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A new trend function based regression kriging for spatial modeling of groundwater hydraulic heads under the sparse distribution of measurement sites

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Discrete groundwater level datasets are interpolated often using kriging group of models to produce a spatially continuous groundwater level map. There is always some level of uncertainty associated with diferent interpolation methods. Therefore, we developed a new trend function with the mean groundwater level as a drift variable in the regression kriging approach to predict the groundwater levels at the unvisited locations. Groundwater level data for 29 observation wells in Adyar River Basin were used to assess the performance of the developed regression kriging models. The cross-validation results shows that the proposed regression kriging method in the spatial domain outperforms other physical and kriging-based methods with R2 values of 0.96 and 0.98 during pre-monsoon and post-monsoon seasons, respectively.
Czasopismo
Rocznik
Strony
751--772
Opis fizyczny
Bibliogr. 46 poz.
Twórcy
  • Water Engineering and Management, Department of Civil and Infrastructure Engineering, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand
  • Water Engineering and Management, Department of Civil and Infrastructure Engineering, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand
  • Water Engineering and Management, Department of Civil and Infrastructure Engineering, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand
  • Water Engineering and Management, Department of Civil and Infrastructure Engineering, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand
  • Water Engineering and Management, Department of Civil and Infrastructure Engineering, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand
  • Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600036, India
  • Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai 600036, India
Bibliografia
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  • 44. Wu T, Li Y (2013) Spatial interpolation of temperature in the United States using residual Kriging. Appl Geogr 44:112–120. https://doi.org/10.1016/j.apgeog.2013.07.012
  • 45. Yin S, Xiao Y, Gu X, Hao Q, Liu H, Hao Z, Meng G, Pan X, Pei Q (2019) Geostatistical analysis of hydrochemical variations and nitrate pollution causes of groundwater in an alluvial fan plain. Acta Geophys 67:1191–1203. https://doi.org/10.1007/s11600-019-00302-5
  • 46. Zhu K, Cui Z, Jiang B, Yang G, Chen Z, Meng Q, Yao Y (2013) A DEM-based residual Kriging model for estimating groundwater levels within a large-scale domain: a study for the Fuyang River Basin. Clean Technol Environ Policy. https://doi.org/10.1007/s10098-012-0563-5
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-35acc97a-2482-4cfe-80ad-93c81dddc888
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