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

Environmental Earth Sciences, Год журнала: 2025, Номер 84(8)

Опубликована: Апрель 1, 2025

Язык: Английский

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

Environmental Earth Sciences, Год журнала: 2025, Номер 84(9)

Опубликована: Апрель 30, 2025

Язык: Английский

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

Sustainability, Год журнала: 2025, Номер 17(10), С. 4348 - 4348

Опубликована: Май 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.

Язык: Английский

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

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Май 20, 2025

Язык: Английский

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

Journal of Environmental Sciences, Год журнала: 2025, Номер unknown

Опубликована: Июнь 1, 2025

Язык: Английский

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