Abstract

Abstract To improve the prediction ability of river ecological water requirement in sponge low-carbon urban areas, a prediction model of it based on big data analysis was proposed. A statistical analysis model of river ecological water requirement in sponge and low-carbon urban areas was established. Differential Particle Swarm Optimization Algorithm (DPSA) was used to calculate the characteristic quantity of river ecological water requirement in sponge and low-carbon urban areas. The detection model of ecosystem hydrothermal balance and lognormal distribution time series of groundwater level were constructed for the prediction of river ecological water requirement in sponge and low-carbon urban areas. Combined with the integrated control method of river ecological environment water demand forecasting and information exchange, a decentralized control model of river ecological water demand in low-carbon sponge urban areas was established. Through applicability analysis and model measurement analysis, combined with comprehensive index measurement, the prediction model of river ecological water requirement in low-carbon sponge urban areas was optimized. The empirical analysis results show that this method has good adaptability and high prediction accuracy for the prediction of river ecological water requirement in sponge low-carbon urban areas and improves the reliability of the prediction of river ecological water requirement and the matching level of urban ecological environment.

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