基于神经网络模型的人工湿地水质预测模型

Artificial wetland water quality prediction model based on neural network model

水质预测是水质保护的基本内容,也是解决水资源危机的重要依据。在水污染防治中,水质的准确表达能够反映水体的污染状况和未来趋势,为特定区域的水资源保护提供科学依据。在当前信息技术迅速发展的背景下,智能算法在构建水质预测模型方面的应用日益广泛,这对水资源的污染控制与预防具有重要意义。首先,应用Z-score方法和局部线性趋势判断法识别并校正数据中的异常值;其次,采用SG滤波法对水质数据进行平滑和降噪处理;进一步地,引入优化后的循环神经网络(RNN)模型,并使用LSTM-GRU结构(一种改良的长短期记忆网络结构)替换标准的隐藏层单元。LSTMGRU模型能够有效区分重要与非重要的信息,实现选择性记忆,从而提高对历史水质参数的学习效率并显著提升预测结果的准确性。通过仿真分析,与传统的水质参数预测模型相比,LSTM-GRU模型的泛化能力更强,预测精度更高,具有更高的有效性和实用性。

Water quality prediction is a fundamental aspect of water quality protection and an important basis for addressing water resource crises.In the prevention and control of water pollution,accurate expression of water quality can reflect the pollution status and future trends of water bodies,providing scientific basis for the protection of water resources in specific regions.In the context of rapid development of information technology,the application of intelligent algorithms in building water quality prediction models is becoming increasingly widespread,which is of great significance for the control and prevention of water pollution.Firstly,the Z-score method and local linear trend judgment method are applied to identify and correct outliers in the data.Secondly,SG filtering method is used to smooth and denoise the water quality data.Furthermore,an optimized recurrent neural network(RNN)model was introduced,and the LSTM-GRU structure(an improved long short-term memory network structure)was used to replace the standard hidden layer units.The LSTM-GRU model can effectively distinguish between important and unimportant information,achieve selective memory,thereby improving the learning efficiency of historical water quality parameters and significantly enhancing the accuracy of prediction results.Through simulation analysis,compared with traditional water quality parameter prediction models,the LSTM-GRU model has stronger generalization ability,higher prediction accuracy,and higher effectiveness and practicality.