Gridded global dataset of industrial water use predicted using the Random Forest
Abstract Spatially distributed industrial water use (IWU) data are essential for effective region-specific water resource management. Such data are often scarce in underdeveloped and developing countries. We propose a random forest regression model to predict IWU at a spatial resolution of 0.5° by c...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04148-5 |
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| author | Manas Ranjan Panda Yeonjoo Kim |
| author_facet | Manas Ranjan Panda Yeonjoo Kim |
| author_sort | Manas Ranjan Panda |
| collection | DOAJ |
| description | Abstract Spatially distributed industrial water use (IWU) data are essential for effective region-specific water resource management. Such data are often scarce in underdeveloped and developing countries. We propose a random forest regression model to predict IWU at a spatial resolution of 0.5° by combining socioeconomic, climatic, and geographical datasets. These datasets included nighttime light (NL), global power plants, country-wise IWU, elevation data (DEM), gross domestic product (GDP), road density (RD), cropland (CRP), wetland (WLND), population (POP), precipitation (PCP), temperature (TEMP), wet days (WET) per year, and potential evapotranspiration (PET). The results show that RD, CRP, POP, GDP, DEM, and TEMP were the most influential variables. We assessed the accuracy of the global IWU map using published and observed datasets from various sources for the major industrialized countries such as the USA and China from 2000 to 2015. The predicted global map shows a reasonable distribution of grid-wise values for highly industrialized countries and data-scarce regions. Thus, fine-resolution maps can support local planning and decision-making for large basins worldwide. |
| format | Article |
| id | doaj-art-284dfc0f38bb4d1ea07bf070665961e8 |
| institution | OA Journals |
| issn | 2052-4463 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
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| series | Scientific Data |
| spelling | doaj-art-284dfc0f38bb4d1ea07bf070665961e82025-08-20T02:31:17ZengNature PortfolioScientific Data2052-44632024-12-0111111410.1038/s41597-024-04148-5Gridded global dataset of industrial water use predicted using the Random ForestManas Ranjan Panda0Yeonjoo Kim1Department of Civil & Environmental Engineering, Yonsei UniversityDepartment of Civil & Environmental Engineering, Yonsei UniversityAbstract Spatially distributed industrial water use (IWU) data are essential for effective region-specific water resource management. Such data are often scarce in underdeveloped and developing countries. We propose a random forest regression model to predict IWU at a spatial resolution of 0.5° by combining socioeconomic, climatic, and geographical datasets. These datasets included nighttime light (NL), global power plants, country-wise IWU, elevation data (DEM), gross domestic product (GDP), road density (RD), cropland (CRP), wetland (WLND), population (POP), precipitation (PCP), temperature (TEMP), wet days (WET) per year, and potential evapotranspiration (PET). The results show that RD, CRP, POP, GDP, DEM, and TEMP were the most influential variables. We assessed the accuracy of the global IWU map using published and observed datasets from various sources for the major industrialized countries such as the USA and China from 2000 to 2015. The predicted global map shows a reasonable distribution of grid-wise values for highly industrialized countries and data-scarce regions. Thus, fine-resolution maps can support local planning and decision-making for large basins worldwide.https://doi.org/10.1038/s41597-024-04148-5 |
| spellingShingle | Manas Ranjan Panda Yeonjoo Kim Gridded global dataset of industrial water use predicted using the Random Forest Scientific Data |
| title | Gridded global dataset of industrial water use predicted using the Random Forest |
| title_full | Gridded global dataset of industrial water use predicted using the Random Forest |
| title_fullStr | Gridded global dataset of industrial water use predicted using the Random Forest |
| title_full_unstemmed | Gridded global dataset of industrial water use predicted using the Random Forest |
| title_short | Gridded global dataset of industrial water use predicted using the Random Forest |
| title_sort | gridded global dataset of industrial water use predicted using the random forest |
| url | https://doi.org/10.1038/s41597-024-04148-5 |
| work_keys_str_mv | AT manasranjanpanda griddedglobaldatasetofindustrialwaterusepredictedusingtherandomforest AT yeonjookim griddedglobaldatasetofindustrialwaterusepredictedusingtherandomforest |