A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods
Floods around the world are having devastating effects on human life and property. In this
paper, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS …
paper, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS …
Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment
Across the world, the flood magnitude is expected to increase as well as the damage caused
by their occurrence. In this case, the prediction of areas which are highly susceptible to …
by their occurrence. In this case, the prediction of areas which are highly susceptible to …
Flood risk assessment of global watersheds based on multiple machine learning models
X Li, D Yan, K Wang, B Weng, T Qin, S Liu - Water, 2019 - mdpi.com
Machine learning algorithms are becoming more and more popular in natural disaster
assessment. Although the technology has been tested in flood susceptibility analysis of …
assessment. Although the technology has been tested in flood susceptibility analysis of …
Flood risk assessment using hybrid artificial intelligence models integrated with multi-criteria decision analysis in Quang Nam Province, Vietnam
Flood risk assessment is an important task for disaster management activities in flood-prone
areas. Therefore, it is crucial to develop accurate flood risk assessment maps. In this study …
areas. Therefore, it is crucial to develop accurate flood risk assessment maps. In this study …
Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling?
This paper introduces a new deep-learning algorithm of deep belief network (DBN) based
on an extreme learning machine (ELM) that is structured by back propagation (BN) and …
on an extreme learning machine (ELM) that is structured by back propagation (BN) and …
Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis
Z Tang, H Zhang, S Yi, Y Xiao - Journal of Hydrology, 2018 - Elsevier
GIS-based multi-criteria decision analysis (MCDA) is increasingly used to support flood risk
assessment. However, conventional GIS-MCDA methods fail to adequately represent spatial …
assessment. However, conventional GIS-MCDA methods fail to adequately represent spatial …
Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt
BA El-Haddad, AM Youssef, HR Pourghasemi… - Natural Hazards, 2021 - Springer
Floods represent catastrophic environmental hazards that have a significant impact on the
environment and human life and their activities. Environmental and water management in …
environment and human life and their activities. Environmental and water management in …
A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods
SR Shikhteymour, M Borji, M Bagheri-Gavkosh… - Applied geography, 2023 - Elsevier
Hazardous flooding occurs across most climate zones. Owing to the lack of appropriate
infrastructures and applicable predictive methods, flooding in arid and semi-arid regions …
infrastructures and applicable predictive methods, flooding in arid and semi-arid regions …
An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines
Floods, as a catastrophic phenomenon, have a profound impact on ecosystems and human
life. Modeling flood susceptibility in watersheds and reducing the damages caused by …
life. Modeling flood susceptibility in watersheds and reducing the damages caused by …
Evaluation of machine learning, information theory and multi-criteria decision analysis methods for flood susceptibility mapping under varying spatial scale of analyses
The annual average economic losses due to various natural disasters are increasing
exponentially across the globe and have reached a mark of US $239.2 billion per year …
exponentially across the globe and have reached a mark of US $239.2 billion per year …