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We present a framework for proactive soft-failure prediction that combines physics-inspired feature engineering, tree-ensemble machine learning, and Infrastructure-as-Code (IaC) orchestration. The framework is validated on (i) a multi-physics stochastic simulation spanning five degradation modes (Ornstein\u2013Uhlenbeck, exponential, Weibull, step, oscillatory) and (ii) a publicly available real optical telemetry benchmark (Ghosh &amp; Adhya, 2025) comprising 756 lightpaths\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\times$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    4 failure classes\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\times$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    900 samples (2.72\u00a0M records). A Random Forest regressor augmented with velocity, acceleration, and rolling-statistic features predicts time-to-failure with 17.9\u00a0s mean absolute error (MAE) on synthetic test data and 73.2\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\,\\pm \\,$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    0.03\u00a0s MAE (95% CI,\n                    <jats:inline-formula>\n                      <jats:tex-math>$$n{=}10$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    seeds) on the real benchmark, outperforming heuristic baselines by 6\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\times$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    and matching a tuned XGBoost (\n                    <jats:inline-formula>\n                      <jats:tex-math>$$72.7\\pm 0.05$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    \u00a0s) while surpassing LSTM and 1D-CNN sequence models trained under identical conditions. A trajectory-level train\/test split eliminates temporal leakage. SHapley Additive exPlanations (SHAP) applied to four operational case studies (EDFA-aging, NLI-accelerating, stable-link, and false-alarm trajectories) show that alarm decisions are driven primarily by current OSNR, rolling-window statistics, and velocity, yielding interpretable diagnostics at the moment of alert. An end-to-end latency budget of the proposed IaC pipeline, measured stage-by-stage, totals 6.7\u00a0s mean wall-clock, dominated by Kubernetes reconciliation and Terraform apply; machine-learning inference contributes &lt;\u00a00.5%. The framework is scoped to gradual OSNR-degrading failures (EDFA pump aging, nonlinear-interference drift); extension to laser-current-visible ECL failures through multi-channel feature fusion is identified as future work.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-52186-3","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T10:32:57Z","timestamp":1779705177000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Proactive soft-failure prediction in optical transport networks via physics-inspired features and Infrastructure-as-Code orchestration"],"prefix":"10.1038","volume":"16","author":[{"given":"Ola Mohammed","family":"Ali","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Mostafa A.","family":"Radwan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar Mostafa A.","family":"Radwan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. 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