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• Innovative MF-HHO-GRNN model for accurate water demand prediction. • First application of multi-fidelity data in regional forecasting. • Utilizing Harris hawks optimization to optimize GRNN's smoothing factor for improved model parameter selection. • Achieving exceptional predictive accuracy of 98%, 98%, and 97% for total water demand across varying time horizons. • Validating practicality in Heilongjiang Province, China. Accurate forecasting of water demand plays a crucial role in decision makers to effectively allocate regional water resources and promotes the rational development and utilization of water resources. To address the challenges posed by limited raw data availability and the difficulty in selecting parameters for water demand forecasting models, this study proposes a coupled model called MF-HHO-GRNN (Multi-Fidelity Data, Hybrid Harris Hawks Optimization, and Generalized Regression Neural Network). Initially, multi-fidelity data was constructed employing water demand factor data for Heilongjiang Province, China (1999–2020) as high-fidelity data, alongside data from Liaoning and Jilin Provinces of China as low-fidelity data. Adaptive boosting algorithms, data concatenation, and data dimensionality reduction techniques were employed to construct multi-fidelity data, thereby improving the availability of the original data. Subsequently, Harris hawk optimization algorithms were implemented to optimize the smoothing factor of the GRNN to resolve the challenge of selecting suitable parameters for water demand forecasting models. Finally, the MF-HHO-GRNN model was compared with other prediction models to analyze the prediction accuracy. The optimized MF-HHO-GRNN model meeting accuracy requirements was utilized to forecast agricultural irrigation, industrial, domestic, ecological water demand, and total water demand in Heilongjiang Province for the next 5 years. Results demonstrated that the proposed MF-HHO-GRNN model attained predictive accuracy of 98 %, 98 %, and 97 % for total water demand under three distinct time horizons (1 year, 3 years, and 5 years), proving excellent predictive capabilities. Furthermore, a comprehensive comparison was made between the predicted total water demand and user-specific water demands for the next 5 years in Heilongjiang Province and the regional development plans. The results showed that the predictions of the coupled model aligned with the future development trends of Heilongjiang Province, validating the practicality of the MF-HHO-GRNN model. This study provides decision-makers with an effective method for forecasting water demand, contributing to the future realization of more intelligent and accurate water resource management and decision-making. [ABSTRACT FROM AUTHOR]

[Display omitted] • China's water footprint (WF) under the SSP-RCP scenario was predicted dynamically. • A new attempt to combine the system dynamic (SD) model with SSP-RCP was made. • The grey WF decreases significantly under all scenarios during the forecast year. • The agricultural WF is the main contributor to China's water footprint. • SSP1-2.6 is the best scenario to alleviate water shortages and water pollution. Under the dual influence of socioeconomic development and climate change, water resources in China are under increasing stress. It is of great significance to comprehensively explore the changing trend of China's water footprint (WF) in the future, clarify the water resource challenges that China will face, and alleviate water shortage and water pollution problems. This paper uses System Dynamics (SD) to build a simulation model of China's WF, calculate China's WF from 2000 to 2019, and for the first time, simulate and optimize China's WF from 2020 to 2050 under the SSP-RCP scenario matrix composed of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). The results are as follows. (1) From 2000 to 2019, China's WF increased to 2009 and began to decline. The main contributors are grey WF and agricultural WF. (2) During 2020–2050, China's total WF under the SSP1-2.6 scenario will always be at the lowest; compared with other scenarios, the simulation result of China's total WF under the SSP2-4.5 scenario is the closest to the actual situation in China in 2020, 2021 and 2022. China's total WF under the SSP4-6.0 scenario is close to that under the SSP2-4.5 scenario, but the numerical variation characteristics of the WF composition are different. China's total WF under the SSP3-7.0 and SSP5-8.5 scenarios is high. (3) Based on the changes in WF and water resources supply/demand ratio under different scenarios, the SSP1-2.6 scenario is the best scenario to mitigate a future water shortage and water pollution in China. The research results can help decision-makers formulate relevant management policies and socioeconomic development models for water resource utilization and provide decision support for alleviating water shortages and water pollution in China. [ABSTRACT FROM AUTHOR]