  Predicting dissolved oxygen (DO) concentrations in north temperate lakes
requires a comprehensive study of phenological patterns across various
ecosystems, which highlights the significance of selecting phenological
features and feature interactions. Process-based models are limited by partial
process knowledge or oversimplified feature representations, while machine
learning models face challenges in efficiently selecting relevant feature
interactions for different lake types and tasks, especially under the
infrequent nature of DO data collection. In this paper, we propose a
Nature-Guided Cognitive Evolution (NGCE) strategy, which represents a
multi-level fusion of adaptive learning with natural processes. Specifically,
we utilize metabolic process-based models to generate simulated DO labels.
Using these simulated labels, we implement a multi-population cognitive
evolutionary search, where models, mirroring natural organisms, adaptively
evolve to select relevant feature interactions within populations for different
lake types and tasks. These models are not only capable of undergoing crossover
and mutation mechanisms within intra-populations but also, albeit infrequently,
engage in inter-population crossover. The second stage involves refining these
models by retraining them with real observed labels. We have tested the
performance of our NGCE strategy in predicting daily DO concentrations across a
wide range of lakes in the Midwest, USA. These lakes, varying in size, depth,
and trophic status, represent a broad spectrum of north temperate lakes. Our
findings demonstrate that NGCE not only produces accurate predictions with few
observed labels but also, through gene maps of models, reveals sophisticated
phenological patterns of different lakes.
