Multi-hazard could exacerbate in coastal Bangladesh in the context of climate change

Journal of Cleaner Production, Год журнала: 2024, Номер 457, С. 142289 - 142289

Опубликована: Апрель 23, 2024

Язык: Английский

---

DEWS: A QGIS tool pack for the automatic selection of reference rain gauges for landslide-triggering rainfall thresholds

Environmental Modelling & Software, Год журнала: 2023, Номер 162, С. 105657 - 105657

Опубликована: Фев. 20, 2023

Язык: Английский

---

A GIS-Based Approach for Shallow Landslides Risk Assessment in the Giampilieri and Briga Catchments Areas (Sicily, Italy)

GeoHazards, Год журнала: 2024, Номер 5(1), С. 209 - 232

Опубликована: Март 1, 2024

Shallow landslides pose a widely growing hazard and risk, globally particularly in Mediterranean areas. The implementation of adequate adaptation mitigation measures necessarily requires the development practical affordable methodologies technologies for assessing shallow its territorial impact. assessment landslide maps involves two different sequential steps: susceptibility runout analysis, respectively, aimed at identification initiation propagation This paper describes application Giampilieri Briga Villages area (Sicily, Italy) risk process basin scale with an innovative approach segment. analysis was conducted using specific GIS tools employing empirical–geometric scale. exposure vulnerability values elements were assigned qualitative semi-quantitative approach, respectively. results highlight effectiveness procedure producing consistent assessments valley areas where more important vulnerable exposed are located. study contributes to addressing public administration demand valuable user-friendly manage drive regional planning.

Язык: Английский

---

A knowledge-aware deep learning model for landslide susceptibility assessment in Hong Kong

The Science of The Total Environment, Год журнала: 2024, Номер 941, С. 173557 - 173557

Опубликована: Май 27, 2024

Язык: Английский

---

Precipitation-induced landslide risk escalation in China’s urbanization with high-resolution soil moisture and multi-source precipitation product

Journal of Hydrology, Год журнала: 2024, Номер 638, С. 131536 - 131536

Опубликована: Июнь 16, 2024

Язык: Английский

---

Contrasting Population Projections to Induce Divergent Estimates of Landslides Exposure Under Climate Change

Earth s Future, Год журнала: 2023, Номер 11(9)

Опубликована: Сен. 1, 2023

Abstract At first glance, assessing future landslide‐exposed population appears to be a straightforward task if landslide hazard estimates, climate change, and projections are available. However, the intersection of with socioeconomic elements may result in significant variation estimated exposure due considerable variations projections. This study aims investigate effects different sources data on evaluation China under four Shared Socioeconomic Pathways (SSPs) scenarios. We utilize multiple global models (GCMs) from Coupled Model Intercomparison Project Phase 6 six high‐resolution spatially explicit static dynamic sets drive available models. The results indicate an overall rise projections, increase potential impact area 0.4%–2.7% frequency 4.7%–20.1%, depending SSPs scenarios periods. likely changes exposed population, as modeled by incorporating hazard, yield divergent outcomes source. Thus, some depict exposure, while others show clear decrease. nationwide divergence ranged −64% +48%. These findings were mainly attributed differences lesser extent GCMs. present highlight need pay closer attention evolution at risk associated uncertainties.

Язык: Английский

---

Projection of the precipitation-induced landslide risk in China by 2050

Environmental Research Letters, Год журнала: 2024, Номер 19(2), С. 024048 - 024048

Опубликована: Янв. 30, 2024

Abstract China is highly susceptible to landslides and debris flow disasters as it a mountainous country with unique topography monsoon climate. In this study, an efficient statistical model used predict the landslide risk in under Representative Concentration Pathway 8.5 by 2050, precipitation data from global climate models (GCMs) driving field. Additionally, for first time, impact of future changes land use types on explored. By distinguishing between susceptibility risk, results indicate that will change near future. The occurrence high-frequency risks concentrated southwestern southeastern China, overall increase frequency. Although different GCMs differ projecting spatio-temporal distribution precipitation, there consensus increased China’s largely attributed extremely heavy precipitation. Moreover, alterations have risk. Huang-Huai-Hai Plain, Qinghai Tibet Plateau, Loess can mitigate risks. Conversely, other areas, such may landslides. This study aims facilitate informed decision-making preparedness measures protect lives assets response changing conditions.

Язык: Английский

---

Probabilistic life-cycle landslide assessment subjected to nonstationary rainfall based on alternating stochastic renewal process

Engineering Geology, Год журнала: 2024, Номер 338, С. 107543 - 107543

Опубликована: Май 17, 2024

The increasing intensity and frequency of rainfall due to climate change poses a significant risk landslides in the future. Therefore, methodology that accounts for nonstationary effects is needed accurately assess future landslides. This study presents novel framework probabilistic life-cycle landslide assessment under based on an alternating stochastic renewal process. process developed evaluate distribution maximum within slope. A slope fragility carried out by employing uncertainties associated with soil properties seepage-stability analysis Monte Carlo simulation. Finally, probability estimated convolving hazard curves total theorem. proposed applied two municipalities Japan, namely Hiroshima Kobe cities. results emphasize increases significantly when are considered, highlighting critical importance incorporating assessments.

Язык: Английский

---

Landslide Hazard Is Projected to Increase Across High Mountain Asia

Earth s Future, Год журнала: 2024, Номер 12(10)

Опубликована: Окт. 1, 2024

Abstract High Mountain Asia has long been known as a hotspot for landslide risk, and studies have suggested that hazard is likely to increase in this region over the coming decades. Extreme precipitation may become more frequent, with nonlinear response relative increasing global temperatures. However, these changes are geographically varied. This article maps probable hazard, shown by indicator (LHI) derived from downscaled temperature. In order capture of slopes extreme precipitation, simple machine‐learning model was trained on database landslides across develop regional LHI. applied statistically data 30 members Seamless System Prediction Earth Research large ensembles produce range possible outcomes under Shared Socioeconomic Pathways 2‐4.5 5‐8.5. The LHI reveals will most parts Asia. Absolute increases be highest already hazardous areas such Central Himalaya, but change greatest Tibetan Plateau. Even regions where declines year 2100, it prior mid‐century mark. seasonal cycle occurrence not greatly Although substantial uncertainty remains projections, overall direction seems reliable. These findings highlight importance continued analysis inform disaster risk reduction strategies stakeholders

Язык: Английский

---

Evaluation of linear, nonlinear and ensemble machine learning models for landslide susceptibility assessment in southwest China

Geocarto International, Год журнала: 2022, Номер 38(1)

Опубликована: Ноя. 24, 2022

Machine learning models are gradually replacing traditional techniques used for landslide susceptibility assessment. This study aims to comprehensively compare multiple models, including linear, nonlinear, and ensemble based on 5281 historical landslides in southwest China, the area most severely affected by disaster. Linear represented logistic regression (LR), nonlinear support vector machine (SVM), artificial neural network (ANN) classification 5.0 decision tree (C5.0 DT), random forest (RF) categorical boosting (Catboost) were selected. The correlation coefficient, variance inflation factor (VIF), relative important analysis select dominate conditioning factors. Using statistical indicators (e.g. Area Under Receiver Operating Characteristic curve (AUC) Kappa), cross-validation qualitative methods evaluate models' performance. findings are: (1) Regarding model predictive performance, best performance was demonstrated Catboost (AUC = 0.823 Kappa 0.593) RF 0.821 0.582), followed SVM 0.775 0.520), ANN 0.770 0.486) C5.0 DT 0.751 0.497), while linear LR 0.756 0.456) had a more limited model, which uses as its baseline classifier, has lot of potential studies into susceptibility. (2) robustness, three types nonspatial (CV) performed relatively similarly terms power, spatial (SPCV), (median AUC 0.714) achieved better results than models. It implies that when distribution is not homogeneous, may be robust. advisable consider various evaluation metrics from different perspectives integrate them with specialist geomorphological empirical knowledge determine model. (3) Gini index-based suggests road density dominant frequency area.

Язык: Английский

---