| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - water-quality |
| - environmental-monitoring |
| - multimodal |
| - anomaly-detection |
| - remote-sensing |
| - time-series |
| - graph-neural-network |
| pipeline_tag: other |
| --- |
| |
| # SENTINEL — Multimodal AI for Early Water Pollution Detection |
|
|
| **Scalable Environmental Network for Temporal Intelligence and Ecological Learning** |
|
|
| Entry to the Stockholm Junior Water Prize (2026) · Bryan Cheng & Austin Jin · New York, USA |
|
|
| SENTINEL is a multimodal deep-learning early-warning system for water contamination. It fuses |
| five independent environmental data streams — water chemistry, satellite imagery, microbial DNA, |
| gene-expression stress signals, and aquatic-organism behavior — each read by a dedicated encoder. |
| When one stream flags an anomaly, the others confirm and classify it. Trained entirely on public |
| data on a single NVIDIA RTX 4060 (8 GB). |
|
|
| - Code & architectures: https://github.com/austinjin1/SENTINEL-STOCKHOLM |
| - These files are **PyTorch checkpoints** (`torch.load`). Model class definitions live in the |
| GitHub repo under `sentinel/models/`. |
|
|
| ## Models |
|
|
| Every model here corresponds to a model presented in the SENTINEL paper. |
|
|
| | File | Architecture | Role | Key metric (real, held-out) | |
| |------|--------------|------|------------------------------| |
| | `aquassm.pt` | AquaSSM — continuous-time state-space model (8 channels) | Sensor encoder | AUROC 0.939 [0.934–0.943] | |
| | `hydrovit.pt` | HydroViT — CNN–ViT hybrid with band attention | Satellite encoder | water-temp R² 0.893 | |
| | `microbiomenet.pt` | MicroBiomeNet — CLR + sparse-gate + transformer | Microbial encoder | macro-F1 0.899 | |
| | `toxigene.pt` | ToxiGene — hierarchical sparse (Reactome/AOP) | Molecular encoder | F1 0.886 | |
| | `biomotion.pt` | BioMotion — denoising diffusion U-Net | Behavioral encoder | AUROC 0.807 | |
| | `sentinel_fusion.pt` | SENTINEL-Fusion — Perceiver IO cross-modal attention | 5-modality fusion | AUROC 0.939 [0.922–0.956] | |
| | `sentinel_fusion_heads.pt` | Four output heads (anomaly / type / source / cascade) | Fusion heads | — | |
| | `stream_gnn.pt` | Stream-network GNN (NHDPlus topology, 561 sites) | Downstream propagation | AUROC 1.000 | |
| | `digital_twin.pt` | Neural-ODE / physics digital twin (≈342K params, 10 state vars) | Ecosystem simulation | MSE 786 (−45.5% vs physics) | |
| | `hydrodensenet.pt` | HydroDenseNet — DenseNet121 + SpectralStem + CBAM (8.4M params) | SENTINEL-Lite drone screening | temp R² 0.78, DO R² 0.46 | |
| | `species_health.pt` | Keystone-species health & occupancy model | Ecological linkage | see paper §3.3 | |
| | `waterborne_disease.pt` | Waterborne-disease risk model (cyanotoxin, Vibrio, Naegleria, schistosomiasis) | Public-health linkage | AUROC 0.988 | |
|
|
| Reported metrics are computed on real public data with held-out splits and bootstrap 95% CIs |
| (paper Table 2). System-level fusion reaches AUROC = 0.992 in the controlled 31-condition |
| ablation and 0.939 on the full real-data holdout. |
|
|
| ## Training data (SENTINEL-DB) |
|
|
| 390M+ records from 19 public sources: USGS NWIS, NEON, EPA WQP/ECOTOX, GRQA, ESA Sentinel-2, |
| Earth Microbiome Project, NCBI GEO, USGS BioData, NHDPlusV2, GBIF. All publicly accessible. |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| ckpt = torch.load("aquassm.pt", map_location="cpu") # state_dict / checkpoint |
| # Instantiate the matching architecture from the GitHub repo (sentinel/models/...), |
| # then load_state_dict. See repo README for per-encoder loading examples. |
| ``` |
|
|
| ## Limitations |
|
|
| Geographic bias (~95% US/Europe training data); per-modality rather than fully integrated 5-modal |
| validation; cannot detect acute instantaneous releases; under strict spatial holdout transcriptomic |
| generalization remains hard (ToxiGene real-GEO F1 = 0.49); SENTINEL-Lite imagery-only predictions |
| are not yet calibrated for high-stakes use. See paper §4 for full discussion. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{cheng2026sentinel, |
| title = {SENTINEL: Multimodal Artificial Intelligence for Early Water Pollution Detection}, |
| author = {Cheng, Bryan and Jin, Austin}, |
| year = {2026}, |
| note = {Stockholm Junior Water Prize 2026}, |
| url = {https://github.com/austinjin1/SENTINEL-STOCKHOLM} |
| } |
| ``` |
|
|
| License: MIT |
|
|
