Overview Of Slope Stability, Earthquakes, Flash Floods And Expansive Soil Hazards In The Republic Of Yemen

Zaid, Hussein Ahmed Hasan; Jamaluddin, Tajul Anuar; Arifin, Mohd Hariri

doi:10.7186/bgsm71202106

Cited by 1 publication

(1 citation statement)

References 26 publications

(35 reference statements)

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“…The escalation of flooding may be due to the synergistic effects of climate change and growing urbanization. Increases in flood disasters can be attributed to regional land use change, population growth, inadequate environmental regulation, and construction of residential buildings in flood-prone locations [5]. Floods in the study areas caused severe crops and economic losses of USD 1638 million, including over 70 fatalities, 25,000 displaced people, and over 2800 buildings destroyed; 340 houses were demolished in Tarim, Al-Kotn, and Shibam [6].…”

Section: Introductionmentioning

confidence: 97%

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

Al-Aizari,

Alzahrani,

AlThuwaynee

et al. 2024

Remote Sensing

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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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Section: Introductionmentioning

confidence: 97%