Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Ullah, Kashif; Wang, Yi; Fang, Zhice; Wang, Lizhe; Rahman, Mohd Tadza Abdul

doi:10.1016/j.gsf.2022.101425

Cited by 54 publications

(16 citation statements)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As natural hazards increase in number and intensity in the context of climate change, improving our understanding of these phenomena becomes increasingly urgent, in order to provide planners with the information they need to minimize future impacts (Bordbar et al, 2022; Ullah et al, 2022). The objective of this study was the development of a theoretical framework to understand and predict the occurrence of multiple hazards using machine learning and remote sensing in the North Central region of Vietnam.…”

Section: Discussionmentioning

confidence: 99%

Section: Significance Of the Findings And Comparison With Similar Stu...mentioning

confidence: 99%

See 1 more Smart Citation

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

Nguyen

Dang

Bui

et al. 2023

Transactions in GIS

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Significance Of the Findings And Comparison With Similar Stu...mentioning

confidence: 99%

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

Nguyen

Dang

Bui

et al. 2023

Transactions in GIS

View full text Add to dashboard Cite

show abstract

“…Although DNNs have many advantages, such as when processing unstructured data, there are also limitations, one of which being a huge need for computing power, both to maintain the neural networks and process the very large amount of data required. Finally, in order to be effective, deep learning needs a large amount of data (Kudashev et al, 2016; Ullah et al, 2022). Therefore, as we did not have access to such an amount of data, this study proposes an optimization framework to determine DNN model hyper‐parameters using optimization algorithms (Adam, SGD, ARO, TSO, SCSO, HBA and MPA).…”

Section: Discussionmentioning

confidence: 99%

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

Nguyen,

et al. 2023

Geological Journal

View full text Add to dashboard Cite

Landslides lead to widespread devastation and significant loss of life in mountainous regions around the world. Susceptibility assessments can provide critical data to help decision‐makers, for example, local authorities and other organizations, mitigating the landslide risk, although the accuracy of existing studies needs to be improved. This study aims to assess landslide susceptibility in the Thua Thien Hue province of Vietnam using deep neural networks (DNNs) and swarm‐based optimization algorithms, namely Adam, stochastic gradient descent (SGD), Artificial Rabbits Optimization (ARO), Tuna Swarm Optimization (TSO), Sand Cat Swarm Optimization (SCSO), Honey Badger Algorithm (HBA), Marine Predators Algorithm (MPA) and Particle Swarm Optimization (PSO). The locations of 945 landslides occurring between 2012 and 2022, along with 14 conditioning factors, were used as input data to build the DNN and DNN‐hybrid models. The performance of the proposed models was evaluated using the statistical indices receiver operating characteristic curve, area under the curve (AUC), root mean square error, mean absolute error (MAE), R2 and accuracy. All proposed models had a high accuracy of prediction. The DNN‐MPA model had the highest AUC value (0.95), followed by DNN‐HBA (0.95), DNN‐ARO (0.95), DNN‐Adam (0.95), DNN‐SGD (0.95), DNN‐TSO (0.93), DNN‐PSO (0.9) and finally DNN‐SCSO (0.83). High‐precision models have identified that the majority of the western region of Thua Thien Hue province is very highly susceptible to landslides. Models like the aforementioned ones can support decision‐makers in updating large‐scale sustainable land‐use strategies.

show abstract

“…Slope (Sl). Slope distribution significantly impacts the evaluation of floods, flash-floods and associated landslide occurrence [ 56 ]. Areas with low slopes are susceptible to flooding.…”

Section: Methodsmentioning

confidence: 99%

Geo-hazards assessment and land suitability estimation for spatial planning using multi-criteria analysis

Chelariu¹,

Minea²,

Iaţu³

2023

Heliyon

View full text Add to dashboard Cite

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Cited by 54 publications

References 96 publications

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

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

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

Geo-hazards assessment and land suitability estimation for spatial planning using multi-criteria analysis

Contact Info

Product

Resources

About