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 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 m

SUBMITTER: Panda MR 

PROVIDER: S-EPMC11621784 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature