基于ICS优化RBF的水库水质三维预测方法

针对已有水质预测模型在数据降噪、网络参数初始值设置和优化、精度提高等方面能力的不足，构建了一种优化的水质三维预测模型。利用主成分分析算法筛选出水质关键参数，并基于自适应噪声的完全集合经验模态分解算法结合小波阈值模型对三维水质参数和气象数据降噪处理，使用3维卷积神经网络(Three-dimensional convolutional neural networks, 3-D CNN)提取出特征数据集，自编码器(Autoencoder, AE)获得径向基函数(Radial basis function, RBF)网络参数初始化值，改进布谷鸟搜索算法(Improved cuckoo search, ICS)优化更新网络中超参数动态初始化值。广东省湛江市徐闻县大水桥水库区域22个典型在线监测站点以及6个手持监测点的实测数据对比验证结果表明，浊度和藻密度分别与总氮含量强正相关，叶绿素含量与气温强正相关，所提出的水质预测模型在5个典型精准性评价指标方面优于已有文献方法。研究成果可为管理部门和研究者对水质监测提供参考。

Reservoir Water Quality Three-dimensional Prediction Method Based on ICS Optimization RBF

Aiming at the shortcomings of existing water quality prediction models in data noise reduction, initial setting and optimization of network parameters, and accuracy improvement, an optimized three-dimensional water quality prediction model was constructed. The key parameters of water quality were screened by using the principal component analysis algorithm, the three-dimensional water quality parameters and meteorological data were de-noised based on the fully set empirical mode decomposition algorithm based on adaptive noise combined with the wavelet threshold model, the feature data set was extracted by using the three-dimensional convolutional neural network (3 D CNN) and the dynamic initial values of hyperparameters in radial basis function (RBF) neural networks were optimized by an improved cuckoo search algorithm (ICS) based on autoencoder (AE). The comparison and verification results of the measured data from twenty-two typical online monitoring stations and six handheld monitoring stations in the Dashuiqiao Reservoir area of Xuwen County, Zhanjiang City, Guangdong Province showed that turbidity and algae density were positively correlated with total nitrogen, and chlorophyll was positively correlated with temperature, the proposed water quality prediction model was superior to the existing literature methods in five typical accuracy evaluation indicators. The research results can provide reference for management departments and researchers to monitor water quality. Introducing inertial weight and adjusting position parameters to improve CS to speed up the convergence of RBF network. The autoencoder was used to initialize the initial values of network parameters to avoid the defects of artificial random setting. Adding WT algorithm can effectively reduce the white noise in the decomposition and reconstruction process of the fully set empirical mode decomposition algorithm based on adaptive noise.