Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220807524> ?p ?o ?g. }
- W4220807524 abstract "A 3D tailings dam visualization early warning system was developed based on GIS (geographic information system) combining ARIMA (autoregressive integrated moving average model) and 3S (RS, GIS, GPS) technology for prediction of phreatic line changes and tailing dam deformation. It was applied for monitoring and early warning for the gold–copper tailing dam in Zijinshan Dadongbei tailing pond. The system consists of equipment management, data management, prediction, monitoring and early warning, and 3D visualization modules. It is able to do data management, visualization and disaster prediction, and early warning based on 79 monitoring points of rainfall, infiltration line, and deformation of the tailing dam in the Zijinshan mine. The design and application of the system reflect its features of rich functionality, high practicality, intuitive effect, and high reference value. The system solves the problems of low visualization of monitoring data, poor management of multiple data, and feasible prediction and early warning of point–surface combination. It realizes high-precision prediction of key factors and real-time warning of disaster." @default.
- W4220807524 created "2022-04-03" @default.
- W4220807524 creator A5032657316 @default.
- W4220807524 creator A5041199382 @default.
- W4220807524 creator A5056377589 @default.
- W4220807524 creator A5071314083 @default.
- W4220807524 date "2022-03-08" @default.
- W4220807524 modified "2023-09-28" @default.
- W4220807524 title "3D Visualization Monitoring and Early Warning System of a Tailings Dam—Gold Copper Mine Tailings Dam in Zijinshan, Fujian, China" @default.
- W4220807524 cites W1512121837 @default.
- W4220807524 cites W1993410425 @default.
- W4220807524 cites W1995292807 @default.
- W4220807524 cites W2045044116 @default.
- W4220807524 cites W2121677486 @default.
- W4220807524 cites W2473140540 @default.
- W4220807524 cites W2507863644 @default.
- W4220807524 cites W2591529938 @default.
- W4220807524 cites W2610607022 @default.
- W4220807524 cites W2729424549 @default.
- W4220807524 cites W2784610207 @default.
- W4220807524 cites W2790048945 @default.
- W4220807524 cites W2898695805 @default.
- W4220807524 cites W2917332375 @default.
- W4220807524 cites W2921737341 @default.
- W4220807524 cites W2953743022 @default.
- W4220807524 cites W2955792649 @default.
- W4220807524 cites W2974439194 @default.
- W4220807524 cites W2980376813 @default.
- W4220807524 cites W2992421976 @default.
- W4220807524 cites W2997301728 @default.
- W4220807524 cites W3010796480 @default.
- W4220807524 cites W3021113524 @default.
- W4220807524 cites W3026019812 @default.
- W4220807524 cites W3046208935 @default.
- W4220807524 cites W3083782034 @default.
- W4220807524 cites W3087971104 @default.
- W4220807524 cites W3089118372 @default.
- W4220807524 cites W3090406409 @default.
- W4220807524 cites W3113199601 @default.
- W4220807524 cites W3123672120 @default.
- W4220807524 cites W3127410082 @default.
- W4220807524 cites W3130189865 @default.
- W4220807524 cites W3132616251 @default.
- W4220807524 cites W3135715490 @default.
- W4220807524 cites W3136020951 @default.
- W4220807524 cites W3152622200 @default.
- W4220807524 cites W3153535255 @default.
- W4220807524 cites W3154586238 @default.
- W4220807524 cites W3156234171 @default.
- W4220807524 cites W3158567937 @default.
- W4220807524 cites W3162986684 @default.
- W4220807524 cites W3176373087 @default.
- W4220807524 cites W3197220749 @default.
- W4220807524 cites W3204445114 @default.
- W4220807524 cites W2801080335 @default.
- W4220807524 doi "https://doi.org/10.3389/feart.2022.800924" @default.
- W4220807524 hasPublicationYear "2022" @default.
- W4220807524 type Work @default.
- W4220807524 citedByCount "7" @default.
- W4220807524 countsByYear W42208075242022 @default.
- W4220807524 countsByYear W42208075242023 @default.
- W4220807524 crossrefType "journal-article" @default.
- W4220807524 hasAuthorship W4220807524A5032657316 @default.
- W4220807524 hasAuthorship W4220807524A5041199382 @default.
- W4220807524 hasAuthorship W4220807524A5056377589 @default.
- W4220807524 hasAuthorship W4220807524A5071314083 @default.
- W4220807524 hasBestOaLocation W42208075241 @default.
- W4220807524 hasConcept C111368507 @default.
- W4220807524 hasConcept C124101348 @default.
- W4220807524 hasConcept C127313418 @default.
- W4220807524 hasConcept C136428324 @default.
- W4220807524 hasConcept C16674752 @default.
- W4220807524 hasConcept C178790620 @default.
- W4220807524 hasConcept C185592680 @default.
- W4220807524 hasConcept C191897082 @default.
- W4220807524 hasConcept C192562407 @default.
- W4220807524 hasConcept C204366326 @default.
- W4220807524 hasConcept C2776648687 @default.
- W4220807524 hasConcept C2779296788 @default.
- W4220807524 hasConcept C29825287 @default.
- W4220807524 hasConcept C2994103380 @default.
- W4220807524 hasConcept C36464697 @default.
- W4220807524 hasConcept C39432304 @default.
- W4220807524 hasConcept C41008148 @default.
- W4220807524 hasConcept C5166401 @default.
- W4220807524 hasConcept C544778455 @default.
- W4220807524 hasConcept C76155785 @default.
- W4220807524 hasConceptScore W4220807524C111368507 @default.
- W4220807524 hasConceptScore W4220807524C124101348 @default.
- W4220807524 hasConceptScore W4220807524C127313418 @default.
- W4220807524 hasConceptScore W4220807524C136428324 @default.
- W4220807524 hasConceptScore W4220807524C16674752 @default.
- W4220807524 hasConceptScore W4220807524C178790620 @default.
- W4220807524 hasConceptScore W4220807524C185592680 @default.
- W4220807524 hasConceptScore W4220807524C191897082 @default.
- W4220807524 hasConceptScore W4220807524C192562407 @default.
- W4220807524 hasConceptScore W4220807524C204366326 @default.
- W4220807524 hasConceptScore W4220807524C2776648687 @default.
- W4220807524 hasConceptScore W4220807524C2779296788 @default.
- W4220807524 hasConceptScore W4220807524C29825287 @default.