A dataset of the global development likelihood of wastewater treatment plants

Sustainable management of water and sanitation requires the constructions of wastewater treatment plants (WWTPs) to remove pollutants from wastewater. A spatially explicit potential map depicting the future development sites of WWTPs is valuable for optimizing their implementation to advance progress toward SDG 6 (Clean Water and Sanitation). Here we use a novel positive-unlabeled deep neural network to model the global development likelihood of WWTPs using fourteen socioeconomic and geographic factors, including GDP, population, land type, vegetation cover, NDVI, distance to settlement, distance to power grid, distance to river, distance to lake, distance to fault, flood hazard, elevation, topographic complexity, and slope. Labeled positive data were from the global database HydroWASTE [1], and 267,325 random sites extracted globally were used as unlabeled data. The positive and unlabeled data were combined and randomly split into a training set (70%) and a test set (30%), repeated ten times. The area under the receiver operating characteristic curve, recall, precision, and F1-score of the model are 0.9876, 0.8611, 0.7178, and 0.7822, respectively. The predicted potential area suitable for WWTPs is about 8,287,091 km2 globally, 51% of which is distributed in the United States, China, India, Russia, and Germany. Cropland, built-up area, tree, and rangeland account for 96% of the potential area. The derived global probability map of WWTPs can provide input for decision-makers to implement multi-objective optimization considering various sustainable development goals. File descriptions: The maps use World Geodetic System 1984 coordinate system with the spatial resolution of 0.0083°. pro_wwtp.tif: the global probability (suitability) map of WWTP development. Ten individual models were trained from ten random realizations of the training set, and the average ensemble model was used for global mapping. std_wwtp.tif: the standard deviation map of the development likelihood of WWTPs over ten individual models. NoData Value: -9999.