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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.18484341 |
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| _version_ | 1866901941534064640 |
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| author | Guaman Pintado, Pamela Maricela Muru, Merle Uuemaa, Evelyn |
| author_facet | Guaman Pintado, Pamela Maricela Muru, Merle Uuemaa, Evelyn |
| contents | <p>This dataset provides the supplementary output products supporting the results and figures reported in <em>Guamán-Pintado et al.</em>, <strong>“In-stream wetland suitability mapping using Analytic Hierarchy Process and machine learning.”</strong></p> <p>The repository contains raster-based <strong>in-stream wetland suitability maps</strong> generated using two complementary approaches: an <strong>expert-based Analytic Hierarchy Process (AHP)</strong> and a <strong>Random Forest (RF)</strong> machine learning model. The outputs represent the final spatial predictions used to analyse and compare methodological performance.</p> <p>Suitability maps are provided for combinations of:</p> <ul> <li> <p>modelling approach (AHP and RF),</p> </li> <li> <p>data origin (local and global datasets), and</p> </li> <li> <p>spatial resolution (10 m and 50 m).</p> </li> </ul> <p>All raster files are delivered in GeoTIFF format and represent continuous suitability scores, where higher values indicate higher feasibility for in-stream wetland creation or restoration.</p> <p>This dataset includes only the final model outputs used to produce the results and figures reported in the article. The model implementation and training code are available at <a target="_new" rel="noopener">https://doi.org/10.5281/zenodo.18403086</a>. Full methodological details, predictor variables, and validation procedures are described in the associated publication.</p> <p>This dataset is intended to support reproducibility, transparency, and reuse in research related to wetland restoration planning, suitability mapping, and spatial environmental modelling.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18484341 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Data supplement to "In-stream wetland suitability mapping using Analytic Hierarchy Process and machine learning" Guaman Pintado, Pamela Maricela Muru, Merle Uuemaa, Evelyn <p>This dataset provides the supplementary output products supporting the results and figures reported in <em>Guamán-Pintado et al.</em>, <strong>“In-stream wetland suitability mapping using Analytic Hierarchy Process and machine learning.”</strong></p> <p>The repository contains raster-based <strong>in-stream wetland suitability maps</strong> generated using two complementary approaches: an <strong>expert-based Analytic Hierarchy Process (AHP)</strong> and a <strong>Random Forest (RF)</strong> machine learning model. The outputs represent the final spatial predictions used to analyse and compare methodological performance.</p> <p>Suitability maps are provided for combinations of:</p> <ul> <li> <p>modelling approach (AHP and RF),</p> </li> <li> <p>data origin (local and global datasets), and</p> </li> <li> <p>spatial resolution (10 m and 50 m).</p> </li> </ul> <p>All raster files are delivered in GeoTIFF format and represent continuous suitability scores, where higher values indicate higher feasibility for in-stream wetland creation or restoration.</p> <p>This dataset includes only the final model outputs used to produce the results and figures reported in the article. The model implementation and training code are available at <a target="_new" rel="noopener">https://doi.org/10.5281/zenodo.18403086</a>. Full methodological details, predictor variables, and validation procedures are described in the associated publication.</p> <p>This dataset is intended to support reproducibility, transparency, and reuse in research related to wetland restoration planning, suitability mapping, and spatial environmental modelling.</p> |
| title | Data supplement to "In-stream wetland suitability mapping using Analytic Hierarchy Process and machine learning" |
| url | https://doi.org/10.5281/zenodo.18484341 |