Evaluating the effects of climate and land use change on the future flood susceptibility in the central region of Vietnam by integrating land change modeler, machine learning methods

Duy, Nguyen Huu; Kha, Dang Dinh; Nguyen, Quoc Hung; Bui, Quang‐Thanh; Petrişor, Alexandru-Ionuţ

doi:10.1080/10106049.2022.2071477

Cited by 14 publications

(6 citation statements)

References 155 publications

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“…The results of this study were unexpected, because the initial hypothesis was that inundation susceptibility in the study area had increased from 2010 to 2020 due to the change in precipitation in the context of climate change. This trend is typical of several regions in the world: Nguyen et al (2022a) pointed out that inundation susceptibility in Nhat Le-Kien Giang had increased due to the change in precipitation in the context of climate change. Previous study, Chen et al ( 2022) described an increase in inundation risk in Taiwan over the period 1979-2099.…”

Section: The External Validation Of Resultsmentioning

confidence: 72%

“…However, the continuous assessment of inundation susceptibility is an effective framework to better support decision makers to understand the causes of increased inundation. Nguyen et al (2022a) showed that constructing strategies that mitigate against inundation is more effective when susceptibility is continuously evaluated. Penning-Rowsell et al (2013) also indicated that continuous inundation assessment plays an important role in building appropriate strategies to reduce inundation damage.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Inundation Susceptibility in the Context of Climate Change, Based on Machine Learning and Remote Sensing: Case Study in Vinh Phuc Province of Vietnam

HUNG¹,

NGUYEN²,

Van³

2023

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Accurate prediction of streamflow plays an important role in water resource management and the continuous assessment of inundation susceptibility in the context of climate change plays a key role in facilitating the construction of appropriate strategies for sustainable development. So far, few studies into inundation susceptibility have explicitly incorporated the effects of climate change into their methodologies. This study aimed to assess inundation susceptibility for Vinh Phuc province in Vietnam, from 2000 to 2020, using machine learning and remote sensing. The algorithms used were support vector machine, catboost, and extratrees. A geo-spatial database of 206 inundation points and 11 conditioning factors (namely elevation, slope, curvature, aspect, distance to river, distance to road, NDVI, NDBI, rainfall, soil type, and TWI) from 2000 to 2020 was developed to be used as the input data. RMSE, MAE, AUC, and R² were used to assess the fit of the models. The results showed that all the proposed models were a good fit, with AUC values of 0.95 and over. In general, the total area marked as very low risk or low risk has increased, with the high risk and very high-risk areas having decreased over the period studied. This change was mainly concentrated in the city of Vinh Yen where there has been strong urban growth. The models proposed in this study are a promising toolkit to assess inundation susceptibility continuously and can support decision makers involved in sustainable development. Our results highlight the benefits and consequences of planned and unplanned development. Properly planned can reduce the flood risk, while unplanned development can increase the risk. Therefore, by applying the theoretical framework in this study, decision makers or planners can build the most appropriate strategies for flood control in the context of climate change. Our approach in this study represents a theoretical framework for future research not only on inundation management but also natural hazard management, in regions around the world.

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Section: The External Validation Of Resultsmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Assessment of Inundation Susceptibility in the Context of Climate Change, Based on Machine Learning and Remote Sensing: Case Study in Vinh Phuc Province of Vietnam

HUNG¹,

NGUYEN²,

Van³

2023

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“…Slope influences the speed of water flow and accumulation. Importantly, it is directly related to landslides, as they contribute significantly toward slope failure (Liao et al, 2022; Nguyen, Dang, et al, 2022). In the study area, the slope value ranged from 0 to 83°.…”

Section: Methods and Techniquesmentioning

confidence: 99%

“…Land use plays an important role in hydrological and geomorphological processes by directly or indirectly influencing permeability, sediment dynamics, and the process of infiltration, all of which can contribute to hazard occurrence (López et al, 2022; Nguyen, Dang, et al, 2022). In the study area, land use in 2021 comprised five main types: water, forest, barren land, crops, and built‐up area.…”

Section: Methods and Techniquesmentioning

confidence: 99%

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Nguyen

Dang

Bui

et al. 2023

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Natural hazards constitute a diverse category and are unevenly distributed in time and space. This hinders predictive efforts, leading to significant impacts on human life and economies. Multi‐hazard prediction is vital for any natural hazard risk management plan. The main objective of this study was the development of a multi‐hazard susceptibility mapping framework, by combining two natural hazards—flooding and landslides—in the North Central region of Vietnam. This was accomplished using support vector machines, random forest, and AdaBoost. The input data consisted of 4591 flood points, 1315 landslide points, and 13 conditioning factors, split into training (70%), and testing (30%) datasets. The accuracy of the models' predictions was evaluated using the statistical indices root mean square error, area under curve (AUC), mean absolute error, and coefficient of determination. All proposed models were good at predicting multi‐hazard susceptibility, with AUC values over 0.95. Among them, the AUC value for the support vector machine model was 0.98 and 0.99 for landslide and flood, respectively. For the random forest model, these values were 0.98 and 0.98, and for AdaBoost, they were 0.99 and 0.99. The multi‐hazard maps were built by combining the landslide and flood susceptibility maps. The results showed that approximately 60% of the study area was affected by landslides, 30% by flood, and 8% by both hazards. These results illustrate how North Central is one of the regions of Vietnam that is most severely affected by natural hazards, particularly flooding, and landslides. The proposed models adapt to evaluate multi‐hazard susceptibility at different scales, although expert intervention is also required, to optimize the algorithms. Multi‐hazard maps can provide a valuable point of reference for decision makers in sustainable land‐use planning and infrastructure development in regions faced with multiple hazards, and to prevent and reduce more effectively the frequency of floods and landslides and their damage to human life and property.

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“…We call for a prudent development plan that emphasizes risk avoidance during urbanization and construction. Unplanned development changing land cover and use, coupled with climate change, would have negative effects on flood risk, as also supported by recent work [21,83]. More land should be made available for mitigation purposes by reconverting farmland into lake, forests and natural floodplains, thus inducing its self-regulation of floods and restoration of erosion.…”

mentioning

confidence: 87%

A Novel Flood Risk Analysis Framework Based on Earth Observation Data to Retrieve Historical Inundations and Future Scenarios

Yao,

Yang,

Wang

et al. 2024

Remote Sensing

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Global warming is exacerbating flood hazards, making the robustness of flood risk management a critical issue. Without considering future scenarios, flood risk analysis built only on historical knowledge may not adequately address the coming challenges posed by climate change. A comprehensive risk analysis framework based on both historical inundations and future projections to tackle uncertainty is still lacking. In this view, a scenario-based, data-driven risk analysis framework that for the first time integrates recent historical floods and future risk trends is here presented, consisting of flood inundation-prone and high-risk zones. Considering the Poyang Lake Eco-Economic Zone (PLEEZ) in China as the study area, we reproduced historical inundation scenarios of major flood events by using Sentinel-1 imagery from 2015 to 2021, and used them to build the risk baseline model. The results show that 11.7% of the PLEEZ is currently exposed to the high-risk zone. In the SSP2-RCP4.5 scenario, the risk would gradually decrease after peaking around 2040 (with a 19.3% increase in high-risk areas), while under the traditional fossil fuel-dominated development pathway (SSP5-RCP8.5), the risk peak would occur with a higher intensity about a decade earlier. The attribution analysis results reveal that the intensification of heavy rainfall is the dominant driver of future risk increase and that the exploitation of unused land such as wetlands induces a significant increase in risk. Finally, a hierarchical panel of recommended management measures was developed. We hope that our risk analysis framework inspires newfound risk awareness and provides the basis for more effective flood risk management in river basins.

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Evaluating the effects of climate and land use change on the future flood susceptibility in the central region of Vietnam by integrating land change modeler, machine learning methods

Cited by 14 publications

References 155 publications

Assessment of Inundation Susceptibility in the Context of Climate Change, Based on Machine Learning and Remote Sensing: Case Study in Vinh Phuc Province of Vietnam

Assessment of Inundation Susceptibility in the Context of Climate Change, Based on Machine Learning and Remote Sensing: Case Study in Vinh Phuc Province of Vietnam

Multi‐hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

A Novel Flood Risk Analysis Framework Based on Earth Observation Data to Retrieve Historical Inundations and Future Scenarios

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