基于长短期记忆网络的西丽水库水质预测

Water Quality Prediction for Xili Reservoir Based on Long-ShortTerm Memory

西丽水库是深圳重要饮水源之一,水库的水质影响着全市人民的供水安全。为及时准确预测西丽水库水质结果,以指导水库水厂供水计划的制定,在利用自适应噪声的完备集合经验模态分解方法进行数据分解的基础上,利用长短期记忆网络(LSTM)模型,建立了基于LSTM的西丽水库水质预测模型。通过模拟计算发现,模型模拟效果较好,其中水质预测模型中总氮、氨氮及总磷的预测结果与实测结果吻合度均较高,能够很好地模拟水库水质浓度变化过程;且对于总氮和氨氮,模型的相对预报误差能控制在10%以下,说明了所建模型的合理性。研究成果可为西丽水库的水质预测及供水计划的制定提供重要模型与技术支撑。

Xili reservoir is one of the most important drinking water sources in Shenzhen.The water quality of the reservoir affects the water supply safety of the whole city.We aim to get timely and accurate water quality prediction results for formulating a scientific and reasonable water supply plan for the reservoir and water plant.Based on data decomposition using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN),we established a long-and short-term memory network model of water quality prediction for Xili Reservoir.Through extensive simulation and calculation,the model demonstrates excellent performance.The prediction results of total nitrogen,ammonia nitrogen and total phosphorus in the water quality prediction model are in good agreement with measured results.For total nitrogen and ammonia nitrogen,the relative prediction error of the model can be controlled below 10%.This highlights the model’s ability to effectively simulate the changing water quality in the reservoir and underscores the model’s rationality.The research findings serve as vital model and technical support for water quality prediction and the development of water supply plans for the Xili Reservoir.