Data and Code for: Changepoint-based early leak detection in Andean water transmission systems: a comparative evaluation of spectral decomposition and Bayesian online methods
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2026
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| _version_ | 1866902077791272960 |
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| author | Benavides-Muñoz, Holger |
| author_facet | Benavides-Muñoz, Holger |
| contents | <p><br><strong>Overview</strong><br>This repository contains the replication data, source code, and comprehensive outputs for the study titled "Changepoint-based early leak detection in Andean water transmission systems: a comparative evaluation of spectral decomposition and Bayesian online methods using flow and pressure signals".<br>The research evaluates the performance of four changepoint detection frameworks (BOCD, PELT-BIC, PELT-AIC, and CUSUM) applied to high-resolution flow and pressure time series. The data was collected from controlled leak experiments in a real-scale Andean water transmission pipeline located in Loja, Ecuador, characterized by steep hydraulic gradients and complex topography.</p> <p><strong>Dataset Description</strong><br>The repository is organized into raw data files and compressed archives containing the computational environment and results:<br><strong>1. Primary Data (Raw)</strong><br><em>Q_P_80_Leak1.xlsx</em>: High-resolution (30s) time series for flow (Q) and pressure (P) during the first experimental campaign (Leak Trial 1).<br><em>Q_P_46_Leak2.xlsx</em>: High-resolution (30s) time series for flow and pressure during the second experimental campaign (Leak Trial 2).</p> <p><strong>2. Source Code and Execution Logs (Code R.zip)</strong><br>This archive contains the Code R/ directory with the following files:<br><em>CHANGEPOINT_Rcode.r</em>: The complete R script for signal preprocessing, spectral decomposition, implementation of the Bayesian Online Changepoint Detection (BOCD) and frequentist methods, and statistical performance evaluation.<br><em>CHANGEPOINT_Console.txt</em>: A comprehensive log capturing the RStudio console output, providing model convergence metrics, summary statistics, and execution timestamps to ensure computational reproducibility.</p> <p><strong>3. Model Outputs (analisis_output.zip)</strong><br>This archive contains the analisis_output/ directory, organized into three specialized sub-directories generated by the R script:<br><em>changepoint/</em>: Contains CSV files with detection results for BOCD, ground truth metadata, and the changepoint_objects.rds file for R environment restoration. It includes performance summaries (Sensitivity, Latency, and Detection Rates).<br><em>figures/</em>: High-resolution (.tiff) visualizations, including combined signal analysis, BOCD posterior probability plots (Figures 7 & 8), and statistical distributions (Figures 9 & 10).<br><em>tables/</em>: Detailed descriptive statistics, performance metrics by trial, and formatted result tables (Results_Tables.docx).</p> <p><strong>Usage Notes</strong><br>To replicate the analysis:<br>Ensure RStudio is installed with the necessary dependencies (e.g., ocp, changepoint, ggplot2).<br>Place the raw Excel datasets in the same working directory as the R script.<br>Execute CHANGEPOINT_Rcode.r to regenerate the directory structure and all associated numerical and graphical outputs.</p> <p><strong>Institutions</strong><br>Departmento de Ingeniería Civil, Arquitectura y Geociencias, Universidad Técnica Particular de Loja (UTPL), Ecuador.<br>Research Group:Research and Development Group for the Sustainability of the Urban and Rural Water Cycle.</p> <p><strong>Acknowledgments</strong><br>This research was supported by the Departamento de Ingeniería Civil, Arquitectura y Geociencias, Universidad Técnica Particular de Loja (UTPL). The field experiments were carried out using instrumentation from the UTPL Hydraulics Laboratory. We thank the civil engineering students at UTPL for their assistance in data collection and processing (Bryan David Sarango Cuenca, Roosevelt David Jaramillo Rodríguez, Jonathan Fernando Quezada Puglla, and Hartman Sarango Tene). The author gratefully acknowledges the statistical and methodological guidance provided by researchers at the Federal University of Mato Grosso do Sul (UFMS), which laid the foundation for the approach subsequently refined at UTPL. Gratitude is also extended to the technical team of UMAPAL (Unidad Municipal de Agua Potable y Alcantarillado de Loja), led by Ing. George Buele, for enabling field access and supporting operational testing.</p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19554361 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Data and Code for: Changepoint-based early leak detection in Andean water transmission systems: a comparative evaluation of spectral decomposition and Bayesian online methods Benavides-Muñoz, Holger BOCD (Bayesian Online Changepoint Detection) Leak detection Water transmission systems Andean topography Time series analysis Pressure and flow monitoring RStudio Loja-Ecuador <p><br><strong>Overview</strong><br>This repository contains the replication data, source code, and comprehensive outputs for the study titled "Changepoint-based early leak detection in Andean water transmission systems: a comparative evaluation of spectral decomposition and Bayesian online methods using flow and pressure signals".<br>The research evaluates the performance of four changepoint detection frameworks (BOCD, PELT-BIC, PELT-AIC, and CUSUM) applied to high-resolution flow and pressure time series. The data was collected from controlled leak experiments in a real-scale Andean water transmission pipeline located in Loja, Ecuador, characterized by steep hydraulic gradients and complex topography.</p> <p><strong>Dataset Description</strong><br>The repository is organized into raw data files and compressed archives containing the computational environment and results:<br><strong>1. Primary Data (Raw)</strong><br><em>Q_P_80_Leak1.xlsx</em>: High-resolution (30s) time series for flow (Q) and pressure (P) during the first experimental campaign (Leak Trial 1).<br><em>Q_P_46_Leak2.xlsx</em>: High-resolution (30s) time series for flow and pressure during the second experimental campaign (Leak Trial 2).</p> <p><strong>2. Source Code and Execution Logs (Code R.zip)</strong><br>This archive contains the Code R/ directory with the following files:<br><em>CHANGEPOINT_Rcode.r</em>: The complete R script for signal preprocessing, spectral decomposition, implementation of the Bayesian Online Changepoint Detection (BOCD) and frequentist methods, and statistical performance evaluation.<br><em>CHANGEPOINT_Console.txt</em>: A comprehensive log capturing the RStudio console output, providing model convergence metrics, summary statistics, and execution timestamps to ensure computational reproducibility.</p> <p><strong>3. Model Outputs (analisis_output.zip)</strong><br>This archive contains the analisis_output/ directory, organized into three specialized sub-directories generated by the R script:<br><em>changepoint/</em>: Contains CSV files with detection results for BOCD, ground truth metadata, and the changepoint_objects.rds file for R environment restoration. It includes performance summaries (Sensitivity, Latency, and Detection Rates).<br><em>figures/</em>: High-resolution (.tiff) visualizations, including combined signal analysis, BOCD posterior probability plots (Figures 7 & 8), and statistical distributions (Figures 9 & 10).<br><em>tables/</em>: Detailed descriptive statistics, performance metrics by trial, and formatted result tables (Results_Tables.docx).</p> <p><strong>Usage Notes</strong><br>To replicate the analysis:<br>Ensure RStudio is installed with the necessary dependencies (e.g., ocp, changepoint, ggplot2).<br>Place the raw Excel datasets in the same working directory as the R script.<br>Execute CHANGEPOINT_Rcode.r to regenerate the directory structure and all associated numerical and graphical outputs.</p> <p><strong>Institutions</strong><br>Departmento de Ingeniería Civil, Arquitectura y Geociencias, Universidad Técnica Particular de Loja (UTPL), Ecuador.<br>Research Group:Research and Development Group for the Sustainability of the Urban and Rural Water Cycle.</p> <p><strong>Acknowledgments</strong><br>This research was supported by the Departamento de Ingeniería Civil, Arquitectura y Geociencias, Universidad Técnica Particular de Loja (UTPL). The field experiments were carried out using instrumentation from the UTPL Hydraulics Laboratory. We thank the civil engineering students at UTPL for their assistance in data collection and processing (Bryan David Sarango Cuenca, Roosevelt David Jaramillo Rodríguez, Jonathan Fernando Quezada Puglla, and Hartman Sarango Tene). The author gratefully acknowledges the statistical and methodological guidance provided by researchers at the Federal University of Mato Grosso do Sul (UFMS), which laid the foundation for the approach subsequently refined at UTPL. Gratitude is also extended to the technical team of UMAPAL (Unidad Municipal de Agua Potable y Alcantarillado de Loja), led by Ing. George Buele, for enabling field access and supporting operational testing.</p> <p> </p> |
| title | Data and Code for: Changepoint-based early leak detection in Andean water transmission systems: a comparative evaluation of spectral decomposition and Bayesian online methods |
| topic | BOCD (Bayesian Online Changepoint Detection) Leak detection Water transmission systems Andean topography Time series analysis Pressure and flow monitoring RStudio Loja-Ecuador |
| url | https://doi.org/10.5281/zenodo.19554361 |