# Sparse Incident-Cluster Learning for 12-hour Port Flood Pre-warning in Digital-Twin Analytics

arXiv: https://arxiv.org/abs/2609.06109 · HTML: https://mlsanity.com/paper/2609.06109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 5 Sep 2026.

## Builds on (most cited first, 4 of 4)

- [LassoNet: A Neural Network with Feature Sparsity](https://mlsanity.com/paper/1907.12207/) (2019)
- [Flood Prediction Using Machine Learning Models: Literature Review](https://mlsanity.com/paper/1908.02781/) (2019)
- [Flood forecasting with machine learning models in an operational framework](https://mlsanity.com/paper/2111.02780/) (2021)
- [Digital Twins for Ports: Derived from Smart City and Supply Chain Twinning Experience](https://mlsanity.com/paper/2301.10224/) (2023)

## Abstract (arXiv)

Port flood digital twins require analytics that warn operators before disruption, but official warning incidents are often few and adjacent observations are temporally dependent. Row-level classification can therefore overstate performance by placing windows from the same event in both model-development and evaluation data. We formulate 12-hour port flood pre-warning as an incident-cluster learning problem and evaluate a digital-twin analytics module using eight-point water-level histories, prediction-time contextual covariates, and interpretable short-window dynamics. The protocol combines fold-specific sparse feature selection, warning-cluster grouping, negative-label controls, 100-repeat random top-k controls, and alert-episode evaluation. Liverpool is the primary four-cluster case study, with harmonised Humber/Hull-proxy and Wessex South data used for protocol-transfer checks. Across the Liverpool folds, the top-10 ElasticNet model achieves mean F2 = 0.696, compared with 0.633 without top-k truncation and 0.681 for full-feature weighted XGBoost. It is the strongest ElasticNet variant, remains competitive with the nonlinear reference using only ten predictors, and exceeds the repeat-level 95th percentile of broad and same-family random subsets. Contextual covariates provide a strong prediction-time anchor, complemented by physically interpretable local dynamics. Historical replay converts risk scores into alert episodes and measures alert duration and false-episode burden. The result is an offline-evaluated analytics and validation module designed for integration into a port digital twin.