摘要
地下水污染溯源辨识是指利用监测井的观测数据对污染源信息进行识别。然而,在应用模拟-优化方法进行溯源工作时,多次运行数值模拟模型会带来较大的计算负荷;在利用传统粒子群算法求解优化模型时,易陷入局部极值点,严重影响辨识结果的精度。研究基于假想算例,应用模拟-优化方法,将模拟模型作为等式约束条件,以模拟输出值与实际观测值之间的偏差极小化作为目标函数,连同其他非负约束条件建立优化模型,对三个潜在污染源的释放历史及渗透系数进行了联合识别。通过训练BP神经网络,建立数值模拟模型的替代模型,以缓解沉重的计算负荷;为了避免求解优化模型时陷入局部极值,研究提出了一种自适应权重算法,增强了传统粒子群优化算法跳出局部极值点的能力,识别结果表明:①运用BP神经网络所建立的替代模型能够很好地近似模拟模型的输入-输出关系,拟合精度达到0.99,且运行速度明显快于数值模拟模型,证明了其可以代替数值模拟模型嵌入优化模型中进行污染源溯源辨识工作;②同运用传统粒子群优化算法相比较,运用自适应权重粒子群优化算法,对优化算法的参数和迭代终止条件进行调节,可以有效地提高算法的收敛速度和计算效率,收敛得到的最优解的相对误差基本小于5%。
Inverse identification of groundwater contamination source involves the reconstruction of pollution source features using limited and discrete observation data.However,when the simulation-optimization method is used to perform the identification task,the numerical simulation model must be invoked repeatedly,inevitably leading a large computational load.Moreover,when optimization model is solved,traditional particle swarm optimization algorithm commonly falls into the local minimum,which hinders precise identification.In the present study,a hypothesis case is designed to evaluate the performance of proposed framework.The numeric simulation model is embedded as equation constraint of optimization model where the objective function is the bias between simulation outputs and observations and the decision variables denotes features of contamination source.In particular,the optimization model is established to simultaneously estimate the release history of three potential contamination sources and hydraulic conductivity.To reduce the huge calculated burden,the BP neural network is introduced to substitute the simulation model.Furthermore,to alleviate being trapped into local minimum,adaptive weighted strategy is proposed to improve the particle swarm algorithm.The identification results indicate that:①The BP neural network surrogate model can approximate the input-output relationship of the simulation model with desired accuracy of R square of 0.99,and the running speed of surrogate is evidently swifter than that of the numerical simulation model.②Compared with the traditional particle swarm optimization algorithm,the adaptive weighted particle swarm optimization algorithm can substantially improve the convergence speed and optimal efficiency by adjusting the parameters and iteration termination conditions of the optimization algorithm,and the relative error of the optimal solution is less than 5%.
作者
高琬玉
卢文喜
潘紫东
白玉堃
GAO Wan-yu;LU Wen-xi;PAN Zi-dong;BAI Yu-kun(Key Laboratory of Groundwater Resources and Environment Ministry of Education,Jilin University,Changchun 130012,Jilin Province,China;College of Environment and Resources,Jilin University,Changchun 130012,Jilin Province,China)
出处
《中国农村水利水电》
北大核心
2022年第12期1-7,16,共8页
China Rural Water and Hydropower
基金
国家自然科学基金资助项目(41672232)。
关键词
污染源溯源辨识
模拟-优化方法
替代模型
自适应权重粒子群算法
BP神经网络方法
contamination sources identification
simulation-optimization
surrogate model
adaptive weighted particle swarm optimization algorithm
BP neural network
