This paper seeks to develop a shared understanding of the state of the art of AI in geotechnics and to explore future developments, and addresses key research challenges, such as data scarcity and interpretability.
It is inferred that most bacterial phyla were ancestrally anaerobic and adopted aerobic lifestyles after the GOE, however, in the cyanobacterial ancestor, aerobic metabolism likely predated the GOE, which may have facilitated the evolution of oxygenic photosynthesis.
It is argued that such methods that reveal the decision processes of AI models can foster trust in their results and facilitate the broader adoption of AI.
This study establishes a benchmark for landslide susceptibility mapping, providing a scalable and adaptable framework for geospatial hazard prediction, and hold significant implications for land-use planning, disaster management, and environmental conservation in vulnerable regions worldwide.
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