Nowadays, urban rivers play an important role in city development and make great contributions to urban ecology. Most urban rivers are the drinking water sources and water quality is extremely critical. The current assessment method in national standard of China has multiple limitations; therefore, this paper introduces an advanced assessment, that is, Canadian Water Quality Index (CWQI). This method can help to provide comprehensive and objective water quality assessment for the urban rivers. Moreover, CWQI can prevent waste of the water resource, since current assessment is pessimistic and tent to underestimate water samples to a lower grade. Linyi development area is selected as study region and CWQI method is applied to assess two major urban rivers within the area. The water monitoring data from 2014 to 2017 is acquired in 24 parameters. Since the CWQI calculation is still based on traditional water quality measurement in parameters, there will be a huge cost when increasing research scale and accuracy. In this paper, remote sensing technique is employed to develop models of CWQI scores from satellite data. By utilizing 23 selected monitoring instances and matching satellite data, linear regression analysis shows that red band data has highest correlation with CWQI in both two urban rivers in the study region. In addition, two testing datasets with five instances for each river are used to validate the RS-based CWQI models and the results show that testing datasets can be fitted well. With the models, CWQI distribution diagrams are generated and assist both spatial and temporal analysis. Experimental results show that the proposed approach can indicate actual water quality pattern which is validated by field visit. The proposed approach in this paper has satisfying effectiveness and robustness.

It is of great significance to evaluate the seawater intrusion degree and predict the change of water quality for coastal groundwater resources. This study takes Dagu River in Jiaodong Peninsula of North China as the target area and combines the relevant theoretical research results to build a seawater intrusion fuzzy analytic hierarchy process (AHP) evaluation model. Five sensitive indicators of water quality, such as Cl, SO, NO, TH, and TDS, were selected to evaluate the seawater intrusion level of the long series monitoring data in Xilaiwan, Guanzhuang, and Ligezhuang of Dagu River Basin by using the basic fuzzy mathematics principles and the improved hierarchical analysis method. In this study, the cubic exponential smoothing method was applied to predict groundwater quality change in Dagu River Basin. In order to evaluate the change of seawater intrusion in detail and make timely prediction, this paper innovatively divided the classification standard of seawater intrusion degree based on relevant norms and scholars' research and predicted the evaluation level of seawater intrusion by using long series historical observation data combined with fuzzy analytic hierarchy process. The cubic exponential smoothing method which has the characteristics of simple and fast was introduced to fit the observation elements, and the historical data were used to verify the prediction of the future development trend. Compared with the evaluation results of seawater intrusion by traditional methods, this study can reflect the whole development trend of seawater intrusion in detail and has the characteristics of more reasonable, accurate, and practical. It also provides a certain reference for the future seawater intrusion prevention. In addition to this case, the method proposed in this study will be applicable to a wider range of coastal zones, providing a new idea for the rational management and control of coastal groundwater resources.© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

Accurate and reliable water quality forecasting is of great significance for water resource optimization and management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in a river system. The accuracy of traditional water quality prediction methods is generally low, and the prediction results have serious autocorrelation. To overcome nonstationarity, randomness, and nonlinearity of the water quality parameter data, an improved least squares support vector machine (LSSVM) model was proposed to improve the model's performance at two gaging stations, namely Panzhihua and Jiujiang, in the Yangtze River, China. In addition, a hybrid model that recruits variational mode decomposition (VMD) to denoise the input data was adopted. A novel metaheuristic optimization algorithm, the sparrow search algorithm (SSA) was also implemented to compute the optimal parameter values for the LSSVM model. To validate the proposed hybrid model, standalone LSSVM, SSA-LSSVM, VMD-LSSVM, support vector regression (SVR), as well as back propagation neural network (BPNN) were considered as the benchmark models. The results indicated that the VMD-SSA-LSSVM model exhibited the best forecasting performance among all the peer models at Panzhihua station. Furthermore, the model forecasting results applied at Jiujiang were consistent with those at Panzhihua station. This result further verified the accuracy and stability of the VMD-SSA-LSSVM model. Thus, the proposed hybrid model was effective method for forecasting nonstationary and nonlinear water quality parameter series and can be recommended as a promising model for water quality parameter forecasting.

水质环境的实时变化和内部耦合导致难以实现水质高效准确的预测。为挖掘水质时间序列中的更多信息,同时提高预测模型的精度,提出一种溶解氧组合预测模型。首先将水质数据去耦合,进行时间序列分解,然后将分解后趋势分量、周期分量和余项分量输入到长短时神经网络模型(LSTM)中进行预测,再针对LSTM网络初始化参数对预测性能的影响提出基于高斯函数的果蝇算法进行优化,最后将各分量的预测值重构为溶解氧浓度的预测值。以海河某3个河流断面的水质数据进行仿真检验,结果表明混合模型对3个站点溶解氧浓度预测效果好,误差小,泛化性强。

水环境中过量的氮、磷和高锰酸盐会对流域造成严重污染,准确预测这三类指标的含量对流域污染治理具有重要意义。然而,现有的模型预测精度低,输入因子的选择缺乏数理依据。基于此,以邕江为研究区域,提出一种RF-BiLSTM的混合网络模型。该模型具有利用RF算法提取水质指标最优特征和利用BiLSTM模型提取输入数据的时间特征的优势,采用先降维后预测的方式对TN、TP和 COD_Mn进行预测,并将深度学习中的CNN、LSTM、BiLSTM和RF-LSTM作为基准模型与本研究所提模型作对比研究。研究结果表明,本研究模型预测TN、TP和COD_Mn的平均绝对百分比误差(MAPE)分别达到了4.330%、6.781%和7.384%,均低于其他基准模型,预测结果具有较高的准确性和实用性,可为水环境的污染治理提供有效的技术支持。