144 Rust and Stoinski: Using Artificial Intelligence to Assist Tree Risk Assessment is needed to organize optimization tasks, as well as error handling. A complex set of rules can and will be error-prone. The metasystem contains general state- ments on how to deal with such errors. The metasystem contains axiomatic statements that can lead to the creation of axioms in the execu- tion layer, as well as their optimization. The formal languages used are independent of each other, which means that statements of the metasystem only affect the execution layer and not the solution space. Con- versely, the implications of the axiomatic causal chains have no influence on the metasystem. CONCLUSIONS In the risk assessment of a complex living organism, such as a tree, few measurable and many nonmeasurable parameters interact. In the case of nonmeasurable parameters, the impact of the expert’s intuition is high. Therefore, the assessment can lack robustness, credibility, and repeatability, 3 of 8 criteria for the effec- tiveness of risk assessment methods identified in ear- lier studies (Norris 2007). By splitting up larger problems into sets of smaller problems, the system presented guides the user’s attention, making them aware of otherwise implicit assumptions, and thus increasing robustness and credibility. By forming fuzzy sets, statements relating to non- measurable fuzzy parameters of intuition are mathe- matically standardized to increase repeatability. Furthermore, the results obtained via the fuzzy sets can be connected via a calculus that incorporates the expert’s knowledge. By using the GDL as an AI sys- tem, the quality of stored knowledge is constantly checked and improved when using the system. The experience of many tree assessors gained over years is made available to support novice tree assessors. In this project, we provide a proof of concept of an AI system for tree risk assessment. The gaps in our knowledge identified here, especially the lack of uni- versally accepted and scientifically proven rules, are not limited to the use of artificial intelligence, but are relevant to tree assessment at large. A broad discus- sion of the rules that would be accepted in AI systems will also improve conventional tree assessment. LITERATURE CITED Bond J. 2011a. Tree load basic field analysis. Arborist News. 20(2):24-26. Bond J. 2011b. Tree load: Concept. Arborist News. 20(1):12-17. Bond J. 2012. Production use of the BMP tree risk assessment method. Arborist News. 21(4):20-21. Bothe HH. 1993. Fuzzy logic: Einführung in Theorie und Anwendungen. Berlin (Germany): Springer. 228 p. Böttcher J, Weihs U, Rust S. 2016. Untersuchungen zum Schlankheitsgrad (l/d-Verhältnis) von Rosskastanienästen (Aesculus spp.). In: Dujesiefken D, editor. Jahrbuch der Baumpflege 2016. Braunschweig (Germany): Haymarket Media Group. p. 236-244. Cannell MGR, Morgan J. 1989. Branch breakage under snow and ice loads. Tree Physiology. 5(3):307-317. https://doi.org/ 10.1093/treephys/5.3.307 Ciftci C, Arwade SR, Kane B, Brena SF. 2014. Analysis of the probability of failure for open-grown trees during wind storms. Probabilistic Engineering Mechanics. 37:41-50. https://doi.org/10.1016/j.probengmech.2014.04.002 Ciftci C, Kane B, Brena SF, Arwade SR. 2014. Loss in moment capacity of tree stems induced by decay. Trees. 28(2):517-529. https://doi.org/10.1107/s00468-013-0968-8 Dahle GA, James KR, Kane B, Grabosky JC, Detter A. 2017. A review of factors that affect the static load-bearing capacity of urban trees. Arboriculture & Urban Forestry. 43(3):89-106. https://doi.org/10.48044/jauf.2017.009 Dubois DJ, Prade H. 1980. Fuzzy sets and systems: Theory and applications. 1st Ed. London (UK): Academic Press. 393 p. Dunster JA. 2014. Fatalities and injuries caused by trees: Do we have a problem? Arboriculture & Urban Forestry. 40(6):352- 354. https://doi.org/10.48044/jauf.2014.034 Dunster JA, Smiley ET, Matheny N, Lilly S. 2013. Tree risk assessment manual. 1st Ed. Champaign (IL, USA): Interna- tional Society of Arboriculture. 194 p. Forschungsgesellschaft Landschaftsentwicklung Landschaftsbau e.V. (FLL). 2004. Richtlinie zur Überprüfung der Verkehrssi- cherheit von Bäumen. Bonn (Germany): FLL. Gladwell M. 2005. Blink: The power of thinking without thinking. 1st Ed. Boston (MA, USA): Little, Brown and Company. 320 p. Hartley MA, Chalk JJ. 2019. A review of deaths in Australia from accidental tree failures. Melbourne (VIC, Australia): Arbori- culture Australia. 18 p. Jillich S, Köhler J, Rust S, Rust C, Detter A. 2013. Zum Tusam- menhang zwischen Schlankheitsgrad und Bruchversagen. In: Dujesiefken D, editor. Jahrbuch der Baumpflege. Braun- schweig (Germany): Haymarket Media Group. p. 267-273. Kane B, Ryan D, Bloniarz DV. 2001. Comparing formulae that assess strength loss due to decay in trees. Journal of Arbori- culture. 27(2):78-87. Kane BCP, Ryan HDPI. 2003. Examining formulas that assess strength loss due to decay in trees: Woundwood toughness improvement in red maple (Acer rubrum). Journal of Arbori- culture. 29(4):209-217. Kane BCP, Ryan HDPI. 2004. The accuracy of formulas used to assess strength loss due to decay in trees. Journal of Arbori- culture. 30(6):347-356. Koeser AK, Smiley ET. 2017. Impact of assessor on tree risk assessment ratings and prescribed mitigation measures. Urban Forestry & Urban Greening. 24:109-115. https://doi.org/10 .1016/j.ufug.2017.03.027 ©2022 International Society of Arboriculture
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