基于深度学习的环太湖河流气象水质协同预测模型研究

Deep learning-based water quality prediction model combined with meteorological data for rivers around Taihu Lake

摘要

【目的】为优化水质监测与预警系统，促进河流生态环境的可持续发展。【方法】研究根据环太湖19个水质监测站点2021—2023年逐日水质监测数据和7个气象站的逐日气象资料，采用综合水质指数法(WQI)对水质状况进行定量评价，通过克里金插值法获得与水质监测站点空间相位一致的气象数据，综合考虑气象要素对水质要素的影响机理和Spearman相关分析结果筛选输入的气象数据。采用长短期记忆网络(LSTM)、门控循环单元(GRU)、反向传播网络(BP)方法以及并行的GRU-LSTM模型对综合水质指数WQI进行预测。【结果】结果表明，水质预测模型中，模型精度受输入步长的影响，步长14 d的并行的GRU-LSTM模型表现最佳，预测精度为R²=0.98。【结论】研究成果采用的深度学习模型为河流水质的长期监测和预测提供了一种新的技术路径，结合气象数据的水质预测能够在实际应用中帮助相关部门提前预警水质变化，优化水资源的调度与治理策略，提高水环境的可持续管理能力。

Abstract

[Objective] To optimize water quality monitoring and early warning systems and promote the sustainable development of river ecosystems. [Methods] Using daily water quality monitoring data from 19 monitoring stations around Taihu Lake from 2021 to 2023 and daily meteorological data from 7 meteorological stations, the water quality conditions were quantitatively evaluated using the comprehensive water quality index(WQI) method. Meteorological data spatially consistent with water quality monitoring stations were obtained through Kriging interpolation. Meteorological input variables were selected by comprehensively considering the influencing mechanisms of meteorological factors on water quality parameters and the result of Spearman correlation analysis. Predictions of the comprehensive WQI were conducted using Long Short-Term Memory(LSTM), Gated Recurrent Units(GRU), Backpropagation(BP) neural network, and a parallel GRU-LSTM model. [Results] The result showed that in the water quality prediction model, model accuracy was affected by the input step length. The parallel GRU-LSTM model using a 14-day input step length achieved the best performance, with a prediction accuracy of R²=0.98. [Conclusion] The deep learning-based prediction model provides a new technical approach for long-term monitoring and prediction of river water quality. Water quality prediction combined with meteorological data can help relevant authorities to warn water quality changes in advance in practical applications, optimize water resources scheduling and management strategies, and improve the sustainable management of water environment.

关键词

气象水质协同预测模型 / 深度学习 / 综合水质指数法 / 河流水质 / 影响因素

Key words

water quality prediction model combined with meteorological data / deep learning / comprehensive water quality index / river water quality / influencing factors

基金资助

国家重点研发计划(2023YFC3207503)