dimensions
Quantifying flood hazards by employing hydraulic/hydrodynamic models for flood risk
map** is a widely implemented non-structural flood management strategy. However, the …
map** is a widely implemented non-structural flood management strategy. However, the …
Flood susceptible prediction through the use of geospatial variables and machine learning methods
Floods are one of the most perilous natural calamities that cause property destruction and
endanger human life. The spatial patterns of flood susceptibility were assessed in this study …
endanger human life. The spatial patterns of flood susceptibility were assessed in this study …
[HTML][HTML] Satellite image processing by Python and R using Landsat 9 OLI/TIRS and SRTM DEM data on Côte d'Ivoire, West Africa
In this paper, we propose an advanced scripting approach using Python and R for satellite
image processing and modelling terrain in Côte d'Ivoire, West Africa. Data include Landsat 9 …
image processing and modelling terrain in Côte d'Ivoire, West Africa. Data include Landsat 9 …