Topological Deep Learning for Coral Reefs

The conservation of marine ecosystems, especially coral reefs, requires advanced tools to capture their complexity. This chapter proposes integrating deep autoencoder neural networks with algebraic topology to analyze and reconstruct marine data. Persistent homology detects invariant topological features components, cycles, cavities across filtration scales. Combined with autoencoders, it reduces dimensionality and preserves topology during encoding and decoding. The process involves data preprocessing, latent-space training, persistence diagrams, Betti curves, and evaluation through mean squared error and Wasserstein distance. Results show that topologically informed models enhance interpretability, reveal reef health indicators, and improve transparency. This fusion of AI and topology supports sustainable ocean conservation through robust monitoring and predictive frameworks aligned with global goals. All the code can be consulted at: https://github.com/JAIME6609/WATER-MODELS/blob/main/ARRECIFES-TOPOLOGIA-02C-INGLES.py