基于RF-BiLSTM模型的河流水质预测

River Water Quality Prediction Based on RF-BiLSTM Model

水环境中过量的氮、磷和高锰酸盐会对流域造成严重污染,准确预测这三类指标的含量对流域污染治理具有重要意义。然而,现有的模型预测精度低,输入因子的选择缺乏数理依据。基于此,以邕江为研究区域,提出一种RF-BiLSTM的混合网络模型。该模型具有利用RF算法提取水质指标最优特征和利用BiLSTM模型提取输入数据的时间特征的优势,采用先降维后预测的方式对TN、TP和COD Mn进行预测,并将深度学习中的CNN、LSTM、BiLSTM和RF-LSTM作为基准模型与本研究所提模型作对比研究。研究结果表明,本研究模型预测TN、TP和COD Mn的平均绝对百分比误差(MAPE)分别达到了4.330%、6.781%和7.384%,均低于其他基准模型,预测结果具有较高的准确性和实用性,可为水环境的污染治理提供有效的技术支持。

Excessive nitrogen,phosphorus,and permanganate in aquatic environments can lead to significant watershed pollution.Accurately predicting the levels of these indicators is crucial for effective pollution control.However,existing models often lack precision,and the selection of input factors lacks a mathematical basis.In this study,we propose a RF-BiLSTM hybrid network model focusing on the Yongjiang watershed as a case study.Leveraging the ability of RF(random forest)to extract optimal water quality index characteristics and the capacity of BiLSTM(bidirectional long-short-term memory)to capture temporal data patterns,our model employs dimensionality reduction followed by prediction to forecast TN,TP,and COD Mn concentrations.Additionally,we conduct comparative analyses with benchmark models such as CNN,LSTM,BiLSTM,and RF-LSTM within the deep learning framework.Results demonstrate that our proposed model achieves lower mean absolute percentage errors(MAPE)for TN,TP,and COD Mn at 4.33%,6.781%,and 7.384%,respectively,outperforming other benchmark models.These findings indicate the high accuracy and practical utility of our predictions,offering valuable technical support for water pollution management.