| dc.description.abstract | Background
Flash floods pose a significant and escalating threat to life, infrastructure, and property in
the mountainous regions of Vietnam. Over the past two decades, Vietnam has experienced
rapid socio-economic development, making it one of the fastest-growing countries in the
region. However, this progress has been accompanied by urbanization, land cover
conversion, and reductions in natural forest coverage, exacerbating the risk of flash floods.
This study focuses on two objectives: (1) Applying the Machine learning (ML) model to
predict the flash flood susceptibility and (2) Evaluating the quantitative influence of human
activities on flash flood susceptibility in recent two decades in mountainous of Vietnam.
Methodology
To solve the above two objectives, the study has examined 452 historical flash flood
points and analyzed 15 independent factors encompassing elevation, slope, aspect, curvature,
topographic wetness index (TWI), stream power index (SPI), flow accumulation, river
density, distance to the river, NDVI, NDBI, land use/ land cover (LULC), rainfall, soil type,
geology. From these data, the study has employed various machine learning algorithms to
predict the probability of flash flood, including Logistic Regression (LR), K-Nearest
Neighbors (KNN), Gaussian Naïve Bayes (GNB), Multi-layer Perceptron (MLP), Support
Vector Machines (SVM), Random Forests (RF), and XGBoost (XGB). The algorithm that
exhibits the highest performance was used to build the flash flood susceptibility maps for the
period of 2001-2010 and 2013-2022. To assess the anthropogenic impact, we conducted a
detailed analysis of changes in land use patterns and employed indices such as the
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Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index
(NDBI). These factors played a significant role in shaping the differences in flash flood
probability between the two time periods.
Results
The results demonstrated that RF, XGB and KNN models showcasing superior
performance, boasting accuracy rates of 0.949, 0.941, and 0.919, along with impressive
AUC-ROC values of 0.990, 0.989, and 0.974, respectively.
The study also showed that human socio-economic development activities in the last two
decades have increased alarmingly in the probability of flash floods by 7.69% and 4.01% in
areas classified as high and very high susceptibility, respectively.
Conclusion
This pioneering endeavor introduces a novel and comprehensive suite of associative
models, adding significant value to existing flood prediction methodologies. Besides, these
findings provide tangible and robust evidence that policymakers can utilize to evaluate the
implications of ongoing socio-economic growth. Furthermore, they serve as a critical
foundation for formulating sustainable development plans that prioritize mitigating the
future escalating risk of flash floods. | en_US |