FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384701057> ?p ?o ?g. }

• W4384701057 endingPage "129946" @default.
• W4384701057 startingPage "129946" @default.
• W4384701057 abstract "Statistical learning seeks to learn statistics-based rules for data analysis tasks from known examples of inputs, or features, and corresponding outcomes and includes machine learning (ML) and deep learning (DL) algorithms. Data sets that are noisy, include significant uncertainty, and have extreme values hinder the learning process. In this study, aquifer recharge predictors are developed using four, random forest or gradient boosting ML methods and Long Short-Term Memory (LSTM) networks, a DL method to: (1) examine predictive skill when trained using noisy and uncertain data and (2) identify advantages of statistical learning implementations for prediction of water budget outcomes relative to process-based water budget calculations. Recharge was selected as the learning outcome because it is not observed and inherently uncertain. Precipitation, potential evapotranspiration (PET), and river discharge are the features, or inputs, and are calculated, or modeled, values and are not directly observed; consequently, they are expected to be noisy and uncertain because of contamination with measurement and model error. A common-sense baseline is developed and implemented to account for uncertainty and noise in outcomes for training and validation; the baseline provides delineation of a lower goodness-of-fit threshold that identifies when trained ML and DL models generate prediction skill and an upper goodness-of-fit threshold above which the models are learning to reproduce noise and bias. For statistical learning regression implementations, features and outcomes need to be transformed to be Gaussian-like. Inherent variability and extreme events in precipitation, discharge, and recharge data sets require power transformation, or at least scaling of logarithms, to enhance predictive skill. Identified advantages to statistical learning of water budget outcomes are the ability to use dimensionless trends for features and to represent a complex study site with the same level of effort as a simple site." @default.
• W4384701057 created "2023-07-20" @default.
• W4384701057 creator A5036625050 @default.
• W4384701057 creator A5039794310 @default.
• W4384701057 date "2023-09-01" @default.
• W4384701057 modified "2023-09-27" @default.
• W4384701057 title "Statistical learning of water budget outcomes accounting for target and feature uncertainty" @default.
• W4384701057 cites W1514248671 @default.
• W4384701057 cites W1966334841 @default.
• W4384701057 cites W2003696872 @default.
• W4384701057 cites W2015050172 @default.
• W4384701057 cites W2037460094 @default.
• W4384701057 cites W2057385875 @default.
• W4384701057 cites W2064675550 @default.
• W4384701057 cites W2077968790 @default.
• W4384701057 cites W2124141023 @default.
• W4384701057 cites W2138763184 @default.
• W4384701057 cites W2142936519 @default.
• W4384701057 cites W2160852351 @default.
• W4384701057 cites W2169744906 @default.
• W4384701057 cites W2587589412 @default.
• W4384701057 cites W2800819102 @default.
• W4384701057 cites W2989857225 @default.
• W4384701057 cites W2995149074 @default.
• W4384701057 cites W3023592169 @default.
• W4384701057 cites W3101203587 @default.
• W4384701057 cites W3106370744 @default.
• W4384701057 cites W3117761798 @default.
• W4384701057 cites W3149057642 @default.
• W4384701057 cites W3203923138 @default.
• W4384701057 cites W4327621744 @default.
• W4384701057 doi "https://doi.org/10.1016/j.jhydrol.2023.129946" @default.
• W4384701057 hasPublicationYear "2023" @default.
• W4384701057 type Work @default.
• W4384701057 citedByCount "0" @default.
• W4384701057 crossrefType "journal-article" @default.
• W4384701057 hasAuthorship W4384701057A5036625050 @default.
• W4384701057 hasAuthorship W4384701057A5039794310 @default.
• W4384701057 hasBestOaLocation W43847010571 @default.
• W4384701057 hasConcept C105795698 @default.
• W4384701057 hasConcept C119857082 @default.
• W4384701057 hasConcept C127413603 @default.
• W4384701057 hasConcept C136389625 @default.
• W4384701057 hasConcept C154945302 @default.
• W4384701057 hasConcept C169258074 @default.
• W4384701057 hasConcept C174091901 @default.
• W4384701057 hasConcept C176783924 @default.
• W4384701057 hasConcept C187320778 @default.
• W4384701057 hasConcept C18903297 @default.
• W4384701057 hasConcept C33923547 @default.
• W4384701057 hasConcept C41008148 @default.
• W4384701057 hasConcept C50644808 @default.
• W4384701057 hasConcept C75622301 @default.
• W4384701057 hasConcept C76177295 @default.
• W4384701057 hasConcept C86803240 @default.
• W4384701057 hasConceptScore W4384701057C105795698 @default.
• W4384701057 hasConceptScore W4384701057C119857082 @default.
• W4384701057 hasConceptScore W4384701057C127413603 @default.
• W4384701057 hasConceptScore W4384701057C136389625 @default.
• W4384701057 hasConceptScore W4384701057C154945302 @default.
• W4384701057 hasConceptScore W4384701057C169258074 @default.
• W4384701057 hasConceptScore W4384701057C174091901 @default.
• W4384701057 hasConceptScore W4384701057C176783924 @default.
• W4384701057 hasConceptScore W4384701057C187320778 @default.
• W4384701057 hasConceptScore W4384701057C18903297 @default.
• W4384701057 hasConceptScore W4384701057C33923547 @default.
• W4384701057 hasConceptScore W4384701057C41008148 @default.
• W4384701057 hasConceptScore W4384701057C50644808 @default.
• W4384701057 hasConceptScore W4384701057C75622301 @default.
• W4384701057 hasConceptScore W4384701057C76177295 @default.
• W4384701057 hasConceptScore W4384701057C86803240 @default.
• W4384701057 hasFunder F4320312994 @default.
• W4384701057 hasLocation W43847010571 @default.
• W4384701057 hasOpenAccess W4384701057 @default.
• W4384701057 hasPrimaryLocation W43847010571 @default.
• W4384701057 hasRelatedWork W2911455822 @default.
• W4384701057 hasRelatedWork W3174196512 @default.
• W4384701057 hasRelatedWork W3211546796 @default.
• W4384701057 hasRelatedWork W4280641190 @default.
• W4384701057 hasRelatedWork W4281560664 @default.
• W4384701057 hasRelatedWork W4281616679 @default.
• W4384701057 hasRelatedWork W4293525103 @default.
• W4384701057 hasRelatedWork W4308191010 @default.
• W4384701057 hasRelatedWork W4318350883 @default.
• W4384701057 hasRelatedWork W4323021782 @default.
• W4384701057 hasVolume "624" @default.
• W4384701057 isParatext "false" @default.
• W4384701057 isRetracted "false" @default.
• W4384701057 workType "article" @default.