# Conformal Prediction-Driven Adaptive Sampling for Digital Water Twins

arXiv: https://arxiv.org/abs/2511.05610 · HTML: https://mlsanity.com/paper/2511.05610/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [Sequential Predictive Conformal Inference for Time Series](https://mlsanity.com/paper/2212.03463/) (2022)
- [Kernel-based Optimally Weighted Conformal Time-Series Prediction](https://mlsanity.com/paper/2405.16828/) (2024)
- [Graph Neural Networks for Pressure Estimation in Water Distribution Systems](https://mlsanity.com/paper/2311.10579/) (2023)
- [Digital Transformation in the Water Distribution System based on the Digital Twins Concept](https://mlsanity.com/paper/2412.06694/) (2024)
- [DiTEC-WDN: A Large-Scale Dataset of Hydraulic Scenarios across Multiple Water Distribution Networks](https://mlsanity.com/paper/2503.17167/) (2025)

## Built on by (most cited first, 1 of 1)

- [Graph Attention Networks with Physical Constraints for Anomaly Detection](https://mlsanity.com/paper/2601.12426/) (2026)

## Abstract (arXiv)

Digital Twins (DTs) for Water Distribution Networks (WDNs) require accurate state estimation with limited sensors. Uniform sampling often wastes resources across nodes with different uncertainty. We propose an adaptive framework combining LSTM forecasting and Conformal Prediction (CP) to estimate node-wise uncertainty and focus sensing on the most uncertain points. Marginal CP is used for its low computational cost, suitable for real-time DTs. Experiments on Hanoi, Net3, and CTOWN show 33--34% lower demand error than uniform sampling at 40% coverage and maintain 89.4--90.2% empirical coverage with only 5--10% extra computation.