FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4295278512> ?p ?o ?g. }

• W4295278512 endingPage "101478" @default.
• W4295278512 startingPage "101478" @default.
• W4295278512 abstract "Ecosystem services are essential for human well-being, but are currently facing many natural and anthropogenic threats. Modeling and mapping ecosystem services helps us mitigate, adapt to, and manage these pressures, but overall the field faces multiple major limitations. These include: 1) data availability, 2) understanding, estimation, and reporting of uncertainties, and 3) connecting socio-ecological aspects of ecosystem services. Recent technological advancements in machine learning coupled with rising availability of big data, offer an opportunity to overcome these challenges. We review studies utilizing machine learning and/or big data to overcome these limitations. We collect 56 papers that exemplify the current use of machine learning and big data to address the three identified gaps in the ecosystem service field. We find that although the use of these tools in ecosystem service research is relatively new, it is growing quickly. Big data can directly address data gaps, especially as new big data resources relevant to ecosystem service mapping become available (ex. social media data). Some properties of machine learning can also contribute to addressing data gaps in data sparse environments. Also, many machine learning algorithms can estimate and consider uncertainty, whereas big data can significantly increase sample size, reducing uncertainties in some situations. Some big data sources, like crowdsourced data, provide direct sources of social behaviors and preferences that relate to ecosystem service demand, thus allowing researchers to connect social and biophysical aspects of ecosystem services. Machine learning algorithms provide an effective and efficient tool for handling these large nonlinear socio-ecological datasets in tandem, giving researchers the ability to more realistically model and map ecosystem services without relying on oversimplified proxies or linear algorithms. Despite these opportunities, implementation is still lacking and limitations still hinder use." @default.
• W4295278512 created "2022-09-12" @default.
• W4295278512 creator A5041702949 @default.
• W4295278512 creator A5042391114 @default.
• W4295278512 creator A5055780987 @default.
• W4295278512 date "2022-10-01" @default.
• W4295278512 modified "2023-10-18" @default.
• W4295278512 title "A review of machine learning and big data applications in addressing ecosystem service research gaps" @default.
• W4295278512 cites W158935462 @default.
• W4295278512 cites W1994400465 @default.
• W4295278512 cites W2039756511 @default.
• W4295278512 cites W2056619009 @default.
• W4295278512 cites W2098961883 @default.
• W4295278512 cites W2106745149 @default.
• W4295278512 cites W2123418328 @default.
• W4295278512 cites W2131715540 @default.
• W4295278512 cites W2146577723 @default.
• W4295278512 cites W2162812646 @default.
• W4295278512 cites W2165338797 @default.
• W4295278512 cites W2305395239 @default.
• W4295278512 cites W2326047914 @default.
• W4295278512 cites W2550874488 @default.
• W4295278512 cites W2558066566 @default.
• W4295278512 cites W2576683119 @default.
• W4295278512 cites W2587918629 @default.
• W4295278512 cites W2604938711 @default.
• W4295278512 cites W2607793948 @default.
• W4295278512 cites W2733177031 @default.
• W4295278512 cites W2741869664 @default.
• W4295278512 cites W2754817565 @default.
• W4295278512 cites W2758780521 @default.
• W4295278512 cites W2782917446 @default.
• W4295278512 cites W2802953454 @default.
• W4295278512 cites W2803051823 @default.
• W4295278512 cites W2822665421 @default.
• W4295278512 cites W2883471235 @default.
• W4295278512 cites W2893007767 @default.
• W4295278512 cites W2900082925 @default.
• W4295278512 cites W2908615337 @default.
• W4295278512 cites W2909496728 @default.
• W4295278512 cites W2912696949 @default.
• W4295278512 cites W2935057829 @default.
• W4295278512 cites W2936964546 @default.
• W4295278512 cites W2942047515 @default.
• W4295278512 cites W2954276333 @default.
• W4295278512 cites W2958711989 @default.
• W4295278512 cites W2972303367 @default.
• W4295278512 cites W2979282877 @default.
• W4295278512 cites W2981887967 @default.
• W4295278512 cites W2982495255 @default.
• W4295278512 cites W2988378203 @default.
• W4295278512 cites W2999878361 @default.
• W4295278512 cites W3008569771 @default.
• W4295278512 cites W3009280013 @default.
• W4295278512 cites W3012636042 @default.
• W4295278512 cites W3013297378 @default.
• W4295278512 cites W3014158919 @default.
• W4295278512 cites W3032810573 @default.
• W4295278512 cites W3038262683 @default.
• W4295278512 cites W3040784584 @default.
• W4295278512 cites W3048206310 @default.
• W4295278512 cites W3048244844 @default.
• W4295278512 cites W3049758432 @default.
• W4295278512 cites W3088034826 @default.
• W4295278512 cites W3104995558 @default.
• W4295278512 cites W3117712833 @default.
• W4295278512 cites W3120122216 @default.
• W4295278512 cites W3159068499 @default.
• W4295278512 cites W3163359168 @default.
• W4295278512 cites W3164265633 @default.
• W4295278512 cites W3185151041 @default.
• W4295278512 cites W3185819046 @default.
• W4295278512 cites W4211176081 @default.
• W4295278512 cites W4239545511 @default.
• W4295278512 doi "https://doi.org/10.1016/j.ecoser.2022.101478" @default.
• W4295278512 hasPublicationYear "2022" @default.
• W4295278512 type Work @default.
• W4295278512 citedByCount "15" @default.
• W4295278512 countsByYear W42952785122022 @default.
• W4295278512 countsByYear W42952785122023 @default.
• W4295278512 crossrefType "journal-article" @default.
• W4295278512 hasAuthorship W4295278512A5041702949 @default.
• W4295278512 hasAuthorship W4295278512A5042391114 @default.
• W4295278512 hasAuthorship W4295278512A5055780987 @default.
• W4295278512 hasBestOaLocation W42952785121 @default.
• W4295278512 hasConcept C110872660 @default.
• W4295278512 hasConcept C119857082 @default.
• W4295278512 hasConcept C124101348 @default.
• W4295278512 hasConcept C144133560 @default.
• W4295278512 hasConcept C154945302 @default.
• W4295278512 hasConcept C162853370 @default.
• W4295278512 hasConcept C18903297 @default.
• W4295278512 hasConcept C202444582 @default.
• W4295278512 hasConcept C2522767166 @default.
• W4295278512 hasConcept C2780378061 @default.
• W4295278512 hasConcept C33923547 @default.
• W4295278512 hasConcept C41008148 @default.
• W4295278512 hasConcept C58941895 @default.

• next