A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps

Bozzolan, Elisa; Holcombe, Elizabeth; Pianosi, Francesca; Marchesini, Ivan; Alvioli, Massimiliano; Wagener, Thorsten

doi:10.1016/j.scitotenv.2022.159412

Cited by 20 publications

(5 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Thua Thien Hue province, the distance to river value ranged from 0 to 13,818 m. Heavy rains over a short period lead to saturated ground, causing landslides. In addition, precipitation also creates grooves in the surface; this change of the slope morphology can also lead to landslides (Bozzolan et al, 2023; Gnyawali et al, 2023).…”

Section: Methodsmentioning

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

Section: Methodsmentioning

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

show abstract

“…Land Use: Land use patterns directly reflect human activity intensity on slopes [36,37] . Field surveys show that most landslides occur in forested and agricultural areas, comprising 87.3% of all landslides(Figure 4i).…”

Section: Rdls (Relativementioning

confidence: 99%

Optimizing the Application of Machine Learning Models in Predicting Landslide Susceptibility Using the Information Value Model in Junlian County of Sichuan Basin

Qian,

Ou,

et al. 2024

Preprint

View full text Add to dashboard Cite

Constructing accurate landslide susceptibility models is crucial for effective landslide prevention.This study explores methods to enhance the accuracy of landslide susceptibility models.This paper focuses on Junlian County, Sichuan, as the study area.Initially, a landslide inventory was created using field surveys and historical records.Eight environmental factors were identified via correlation analysis: elevation, slope, aspect, stratigraphic lithology, and distances from faults, roads, rivers, and areas of land use.Subsequently, we constructed an information value model.For training the IV-RF model, non-landslide points in areas of low susceptibility were randomly selected at various ratios (1:1, 1:2, 1:3, 1:4, 1:5).The optimal ratio was used to develop coupled models (IV-RF, IV-LR, IV-SVM, IV-BP), comparing their accuracy and discussing the impact of environmental factors on landslide susceptibility.Results indicate that: (1) the highest prediction accuracy was achieved with a non-landslide ratio of 1:1; (2) the IV-RF model achieved the highest AUC of 0.994; and (3) the most significant factors influencing landslide distribution were stratigraphic lithology and river proximity, followed by elevation and fault distance.

show abstract

“…Landslide is a widespread natural disaster. It has the characteristics of regionality, multi-causality, strong destructiveness, and irremovability (Bozzolan et al, 2023). According to the GFLD (Global Fatal Landslide Database), 55,997 people have died due to landslides worldwide in the past 50 years (Dowling and Santi, 2014;Lin et al, 2017;Wu et al, 2023a).…”

Section: Introductionmentioning

confidence: 99%

Spatial distributions and multi-factor driving mechanism of landslide in southern Liaodong Peninsula

Li,

Zheng,

Yan

et al. 2024

Front. Ecol. Evol.

View full text Add to dashboard Cite

Coastal areas are characterized by high levels of urbanization and also the most active and highly sensitive to geological activities. Due to the impact of global climate change, landslides occur frequently in coastal areas, which have significantly impacted the sustainable socio-economic development of the region. Therefore, studying the spatial distribution of landslides and the factors that drive them holds immense practical significance for the geohazards prevention and mitigation work and optimization of ecological security patterns in coastal areas. This study takes the southern Liaodong Peninsula as the study area and analyzes the spatial distribution characteristics and influence mechanism of landslides. We first applied the Getis-Ord Gi*, kernel density analysis techniques, and Ripley’s K function to explore the spatial distribution characteristics of landslides in the study area at different scales. Then, we employed the information value method to test for the linkage relationships between the driving factors and landslides. Finally, we utilized the geographical detector to reveal the potential impacts and interaction of the driving factors on landslides. The results showed that: (1) landslides in the Southern Liaodong Peninsula exhibit strong spatial clustering characteristics, and have a spatial scale effect; (2) the high susceptibility areas are mainly concentrated in the southern hilly regions and the mountainous regions in the northern parts of the study area; (3) The information value method reveals that there is an optimal combination of factors driving landslides. (4) The factor detector analysis reveals that the primary driver of landslide spatial distribution is the geomorphological types (q-value of 39.10%). (5) The interaction detector indicates that the interplay of all driving factor pairs exhibited an enhanced effect, which is not a simple superposition effect but bivariate and non-linear. The collective influence of natural factors and human activities holds a more substantial influence over landslide development and distribution. The research offers guidance for the execution of landslide prevention initiatives and the establishment of preservation policies for the ecological environment in the southern Liaodong Peninsula region.

show abstract

A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps

Cited by 20 publications

References 64 publications

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

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

Optimizing the Application of Machine Learning Models in Predicting Landslide Susceptibility Using the Information Value Model in Junlian County of Sichuan Basin

Spatial distributions and multi-factor driving mechanism of landslide in southern Liaodong Peninsula

Contact Info

Product

Resources

About