School of Mathematics and Information Science & Technology, Hebei Normal University of Science & Technology, Key Laboratory of Ocean Dynamics and Resources and Environments, Hebei Agricultural Data Intelligent Perception and Application Technology Innovation Center, Qinhuangdao 066000, Hebei, China.
School of Business Administration, Hebei Normal University of Science & Technology, Qinhuangdao 066000, China.
Comput Intell Neurosci. 2022 Aug 3;2022:1264385. doi: 10.1155/2022/1264385. eCollection 2022.
Water pollution threatens the safety of human production and life. To quickly respond to water pollution, it is important for water management staff to predict water quality changes in advance. Drawing on the temporality of water quality data, the leaky integrator echo state network (ESN) was introduced to construct the water quality prediction models for dissolved oxygen (DO), permanganate index (CODMn), and total phosphorus (TP), respectively. First, the missing values were filled by the linear trend method of adjacent points, and the outliers were detected and corrected by the -score method and the linear trend method. Second, the singular spectrum analysis (SSA) was performed to denoise the original monitoring data, such that the predicted data catch up with the real data, and the model accuracy is not affected by the hidden noise in the data. Third, the correlation between water quality indices was measured by the maximum information coefficient (MIC), and the strongly correlated indices were imported to the prediction model. Finally, according to these strong correlation indicators, the water quality prediction models based on multiple features were constructed, respectively, using the offline and online learning algorithms of the ESN. The hyperparameters of the models were optimized through the sequential model-based optimization (SMBO). Experimental results show that the proposed water quality prediction models, namely, SSA-MIC-SMBO-Offline ESN and SSA-MIC-SMBO-Online ESN, predicted DO, CODMn, and TP accurately, providing suitable tools for practical applications.
水污染威胁着人类生产和生活的安全。为了快速应对水污染，水务管理人员提前预测水质变化非常重要。借鉴水质数据的时间特性，引入了渗漏积分器回声状态网络（ESN），分别构建溶解氧（DO）、高锰酸盐指数（CODMn）和总磷（TP）的水质预测模型。首先，采用相邻点的线性趋势法填充缺失值，并用 -score 法和线性趋势法检测和修正异常值。其次，进行奇异谱分析（SSA）以对原始监测数据进行去噪，使预测数据能够跟上实际数据，并且模型精度不受数据中隐藏噪声的影响。然后，通过最大信息系数（MIC）测量水质指标之间的相关性，并将强相关指标导入预测模型。最后，根据这些强相关指标，分别使用 ESN 的离线和在线学习算法构建基于多特征的水质预测模型。通过序贯模型优化（SMBO）优化模型的超参数。实验结果表明，所提出的水质预测模型，即 SSA-MIC-SMBO-Offline ESN 和 SSA-MIC-SMBO-Online ESN，准确地预测了 DO、CODMn 和 TP，为实际应用提供了合适的工具。
