English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This study selected ten condition factors from the perspectives of topography, geological structure, hydrology and human for mining subsidence mapping by two types of models (knowledge-driven model and data-driven model) by taking Jining City, China as an example. The knowledge-driven model is achieved by analytic hierarchy process (AHP), and the data-driven model is achieved by frequency ratio (FR), combined with fuzzy comprehensive evaluation (FCE) and entropy evaluation method (EWM). Based on these models, the areas under receiver operating characteristic curve are 0.777, 0.949, and 0.938, which means FR-FCE obtained the highest accuracy. The differences reflected by the three methods are concentrated in Liangshan County and central to northern areas of Weishan County. The best model was proposed for the protection measures and land use management. Finally, the factors system and methods used in this study can be widely applied to the mining subsidence susceptibility evaluation in plain areas.

Evaluation of Jining mining subsidence susceptibility based on three multiple-criteria decision analysis methods