Uncertainty Reduction in Flood Susceptibility Mapping Using Random Forest and eXtreme Gradient Boosting Algorithms in Two Tropical Desert Cities, Shibam and Marib, Yemen

Flooding is a natural disaster that coexists with human beings and causes severe loss of life and property worldwide. Although numerous studies for flood susceptibility modelling have been introduced, a notable gap has been the overlooked or reduced consideration of the uncertainty in the accuracy o...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 16; no. 2; p. 336
Main Authors: Al-Aizari, Ali R., Alzahrani, Hassan, AlThuwaynee, Omar F., Al-Masnay, Yousef A., Ullah, Kashif, Park, Hyuck-Jin, Al-Areeq, Nabil M., Rahman, Mahfuzur, Hazaea, Bashar Y., Liu, Xingpeng
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.01.2024
Subjects:
Accuracy
Algorithms
Analysis
Case studies
Cities
Conditioning
Cyclones
Datasets
desert city
Disaster risk
Displaced persons
extreme gradient boosting
Flood forecasting
Flood mapping
Flood predictions
flood susceptibility
Flooding
Floods
Historic floods
humans
Machine learning
Mapping
Methods
model uncertainty
Modelling
Natural disasters
Neural networks
overfitting
prediction
random forest
Random variables
Remote sensing
Susceptibility
topography
Uncertainty
Yemen
Yemen
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary:Flooding is a natural disaster that coexists with human beings and causes severe loss of life and property worldwide. Although numerous studies for flood susceptibility modelling have been introduced, a notable gap has been the overlooked or reduced consideration of the uncertainty in the accuracy of the produced maps. Challenges such as limited data, uncertainty due to confidence bounds, and the overfitting problem are critical areas for improving accurate models. We focus on the uncertainty in susceptibility mapping, mainly when there is a significant variation in the predictive relevance of the predictor factors. It is also noted that the receiver operating characteristic (ROC) curve may not accurately depict the sensitivity of the resulting susceptibility map to overfitting. Therefore, reducing the overfitting problem was targeted to increase accuracy and improve processing time in flood prediction. This study created a spatial repository to test the models, containing data from historical flooding and twelve topographic and geo-environmental flood conditioning variables. Then, we applied random forest (RF) and extreme gradient boosting (XGB) algorithms to map flood susceptibility, incorporating a variable drop-off in the empirical loop function. The results showed that the drop-off loop function was a crucial method to resolve the model uncertainty associated with the conditioning factors of the susceptibility modelling and methods. The results showed that approximately 8.42% to 9.89% of Marib City and 9.93% to 15.69% of Shibam City areas were highly vulnerable to floods. Furthermore, this study significantly contributes to worldwide endeavors focused on reducing the hazards linked to natural disasters. The approaches used in this study can offer valuable insights and strategies for reducing natural disaster risks, particularly in Yemen.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs16020336