FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4353095470> ?p ?o ?g. }

• W4353095470 endingPage "129424" @default.
• W4353095470 startingPage "129424" @default.
• W4353095470 abstract "Large groundwater level (GWL) data sets are often patchy with hydrographs containing continuous gaps and irregular measurement frequencies. However, most statistical time series analyses require regular observations, thus hydrographs with larger gaps are routinely excluded from further analysis despite the loss of coverage and representativity of an initially large data set. Missing values can be filled in with different imputation methods, yet the challenge is to assess the imputation performance of automated methods. Assessment of such methods tends to be carried out on randomly introduced missing values. However, large GWL data sets are commonly dominated by more complex patterns of missing values with longer contiguous gaps. This study presents a new artificial gap introduction approach (TGP- typical gap patterns) that improves our understanding of automated imputation performance by mimicking typical gap patterns found in regional scale groundwater hydrographs. Imputation performance of machine learning algorithm missForest and imputePCA is then compared with commonly applied linear interpolation to prepare a gapless daily GWL data set for the Baltic states (Estonia, Latvia, Lithuania). We observed that imputation performance varies among different gap patterns, and performance for all imputation algorithms declined when infilling previously unseen extremes and hydrographs influenced by groundwater abstraction. Further, missForest algorithm substantially outperformed other methods when infilling contiguous gaps (up to 2.5 years), while linear interpolation performs similarly for short random gaps. The TGP approach can be of use to assess the complexity of missing observation patterns in a data set and its value lies in assessing the performance of gap filling methods in a more realistic way. Thus the approach aids the appropriate selection of imputation methods, a task not limited to groundwater level time series alone. The study further provides insights into region-specific data peculiarities that can assist groundwater analysis and modelling." @default.
• W4353095470 created "2023-03-23" @default.
• W4353095470 creator A5007524014 @default.
• W4353095470 creator A5019307159 @default.
• W4353095470 creator A5022780605 @default.
• W4353095470 creator A5025954664 @default.
• W4353095470 date "2023-05-01" @default.
• W4353095470 modified "2023-10-09" @default.
• W4353095470 title "Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns" @default.
• W4353095470 cites W1616007686 @default.
• W4353095470 cites W1977098485 @default.
• W4353095470 cites W1993220086 @default.
• W4353095470 cites W2016381774 @default.
• W4353095470 cites W2041954775 @default.
• W4353095470 cites W2049017883 @default.
• W4353095470 cites W2058191083 @default.
• W4353095470 cites W2064186732 @default.
• W4353095470 cites W2127170577 @default.
• W4353095470 cites W2131258393 @default.
• W4353095470 cites W2141019052 @default.
• W4353095470 cites W2328399833 @default.
• W4353095470 cites W2329290504 @default.
• W4353095470 cites W2367241647 @default.
• W4353095470 cites W2581082906 @default.
• W4353095470 cites W2588589522 @default.
• W4353095470 cites W2735864302 @default.
• W4353095470 cites W2742041047 @default.
• W4353095470 cites W2791662920 @default.
• W4353095470 cites W2791905261 @default.
• W4353095470 cites W2797174322 @default.
• W4353095470 cites W2802094115 @default.
• W4353095470 cites W2819873736 @default.
• W4353095470 cites W2835039253 @default.
• W4353095470 cites W2884738788 @default.
• W4353095470 cites W2885373369 @default.
• W4353095470 cites W2887293259 @default.
• W4353095470 cites W2891661815 @default.
• W4353095470 cites W2892133999 @default.
• W4353095470 cites W2907891425 @default.
• W4353095470 cites W2911964244 @default.
• W4353095470 cites W2943844017 @default.
• W4353095470 cites W2945381569 @default.
• W4353095470 cites W2957997280 @default.
• W4353095470 cites W2965460175 @default.
• W4353095470 cites W2981147788 @default.
• W4353095470 cites W3003647971 @default.
• W4353095470 cites W3004565078 @default.
• W4353095470 cites W3015307406 @default.
• W4353095470 cites W3037074555 @default.
• W4353095470 cites W3090852558 @default.
• W4353095470 cites W3100771313 @default.
• W4353095470 cites W3126156438 @default.
• W4353095470 cites W3130057174 @default.
• W4353095470 cites W3163502814 @default.
• W4353095470 cites W3168031629 @default.
• W4353095470 cites W3170657538 @default.
• W4353095470 cites W3199836289 @default.
• W4353095470 cites W3209796424 @default.
• W4353095470 cites W4200198638 @default.
• W4353095470 cites W4205820660 @default.
• W4353095470 cites W4210535887 @default.
• W4353095470 cites W4214690209 @default.
• W4353095470 cites W4220727972 @default.
• W4353095470 cites W4220732246 @default.
• W4353095470 cites W4220898870 @default.
• W4353095470 cites W4283395601 @default.
• W4353095470 cites W4307019960 @default.
• W4353095470 cites W96574957 @default.
• W4353095470 doi "https://doi.org/10.1016/j.jhydrol.2023.129424" @default.
• W4353095470 hasPublicationYear "2023" @default.
• W4353095470 type Work @default.
• W4353095470 citedByCount "3" @default.
• W4353095470 countsByYear W43530954702023 @default.
• W4353095470 crossrefType "journal-article" @default.
• W4353095470 hasAuthorship W4353095470A5007524014 @default.
• W4353095470 hasAuthorship W4353095470A5019307159 @default.
• W4353095470 hasAuthorship W4353095470A5022780605 @default.
• W4353095470 hasAuthorship W4353095470A5025954664 @default.
• W4353095470 hasBestOaLocation W43530954701 @default.
• W4353095470 hasConcept C104114177 @default.
• W4353095470 hasConcept C105795698 @default.
• W4353095470 hasConcept C124101348 @default.
• W4353095470 hasConcept C126645576 @default.
• W4353095470 hasConcept C137800194 @default.
• W4353095470 hasConcept C153180895 @default.
• W4353095470 hasConcept C154936535 @default.
• W4353095470 hasConcept C154945302 @default.
• W4353095470 hasConcept C171836373 @default.
• W4353095470 hasConcept C205649164 @default.
• W4353095470 hasConcept C33923547 @default.
• W4353095470 hasConcept C41008148 @default.
• W4353095470 hasConcept C58041806 @default.
• W4353095470 hasConcept C58640448 @default.
• W4353095470 hasConcept C9357733 @default.
• W4353095470 hasConceptScore W4353095470C104114177 @default.
• W4353095470 hasConceptScore W4353095470C105795698 @default.
• W4353095470 hasConceptScore W4353095470C124101348 @default.
• W4353095470 hasConceptScore W4353095470C126645576 @default.

• next