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XAI
Application in Forestry and Tree-Growth Modeling:
The primary focus of XAI in the text is on its application in
forestry and tree-growth modeling.
XAI is proposed as a tool to enhance the interpretability of tree-
growth models, allowing for a better understanding of the
factors influencing outcomes.
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Use of XAI Techniques in Model Interpretability:
XAI techniques, specifically long short-term memories (LSTMs)
combined with example-based explanations, are suggested to
improve the interpretability of predictive models for tree growth.
Example-based explanations involve generating counterfactual
examples to identify critical features that impact model
outcomes.
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Addressing Challenges in Forestry and Tree-
Growth Modeling:
The challenges specific to integrating XAI in forestry and tree-
growth modeling are discussed. These challenges include the
need for more data on certain aspects of tree growth and the
unique hurdles in this domain.
XAI is presented as a solution to address challenges such as
evaluating model interpretability, ensuring transparency,
overcoming inconsistent terminology, and integrating domain
knowledge.
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Benefits in Decision-Making:
XAI is emphasized as a tool that can provide transparency and
accountability in decision-making processes related to forestry
and climate change mitigation.
The use of XAI aims to support informed decision-making by
making complex models more understandable for both experts
and end-users.
