FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4280498832> ?p ?o ?g. }

• W4280498832 endingPage "108977" @default.
• W4280498832 startingPage "108977" @default.
• W4280498832 abstract "Understanding the relationship between land use and water quality is essential for effective watershed management. However, it remains challenging to identify such a relationship owing to its nonlinearity. We developed an interpretable machine learning method that integrated the random forest regression (RFR) model with the Shapley Additive exPlanations (SHAP) method to explore the relationship between water quality and land use in the Potomac River Basin (PRB), the second largest tributary entering Chesapeake Bay from 2006 to 2019. The water quality of the 26 sub-watersheds, classified into five types (natural, forested, agricultural, mixed, and urbanized), was investigated using statistical methods and scenario analysis. The results showed that the models employed were effective in predicting the water quality. The mean absolute error (MAE), root mean square error (RMSE), percent bias (PBIAS), R2 coefficient of determination (R2), and Kling-Gupta efficiency (KGE) were 0.011–0.159 mg/L, 0.019–0.219 mg/L, −0.14–0.64%, 0.79–0.99, and 0.69–0.98, respectively, during the training period, which were 0.010–0.201 mg/L, 0.017–0.292 mg/L, −1.87–0.41%, 0.82–0.99, and 0.80–0.97, respectively, during the testing period. The threshold effects of land use patterns were obvious for water quality indicators with high concentrations (i.e., TN and NH4+-N). In contrast, the water quality at low concentrations (i.e., TP and NO3–-N) may be more sensitive to wetland or barren land with changing climate. Agricultural activities and urbanization could be the dominant factors determining nutrient export to the PRB. Meanwhile, the typical ‘sink’ for the nutrient such as wetland may change into the ‘source’ for different nutrient. This study provides an in-depth understanding of how riverine nutrient export responds to the land use gradient in the Chesapeake Bay watershed." @default.
• W4280498832 created "2022-05-22" @default.
• W4280498832 creator A5012449567 @default.
• W4280498832 creator A5022371110 @default.
• W4280498832 creator A5045309022 @default.
• W4280498832 creator A5075480929 @default.
• W4280498832 creator A5081661726 @default.
• W4280498832 creator A5088490090 @default.
• W4280498832 date "2022-07-01" @default.
• W4280498832 modified "2023-10-18" @default.
• W4280498832 title "Use of interpretable machine learning to identify the factors influencing the nonlinear linkage between land use and river water quality in the Chesapeake Bay watershed" @default.
• W4280498832 cites W1967930932 @default.
• W4280498832 cites W1976842871 @default.
• W4280498832 cites W1986756782 @default.
• W4280498832 cites W1995822022 @default.
• W4280498832 cites W2034822584 @default.
• W4280498832 cites W2040898060 @default.
• W4280498832 cites W2058998445 @default.
• W4280498832 cites W2067104632 @default.
• W4280498832 cites W2071147686 @default.
• W4280498832 cites W2090137585 @default.
• W4280498832 cites W2094024519 @default.
• W4280498832 cites W2117969097 @default.
• W4280498832 cites W2119931696 @default.
• W4280498832 cites W2129888542 @default.
• W4280498832 cites W2138763184 @default.
• W4280498832 cites W2139567503 @default.
• W4280498832 cites W2162307871 @default.
• W4280498832 cites W2174608690 @default.
• W4280498832 cites W2328399833 @default.
• W4280498832 cites W2528747924 @default.
• W4280498832 cites W2749975111 @default.
• W4280498832 cites W2790976293 @default.
• W4280498832 cites W2802480633 @default.
• W4280498832 cites W2897395157 @default.
• W4280498832 cites W2911964244 @default.
• W4280498832 cites W2997387270 @default.
• W4280498832 cites W2998681661 @default.
• W4280498832 cites W3006345903 @default.
• W4280498832 cites W3014125413 @default.
• W4280498832 cites W3026725998 @default.
• W4280498832 cites W3039430715 @default.
• W4280498832 cites W3081268376 @default.
• W4280498832 cites W3088222451 @default.
• W4280498832 cites W3112905420 @default.
• W4280498832 cites W3120405596 @default.
• W4280498832 cites W3124498037 @default.
• W4280498832 cites W3138987969 @default.
• W4280498832 cites W3170902298 @default.
• W4280498832 cites W3177823658 @default.
• W4280498832 cites W3207854470 @default.
• W4280498832 cites W4322754442 @default.
• W4280498832 doi "https://doi.org/10.1016/j.ecolind.2022.108977" @default.
• W4280498832 hasPublicationYear "2022" @default.
• W4280498832 type Work @default.
• W4280498832 citedByCount "12" @default.
• W4280498832 countsByYear W42804988322022 @default.
• W4280498832 countsByYear W42804988322023 @default.
• W4280498832 crossrefType "journal-article" @default.
• W4280498832 hasAuthorship W4280498832A5012449567 @default.
• W4280498832 hasAuthorship W4280498832A5022371110 @default.
• W4280498832 hasAuthorship W4280498832A5045309022 @default.
• W4280498832 hasAuthorship W4280498832A5075480929 @default.
• W4280498832 hasAuthorship W4280498832A5081661726 @default.
• W4280498832 hasAuthorship W4280498832A5088490090 @default.
• W4280498832 hasBestOaLocation W42804988321 @default.
• W4280498832 hasConcept C105795698 @default.
• W4280498832 hasConcept C119857082 @default.
• W4280498832 hasConcept C127413603 @default.
• W4280498832 hasConcept C128990827 @default.
• W4280498832 hasConcept C139945424 @default.
• W4280498832 hasConcept C150547873 @default.
• W4280498832 hasConcept C16828302 @default.
• W4280498832 hasConcept C169258074 @default.
• W4280498832 hasConcept C187320778 @default.
• W4280498832 hasConcept C18903297 @default.
• W4280498832 hasConcept C205649164 @default.
• W4280498832 hasConcept C2780797713 @default.
• W4280498832 hasConcept C2985168408 @default.
• W4280498832 hasConcept C33923547 @default.
• W4280498832 hasConcept C39432304 @default.
• W4280498832 hasConcept C39853841 @default.
• W4280498832 hasConcept C41008148 @default.
• W4280498832 hasConcept C4792198 @default.
• W4280498832 hasConcept C524765639 @default.
• W4280498832 hasConcept C58640448 @default.
• W4280498832 hasConcept C67715294 @default.
• W4280498832 hasConcept C76886044 @default.
• W4280498832 hasConcept C86803240 @default.
• W4280498832 hasConcept C88160329 @default.
• W4280498832 hasConceptScore W4280498832C105795698 @default.
• W4280498832 hasConceptScore W4280498832C119857082 @default.
• W4280498832 hasConceptScore W4280498832C127413603 @default.
• W4280498832 hasConceptScore W4280498832C128990827 @default.
• W4280498832 hasConceptScore W4280498832C139945424 @default.
• W4280498832 hasConceptScore W4280498832C150547873 @default.
• W4280498832 hasConceptScore W4280498832C16828302 @default.
• W4280498832 hasConceptScore W4280498832C169258074 @default.

• next