Cited by Surface water and groundwater suitability assessment for drinking and irrigation in a coal-mining area of southwestern China: EWQI, IWQI, and sensitivity analysis

Explainable machine learning-based land subsidence susceptibility mapping: from feature importance to individual model contributions in ensembled system DOI

Bofan Yu,

Huaixue Xing, Weiya Ge

et al.

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

Published: May 20, 2025

Language: Английский

Citations

Hydrochemical evolution and assessment of groundwater quality in an intensively agricultural area: case study of Chengdu plain, Southwestern China DOI

Rongwen Yao, Jiaqian Xu, Ye Zhou

et al.

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(8)

Published: April 1, 2025

Language: Английский

Citations

Mapping landslide susceptibility in the Eastern Mediterranean mountainous region: a machine learning perspective DOI

Hazem Ghassan Abdo, Sahar Mohammed Richi, Pankaj Prasad

et al.

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(9)

Published: April 30, 2025

Language: Английский

Citations

Rainfall-Induced Geological Hazard Susceptibility Assessment in the Henan Section of the Yellow River Basin: Multi-Model Approaches Supporting Disaster Mitigation and Sustainable Development DOI

Y. Zhang,

Hui Ci, Hui Yang

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(10), P. 4348 - 4348

Published: May 11, 2025

The Henan section of the Yellow River Basin (3.62 × 104 km2, 21.7% Province), a vital agro-industrial and politico-economic hub, faces frequent rainfall-induced geohazards. 2021 “7·20” Zhengzhou disaster, causing 398 fatalities CNY 120.06 billion loss, highlights its vulnerability to extreme weather. While machine learning (ML) aids geohazard assessment, geological hazard susceptibility assessment (RGHSA) remains understudied, with single ML models lacking interpretability precision for complex disaster data. This study presents hybrid framework (IVM-ML) that integrates Information Value Model (IVM) ML. uses historical data 11 factors (e.g., rainfall erosivity, relief amplitude) calculate information values construct prediction model these quantitative results. By combining IVM’s spatial analysis ML’s predictive power, it addresses limitations conventional models. ROC curve validation shows Random Forest (RF) in IVM-ML achieves highest accuracy (AUC = 0.9599), outperforming standalone IVM 0.7624). All exhibit AUC exceeding 0.75, demonstrating strong capability capturing rainfall–hazard relationships reliable performance. Findings support RGHSA practices mid-Yellow urban cluster, offering insights sustainable risk management, land-use planning, climate resilience. Bridging geoscience data-driven methods, this advances global sustainability goals reduction environmental security vulnerable riverine regions.

Language: Английский

Citations

Surface water and groundwater suitability assessment for drinking and irrigation in a coal-mining area of southwestern China: EWQI, IWQI, and sensitivity analysis DOI

Shiming Yang, Denghui Wei, Zhan Xie

et al.

Journal of Environmental Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: June 1, 2025

Language: Английский

Citations