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Main Author: ArlexMR
Format: Recurso digital
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Published: Zenodo 2026
Online Access:https://doi.org/10.5281/zenodo.18553832
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author ArlexMR
author_facet ArlexMR
contents <p><strong>See the most up-to-date version of this library in the <a href="https://github.com/ArlexMR/HySOM" target="_blank" rel="noopener">GitHub repository</a></strong></p> <p>HySOM is a Python library that simplifies the training and visualization of Self-Organizing Maps (SOMs) for 2D time series. It is specifically designed for the study of concentration–discharge (C–Q) hysteresis loops. With HySOM, you can access the General T-Q SOM—a standard framework for classifying sediment transport hysteresis loops. The library also includes several visualization tools to streamline the analysis of sediment transport hysteresis loops. Additionally, HySOM allows you to train your own SOM for C–Q analysis.</p> <p> </p>
format Recurso digital
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institution Zenodo
language
publishDate 2026
publisher Zenodo
record_format zenodo
spellingShingle HySOM: A Python library for Self-Organizing Map–based analysis of concentration–discharge hysteresis
ArlexMR
<p><strong>See the most up-to-date version of this library in the <a href="https://github.com/ArlexMR/HySOM" target="_blank" rel="noopener">GitHub repository</a></strong></p> <p>HySOM is a Python library that simplifies the training and visualization of Self-Organizing Maps (SOMs) for 2D time series. It is specifically designed for the study of concentration–discharge (C–Q) hysteresis loops. With HySOM, you can access the General T-Q SOM—a standard framework for classifying sediment transport hysteresis loops. The library also includes several visualization tools to streamline the analysis of sediment transport hysteresis loops. Additionally, HySOM allows you to train your own SOM for C–Q analysis.</p> <p> </p>
title HySOM: A Python library for Self-Organizing Map–based analysis of concentration–discharge hysteresis
url https://doi.org/10.5281/zenodo.18553832