College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China.
College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006, China.
Environ Sci Pollut Res Int. 2019 Oct;26(29):30374-30385. doi: 10.1007/s11356-019-06049-2. Epub 2019 Aug 22.
Due to increasingly serious deterioration of surface water quality, effective water quality prediction technique for real-time early warning is essential to guarantee the emergency response ability in advance for sustainable water management. In this study, an effective data-driven model for surface water quality prediction is developed to analyze the inherent water quality variation tendencies and provide real-time early warnings according to the historical observation data. The developed data-driven model is integrated by an improved genetic algorithm (IGA) for selecting optimal initial weight parameters of neural a network and a back-propagation neural network (BPNN) for adjusting appropriate connection architectures of neural network. First, improved genetic algorithm is used to optimize the reasonable initial weight parameters and prevent the developed model from selecting a local optimal result. Second, BPNN is applied to adjust appropriate connection architectures and identify the features of water quality variation. The developed model is then applied to forecast the surface water quality variations for real-time early warning in Ashi River, China, comparing with simple BPNN model. The prediction results demonstrate that the developed data-driven model can significantly improve the prediction performance both in prediction accuracy and reliability, and effectively provide real-time early warning for emergency response.
由于地表水水质日益恶化，为了保证可持续水资源管理的提前应急响应能力，开发有效的水质实时预警预测技术至关重要。在本研究中，开发了一种有效的基于数据驱动的地表水质量预测模型，以根据历史观测数据分析内在的水质变化趋势，并提供实时预警。所开发的数据驱动模型由改进的遗传算法（IGA）集成，用于选择神经网络的最优初始权重参数，以及反向传播神经网络（BPNN），用于调整神经网络的合适连接结构。首先，改进的遗传算法用于优化合理的初始权重参数，防止开发的模型选择局部最优结果。其次，BPNN 用于调整合适的连接结构，并识别水质变化的特征。然后，将所开发的模型应用于中国的阿什河进行地表水质量变化的实时预警预测，并与简单的 BPNN 模型进行比较。预测结果表明，所开发的数据驱动模型可以显著提高预测性能，无论是在预测精度还是可靠性方面，都能有效提供应急响应的实时预警。
