LAUSR

Abstract Footprint evaluation is an important tool for assessing the appropriation of ecological assets, GHG emissions, freshwater consumption parameters, etc., within a specified region. However, traditional evaluation of footprints for mega cities or urban agglomerations requires overmuch different types of high-quality data. There is a great need of seeking a smart model/approach with declined data requirements for evaluation of footprints where part of data can hardly be accessed. Here we propose a new ensemble inversion model (EIM) based on integrated multitask machine learning (MML) and multi-modeling stacking (MMS) algorithms for smart evaluation and prediction of water, carbon and ecological footprints. The accuracy and generalization capability of the model are illustrated through three largest urban agglomerations in the middle reaches of the Yangtze River (MRYR). The testing results show that the EIM achieves similar prediction performance compared to traditional footprints calculation methods (R2 = 0.91, RMSE = 0.18, MAE = 0.11), yet greatly reduces the amount of required data by approximately 80%. Moreover, the accuracy of the EIM is improved by more than 20%, compared with other models using a single inversion algorithm. The modeling results also show that 1) water, carbon and ecological footprints are significantly positively correlated, and 2) an annual increase of 4.8% can be found in terms of the urban environmental pressure index (UEPI), and its projection is even less optimistic for the future.