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Increasing the robustness of natural resource management research by using multiple methods

Frey, Ulrich (2018) Increasing the robustness of natural resource management research by using multiple methods. European Social Simulation Conference, 2018-08-20 - 2018-08-24, Stockholm.

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Kurzfassung

Research on natural resource management like fisheries, irrigation systems or forestry traditionally uses case studies, providing us with a rich, in-depth perspective on many single systems. This comes with a disadvantage - lacking comparability. One reason for that is a multitude of methods used. If, for example, the drivers for ecological success in irrigation systems are to be identified, disagreements between studies may be due to differences in the variables examined, their operationalization or, precisely the methods used. To my knowledge, there is no study in the natural resource management and social-ecological systems literature that analyses the influence of method choice on the results. Therefore, we use a high-quality data set, the Nepal Irrigation Institutions and Systems database (NIIS), developed at the Ostrom Workshop, of 263 cases with each record having information on around 600 variables. With that data set we test the influence of methods on the results, i.e. the relevance of well-known concepts for success in natural resource management, like participation of users, relations with other groups, etc. We use multivariate linear regressions (MLR), random forests (RF), gradient boosting (GBM), shallow neural networks (SNN) and deep neural networks (DNN). The results indicate that although some agreements exist across methods, there are substantial differences as well. We see this research as a step towards increasing the robustness of results and improving the generalisability and reproducibility of natural resource management research.

elib-URL des Eintrags:https://elib.dlr.de/128580/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Increasing the robustness of natural resource management research by using multiple methods
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Frey, UlrichUlrich.Frey (at) dlr.dehttps://orcid.org/0000-0002-9803-1336NICHT SPEZIFIZIERT
Datum:2018
Referierte Publikation:Ja
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:natural resource management; sustainability; machine learning
Veranstaltungstitel:European Social Simulation Conference
Veranstaltungsort:Stockholm
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:20 August 2018
Veranstaltungsende:24 August 2018
HGF - Forschungsbereich:Energie
HGF - Programm:TIG Technologie, Innovation und Gesellschaft
HGF - Programmthema:Erneuerbare Energie- und Materialressourcen für eine nachhaltige Zukunft
DLR - Schwerpunkt:Energie
DLR - Forschungsgebiet:E SY - Energiesystemanalyse
DLR - Teilgebiet (Projekt, Vorhaben):E - Systemanalyse und Technikbewertung (alt)
Standort: Stuttgart
Institute & Einrichtungen:Institut für Technische Thermodynamik > Energiesystemanalyse
Hinterlegt von: Frey, Ulrich
Hinterlegt am:22 Aug 2019 16:04
Letzte Änderung:24 Apr 2024 20:32

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