Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based …
Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we
propose a new flood susceptibility mapping technique. We employ new ensemble models …
propose a new flood susceptibility mapping technique. We employ new ensemble models …
Novel ensemble machine learning models in flood susceptibility mapping
The research aims to propose the new ensemble models by combining the machine
learning techniques, such as rotation forest (RF), nearest shrunken centroids (NSC), k …
learning techniques, such as rotation forest (RF), nearest shrunken centroids (NSC), k …
A novel hybrid artificial intelligence approach for flood susceptibility assessment
A new artificial intelligence (AI) model, called Bagging-LMT-a combination of bagging
ensemble and Logistic Model Tree (LMT)-is introduced for mapping flood susceptibility. A …
ensemble and Logistic Model Tree (LMT)-is introduced for mapping flood susceptibility. A …
Comparison of machine learning algorithms for flood susceptibility mapping
Floods are one of the most destructive natural disasters, causing financial and human losses
every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective …
every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective …
[HTML][HTML] Flood susceptibility mapping using multi-temporal SAR imagery and novel integration of nature-inspired algorithms into support vector regression
Flood has long been known as one of the most catastrophic natural hazards worldwide.
Mapping flood-prone areas is an important part of flood disaster management. In this study …
Mapping flood-prone areas is an important part of flood disaster management. In this study …
Modeling spatial flood using novel ensemble artificial intelligence approaches in northern Iran
The uncertainty of flash flood makes them highly difficult to predict through conventional
models. The physical hydrologic models of flash flood prediction of any large area is very …
models. The physical hydrologic models of flash flood prediction of any large area is very …
Computational machine learning approach for flood susceptibility assessment integrated with remote sensing and GIS techniques from Jeddah, Saudi Arabia
Floods, one of the most common natural hazards globally, are challenging to anticipate and
estimate accurately. This study aims to demonstrate the predictive ability of four ensemble …
estimate accurately. This study aims to demonstrate the predictive ability of four ensemble …
Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks
M Ahmadlou, A Al‐Fugara… - Journal of Flood Risk …, 2021 - Wiley Online Library
Floods are one of the most destructive natural disasters causing financial damages and
casualties every year worldwide. Recently, the combination of data‐driven techniques with …
casualties every year worldwide. Recently, the combination of data‐driven techniques with …
Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping
In this research, eight individual machine learning and statistical models are implemented
and compared, and based on their results, seven ensemble models for flood susceptibility …
and compared, and based on their results, seven ensemble models for flood susceptibility …
Flood susceptibility mapping using remote sensing and integration of decision table classifier and metaheuristic algorithms
S Askar, S Zeraat Peyma, MM Yousef, NA Prodanova… - Water, 2022 - mdpi.com
Flooding is one of the most prevalent types of natural catastrophes, and it can cause
extensive damage to infrastructure and the natural environment. The primary method of …
extensive damage to infrastructure and the natural environment. The primary method of …