Hydrological time series refers to the observation time point and the observed time value. The simulation and prediction of hydrological time series will greatly improve the predictability of hydrological time series, which is of great significance for hydrological forecasting. TensorFlow, the second generation of artificial intelligence learning system in Google, has been favored by a large number of researchers by virtue of its high flexibility, portability, multi-language support, and performance optimization. However, the application of deep learning in hydrology is less. Based on the TensorFlow framework, the AR model and the LSTM model are constructed in Python language. The hydrological time series is used as the input object, and the model is deeply studied and trained to simulate and predict the hydrological time series. The effect of the model was tested by fitting degrees and other indexes. The fitting degree of the AR model is 0.9551, and the fitting degree of the LSTM model is 0.8012, which shows the feasibility of the model for predicting the hydrological time series, and puts forward the solution for the limitation of the existing analysis results.

In response to the low accuracy exhibited by the Storm Water Management Model (SWMM), we propose an enhanced Differential Evolution and Bayesian Optimization Algorithm (DE-BOA). This algorithm integrates the global search capability of the differential evolution algorithm with the local search capability of the Bayesian optimization algorithm, which enables a more comprehensive exploration of the vector solution space. A comparative analysis of various types of rainfall events is conducted. For model calibration and validation, a drainage subzone in Jinshazhou, Guangzhou City, is selected as the research subject. In total, 20 specific rainfall events are selected, and the DE-BOA algorithm outperforms the manual calibration, the differential evolution algorithm, and the Bayesian optimization algorithm regarding model calibration accuracy. Furthermore, the DE-BOA algorithm exhibits robust adaptability to rainfall events characterized by multiple peaks and higher precipitation levels, with the Nash–Sutcliffe efficiency coefficient values surpassing 0.90. This study’s findings could hold significant reference value for dynamically updating model parameters, thereby enhancing the model simulation performance and improving the accuracy of the urban intelligent water management platform.

This paper aims to gain a better understanding of urban river pollution through evaluation of water quality. Data for 10 parameters at eight sites of the Tongzhou Section of the Beiyun River (TSBR) are analyzed. Hierarchical cluster analysis, fuzzy comprehensive assessment, discriminant analysis and Spearman’s correlation analysis were used to estimate the water situation of each cluster and analyze its spatial-temporal variations. Principal component analysis/factor analysis were applied to extract and recognize the sources responsible for water-quality variations. The results showed that temporal variation is greater than spatial and sewage discharge is the dominant factor of the seasonal distribution. Moreover, during the rapid-flow period, water quality is polluted by a combination of organic matter, phosphorus, bio-chemical pollutants and nitrogen; during the gentle-flow period, water quality is influenced by domestic and industrial waste, the activities of algae, aquatic plants and phosphorus pollution. In regard to future improvement of water quality in TSBR, the control of reclaimed wastewater from adjacent factories should first be put in place, as well as other techniques, for example, an increase of the impervious area, low-impact development, and integrated management practices should also be proposed in managing storm water runoff.

This paper addresses recent developments in the application of water Resource dispatching systems (WRDSs) in China. Through a survey of watershed managers and a literature analysis, it was found that water diversion projects should be the top priority of water resource management by considering the recovery construction of water diversion projects. Case studies of WRDSs in the South-to-North Water Diversion (SNWD) and Pearl River Basin are discussed in this article. The results show that total water consumption management (WCM), water quality monitoring and management (WQMM), minimum discharge flow management (MDFM), and water dispatch management (WDM) modules should be considered in WRDSs. Finally, strategies and needs for resolving water resource management problems are discussed, along with other applications of WRDSs in China.

The flash flood early warning method based on dynamic critical precipitation is proposed, which takes into account the percentage saturation of soil moisture content in double-excess model. A series of historical precipitation data of various gauge stations in the upper catchment of the study area at the early warning cross-section are set as the input parameters, thereby the runoff generation and concentration in the catchment are obtained in the double-excess model, and the percent saturation of soil moisture content is calculated. Based on the warning discharge in combination with the percentage saturation of soil moisture content, the discriminant relations of the critical precipitation for the time intervals, including 0.5 h, 1 h, 1.5 h, 2 h, 2.5 h, and 3 h, are computed respectively using the inversion method. Using the precipitation data from ground rain gauge stations for year x and flood hydrograph data of x typical flood events for the Dayuhe River catchment, the SCE-UA algorithm is adopted to calibrate the parameters of the double-excess model, and the discriminant functions of dynamic critical precipitation for flash flood early warning with 6 time scales are validated using x representative historical flood hydrographs. The qualification ratio for flash flood early warning exceeds x, which demonstrates the feasibility and applicability of the proposed method.