Spatiotemporal analysis and threshold modeling of rainfall-induced geological disasters in Anhui Province DOI Creative Commons
Bo Wang, Jie Liu,

Gaoping Liu

et al.

Frontiers in Earth Science, Journal Year: 2025, Volume and Issue: 13

Published: April 17, 2025

Rainfall-induced geological disasters are widespread in the Jianghuai region of China, endangering human lives and socioeconomic activities. Anhui Province, a hotspot for these disasters, warrants thorough analysis temporal spatial distribution their correlation with rainfall effective forecasting warning. This study divides Province into Dabie Mountains, southern other areas based on different background conditions, establishes threshold warning models each. We reconstructed collection disaster precipitation records data from 2008 to 2023. Using binary logistic regression, we analyzed between factors selected optimal attenuation parameters area, determined critical levels. Results show: (1) Landslides collapses main types, mostly occurring high altitude like concentrated rainy season June - July each year; (2) Rainfall is inducer, both single heavy processes sustained influencing occurrence, through combined effect; (3) Effective significantly correlated day previous 8 days rainfall. The coefficients regions 0.60, 0.66, 0.61, respectively. shows that setting fine tuned better than province wide threshold. With 79% forecast accuracy, it can provide scientific basis meteorological risk Province.

Language: Английский

Increasing irrigation-triggered landslide activity caused by intensive farming in deserts on three continents DOI Creative Commons
Zijing Liu, Haijun Qiu,

Yaru Zhu

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 134, P. 104242 - 104242

Published: Oct. 23, 2024

Language: Английский

Citations

26
Do post-failure landslides become stable? DOI
Haijun Qiu,

Yijun Li,

Yaru Zhu

et al.

CATENA, Journal Year: 2025, Volume and Issue: 249, P. 108699 - 108699

Published: Jan. 5, 2025

Citations

1
Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China Karakoram Highway DOI Creative Commons

Mohib Ullah,

Haijun Qiu,

Wenchao Huangfu

et al.

Land, Journal Year: 2025, Volume and Issue: 14(1), P. 172 - 172

Published: Jan. 15, 2025

The effectiveness of data-driven landslide susceptibility mapping relies on data integrity and advanced geospatial analysis; however, selecting the most suitable method identifying key regional factors remains a challenging task. To address this, this study assessed performance six machine learning models, including Convolutional Neural Networks (CNNs), Random Forest (RF), Categorical Boosting (CatBoost), their CNN-based hybrid models (CNN+RF CNN+CatBoost), Stacking Ensemble (SE) combining CNN, RF, CatBoost in along Karakoram Highway northern Pakistan. Twelve were examined, categorized into Topography/Geomorphology, Land Cover/Vegetation, Geology, Hydrology, Anthropogenic Influence. A detailed inventory 272 occurrences was compiled to train models. proposed stacking ensemble improve modeling, with achieving an AUC 0.91. Hybrid modeling enhances accuracy, CNN–RF boosting RF’s from 0.85 0.89 CNN–CatBoost increasing CatBoost’s 0.87 0.90. Chi-square (χ2) values (9.8–21.2) p-values (<0.005) confirm statistical significance across This identifies approximately 20.70% area as high very risk, SE model excelling detecting high-risk zones. Key influencing showed slight variations while multicollinearity among variables remained minimal. approach reduces uncertainties, prediction supports decision-makers implementing effective mitigation strategies.

Language: Английский

Citations

1
Landslide susceptibility assessment using information quantity and machine learning integrated models: a case study of Sichuan province, southwestern China DOI

Pengtao Zhao,

Ying Wang, Yi Xie

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Jan. 18, 2025

Language: Английский

Citations

1
A robust index was extracted to assess the prolonged changes in debris flow activity after a high magnitude earthquake DOI
Xiong Jiang, Huayong Chen, Chuan Tang

et al.

CATENA, Journal Year: 2025, Volume and Issue: 250, P. 108733 - 108733

Published: Jan. 18, 2025

Language: Английский

Citations

1
A cooperative search algorithm-based flood forecasting framework: application across diverse Chinese catchments DOI Creative Commons
Jinhai Yang, Lei Wen, Junliang Guo

et al.

Frontiers in Earth Science, Journal Year: 2025, Volume and Issue: 13

Published: Feb. 11, 2025

Flood forecasting is crucial for disaster mitigation, particularly in regions prone to flash floods. This study introduces a novel flood framework by coupling the Geomorphological Instantaneous Unit Hydrograph (GIUH) with Xinanjiang model and optimizing parameters using Cooperation Search Algorithm (CSA). Applied across six diverse Chinese catchments, significantly improved computational efficiency accuracy. Key findings demonstrate that: 1) CSA achieved high Nash-Sutcliffe Efficiency (NSE &gt;0.9) only 16 optimization trials on average, outperforming SCE-UA algorithms; 2) The performed exceptionally data-sparse regions, achieving NSE values &gt;0.9 even minimal datasets; 3) Enhanced runoff routing via GIUH enabled accurate simulation of extreme rainfall events. These results highlight framework’s potential operational management globally. Future research will expand validation datasets explore applications varied hydrological climatic conditions.

Language: Английский

Citations

1
Predicting the Dynamic of Debris Flow Based on Viscoplastic Theory and Support Vector Regression DOI Open Access
Xinhai Zhang,

Hanze Li,

Yazhou Fan

et al.

Water, Journal Year: 2025, Volume and Issue: 17(1), P. 120 - 120

Published: Jan. 4, 2025

The prediction of debris flows is essential for safeguarding infrastructure and minimizing the economic losses associated with hazards. Traditional empirical theoretical models, while providing foundational insights, often struggle to capture complex nonlinear behaviors inherent in flows. This study aims enhance flow by integrating modeling data-driven approaches. We model as a viscoplastic fluid, employing Herschel–Bulkley rheological describe its behavior. By combining kinematic wave lubrication theory, we develop comprehensive framework that encapsulates mechanical physics identifies key governing parameters. Numerical solutions this are utilized generate an extensive training dataset, which subsequently used train support vector regression (SVR) model. SVR targets slide depth velocity upon impact, using explanatory variables including yield stress, material density, source area length, slope length. demonstrates high predictive accuracy, achieving coefficients determination R2 0.956 0.911 at impact. Additionally, relative residuals σ primarily distributed within range −0.05 0.05 both These results indicate proposed hybrid not only incorporates fundamental physical mechanisms but also significantly enhances performance through optimization. underscores critical advantage merging models machine learning techniques, offering robust tool improved risk assessment, can inform development more effective early warning systems mitigation measures.

Language: Английский

Citations

0
Backward erosion piping mechanism in dike foundations with and without landside blanket layers: numerical simulation of size effects DOI Creative Commons

Qiuling Yao,

Xiping Yan,

Zebin Wu

et al.

Frontiers in Earth Science, Journal Year: 2025, Volume and Issue: 12

Published: Jan. 8, 2025

Introduction This study investigates the backward erosion piping mechanism and its dependency on model size through both experiments numerical simulations. The objective is to understand how different dimensions affect hydraulic gradients behavior in dike systems. Methods Numerical simulations were performed using finite element method (FEM), where foundation was modeled 3D seepage flow simulated under various gradients. Physical also conducted small-scale models verify results effects of size. Results Discussion show that dikes without blanket layers, increase steadily as channel develops, leading upstream failure. In contrast, with a layer exhibit stabilizing effect: gradient initially decreases before increasing, self-healing phenomenon halts further progression. reveals effect—indicated by gradients—diminishes larger becomes negligible beyond certain threshold. Additionally, interaction between width depth significantly influences progression piping. These findings offer valuable insights for designing more resilient systems improving flood protection strategies.

Language: Английский

Citations

0
Flash flood simulation based on distributed hydrological model in future scenarios DOI Creative Commons
Qi Liu, Nan Zhang, Lingling Wang

et al.

Frontiers in Earth Science, Journal Year: 2025, Volume and Issue: 12

Published: Jan. 14, 2025

Extreme rainfall events are frequent, particularly in economically underdeveloped hilly areas, where conventional hydrological models struggle to accurately simulate the formation of flash floods. Therefore, this study focuses on Daxi River Basin Guangdong Province. First, CMIP6 precipitation data is utilized analyze future variations interannual and monthly scales. Compared baseline period, annual increases under all three scenarios. Next, design storms with a return period greater than 2 years allocated into patterns. By combining accumulated soil moisture content, different distributed applied calculate corresponding flood discharges for events. The results indicate that: 1) Precipitation SSP5-8.5 scenario generally higher SSP1-2.6 SSP2-4.5 scenarios, showing mildest increase. 2) peak simulated by CREST model relatively low, at 235.4 m³/s, fewer covered, which significantly lower simulation accuracy CNFF model. 3) has low probability experiencing disasters exceeding 10-year from 2026 2070. above research will provide important references disaster prevention similar basins.

Language: Английский

Citations

0
Increasing landslide deformation and activity in a changing local environment: a case study of Zhouqu County in the Bailong River Basin DOI
Zijing Liu, Haijun Qiu, Ya Liu

et al.

Bulletin of Engineering Geology and the Environment, Journal Year: 2025, Volume and Issue: 84(2)

Published: Feb. 1, 2025

Language: Английский

Citations

0