Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms
Study region: Qarhan Salt Lake, the largest salt lake in China, located in the Qaidam Basin. Study focus: Sustainable management of brine resources in Qarhan Salt Lake is crucial due to the impacts of sustained brine pumping, which has altered brine level and salinity distributions. This study developed an automated machine learning (AutoML) approach to model brine levels and salinity, providing a tool for informed resource management decisions. The Geodetector was employed to quantify the influence of various factors on these parameters. New hydrological insights for the region: An integrated approach using AutoML and GIS significantly improved prediction accuracy for both brine levels and salinity. For brine level prediction, the LightGBM (LGBM) model performed best, achieving an R2 of 0.880 (training) and 0.869 (testing). For salinity, Random Forest (RF) was optimal, with an R2 of 0.895 (training) and 0.881 (testing). Geodetector analysis revealed that distance to pumps (q = 0.544), canal density (q = 0.346), lithology (q = 0.324), and distance to lakes (q = 0.260) are key factors influencing brine levels. For salinity, precipitation (q = 0.350) and distance to lakes (q = 0.097) were found to be the most influential. This study demonstrates AutoML's effectiveness in modeling brine dynamics and offers insights into factors influencing changes, aiding brine extraction optimization and sustainable resource management in fragile salt lake ecosystems.
Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms
Study region: Qarhan Salt Lake, the largest salt lake in China, located in the Qaidam Basin. Study focus: Sustainable management of brine resources in Qarhan Salt Lake is crucial due to the impacts of sustained brine pumping, which has altered brine level and salinity distributions. This study developed an automated machine learning (AutoML) approach to model brine levels and salinity, providing a tool for informed resource management decisions. The Geodetector was employed to quantify the influence of various factors on these parameters. New hydrological insights for the region: An integrated approach using AutoML and GIS significantly improved prediction accuracy for both brine levels and salinity. For brine level prediction, the LightGBM (LGBM) model performed best, achieving an R2 of 0.880 (training) and 0.869 (testing). For salinity, Random Forest (RF) was optimal, with an R2 of 0.895 (training) and 0.881 (testing). Geodetector analysis revealed that distance to pumps (q = 0.544), canal density (q = 0.346), lithology (q = 0.324), and distance to lakes (q = 0.260) are key factors influencing brine levels. For salinity, precipitation (q = 0.350) and distance to lakes (q = 0.097) were found to be the most influential. This study demonstrates AutoML's effectiveness in modeling brine dynamics and offers insights into factors influencing changes, aiding brine extraction optimization and sustainable resource management in fragile salt lake ecosystems.
Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms
Dongmei Yu (Autor:in) / Zitao Wang (Autor:in) / Chao Yue (Autor:in) / Jianping Wang (Autor:in)
2025
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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