基于SSA-BP-SVM模型的云龙湖水质反演研究

利用遥感技术进行水质监测,全面地掌握水质分布情况对水环境保护具有重要意义.水质参数与地表反射率并非简单的线性关系,BP神经网络和支持向量机(SVM),因其非线性模拟的特点,被广泛应用于水质反演.传统BP神经网络存在收敛缓慢、容易陷入局部最优的问题;SVM虽然具有很好的拟合能力,但受惩罚系数及核函数参数影响较大.以云龙湖为研究区域,利用Sentinel-2影像和实测数据,针对重要水质参数电导率和浊度,提出一种基于麻雀搜索算法(SSA)优化BP神经网络及SVM的水质反演耦合模型,利用SSA对BP神经网络及SVM进行参数寻优,基于验证集MAE计算模型权重,对SSA-BP、SSA-SVM模型测试组输出层加权计算后获得最终反演结果.与BPNN、SVM、SSA-BP、SSA-SVM模型对比,结果表明:1)Sentinel-2影像对电导率及浊度的敏感波段均为可见光及短波红外波段;2)SSA-BP-SVM水质反演耦合模型精度更高,电导率及浊度反演模型R²分别为0.92、0.89;3)云龙湖具有典型的城市水体特征,电导率受上游南望净水厂排水影响较大,浊度受社会生产活动带来的颗粒污染物影响较大.基于Sentinel-2影像利用SSA-BP-SVM模型进行水质反演具有较好的应用潜力,能够为云龙湖水质监测以及制定保护措施提供一定的技术支撑.

Water quality retrieval of Yunlong Lake based on SSA-BP-SVM model

The timely and accurate monitoring of water quality via remote sensing is of great significance to water environment protection.However,the relationship between water quality parameters and surface reflectance is not a simple linear one.BP neural network and Support Vector Machine (SVM) have been widely used in water quality inversion for their nonlinear simulation characteristics,yet traditional BP neural network is perplexed by slow convergence and being easy to fall into local optimum,while SVM is greatly affected by penalty coefficient and kernel function parameter.Here,a coupled model using Sparrow Search Algorithm (SSA) to optimize BP neural network and SVM is proposed to retrieve water quality parameters of conductivity and turbidity in Yunlong Lake from Sentinel-2 images.SSA is used to optimize the parameters of BP neural network and SVM,the model weight is calculated based on verification set MAE,and the final inversion results are obtained after the weighted calculation of output layer of SSA-BP and SSA-SVM model test group.And comparisons are carried out between the proposed SSA-BP-SVM model and BPNN,SVM,SSA-BP,and SSA-SVM models.The results show that,the sensitive bands of Sentinel-2 image to conductivity and turbidity are visible light and shortwave infrared;the proposed model of SSA-BP-SVM is more precise with the R² of the inverted conductivity and turbidity being 0.92 and 0.89,respectively;the Yunlong Lake is a typical urban water body with conductivity being greatly affected by the drainage from upstream water treatment plant and turbidity being greatly affected by particulate pollutants from social production activities.The proposed SSA-BP-SVM model has good application potential in water quality inversion from Sentinel-2 image,which can provide technical support for water quality monitoring and protection of Yunlong Lake.