# SAM-kNN Regressor for Online Learning in Water Distribution Networks

arXiv: https://arxiv.org/abs/2204.01436 · HTML: https://mlsanity.com/paper/2204.01436/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 4 Apr 2022.

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

- [Mnemonics Training: Multi-Class Incremental Learning without Forgetting](https://mlsanity.com/paper/2002.10211/) (2020)
- [Incremental Classifier Learning with Generative Adversarial Networks](https://mlsanity.com/paper/1802.00853/) (2018)
- [Balanced softmax cross-entropy for incremental learning with and without memory](https://mlsanity.com/paper/2103.12532/) (2021)
- [Incremental Concept Learning via Online Generative Memory Recall](https://mlsanity.com/paper/1907.02788/) (2019)
- [Class-incremental Learning with Rectified Feature-Graph Preservation](https://mlsanity.com/paper/2012.08129/) (2020)
- [Incremental Classifier Learning Based on PEDCC-Loss and Cosine Distance](https://mlsanity.com/paper/1906.04734/) (2019)

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

- [Unsupervised Unlearning of Concept Drift with Autoencoders](https://mlsanity.com/paper/2211.12989/) (2022)

## Abstract (arXiv)

Water distribution networks are a key component of modern infrastructure for housing and industry. They transport and distribute water via widely branched networks from sources to consumers. In order to guarantee a working network at all times, the water supply company continuously monitors the network and takes actions when necessary -- e.g. reacting to leakages, sensor faults and drops in water quality. Since real world networks are too large and complex to be monitored by a human, algorithmic monitoring systems have been developed. A popular type of such systems are residual based anomaly detection systems that can detect events such as leakages and sensor faults. For a continuous high quality monitoring, it is necessary for these systems to adapt to changed demands and presence of various anomalies. In this work, we propose an adaption of the incremental SAM-kNN classifier for regression to build a residual based anomaly detection system for water distribution networks that is able to adapt to any kind of change.