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.hydrological and hydro-chemical characteristic when compared to natural river courses. Studies on pollution status and distribution in urban river courses may serve as a guidance for water quality improvement and water safety insurance in urban areas.Spatial and temporal variations of river water condition have always been a hotspot for research on water environments. While most studies focus on natural river courses or lakes, there is still lack of research on highly artificialized urban river courses, which always occur on a small scale. V. Simeonov et al. focus on a three-year survey conducted in the major river systems (Aliakmon, Axios, Gallikos, Loudias and Strymon) as well as streams, tributaries and ditches in Northern Greece, using a multivariate receptor model to estimate the contribution of identified sources to the concentration of the physicochemical parameters [6]. Research on marine water quality in Eastern Hong Kong found that water quality in 12 months could be grouped into two groups, June-September and the remaining months, and the entire area could be divided into two parts, representing different pollution levels. Pattern recognition provided better information for Hong Kong offshore water-quality monitoring and function division, in the meantime allowed the identification of possible factors that influenced the water systems [10]. Pejman et al. [11] used multivariate statistical techniques to evaluate spatial and seasonal variations of water quality in the Haraz River Basin, providing a representative and reliable estimation to get more information about water quality and design monitoring network.Multivariate mathematical and statistical techniques ser...

Sudden floods in the medium and small watershed by a sudden rainstorm and locally heavy rainfall often lead to flash floods. Therefore, it is of practical and theoretical significance to explore appropriate flood forecasting model for medium and small watersheds for flood control and disaster reduction in the loess region under the condition of underlying surface changes. This paper took the Gedong basin in the loess region of western Shanxi as the research area, analyzing the underlying surface and floods characteristics. The underlying surface change was divided into three periods (HSP1, HSP2, HSP3), and the floods were divided into three grades (great, moderate, small). The paper applied K Nearest Neighbor method and Fireworks Algorithm to improve the Extreme Learning Machine model (KNN-FWA-ELM) and proposed KNN-FWA-ELM hybrid flood forecasting model, which was further applied to flood forecasting of different underlying surface conditions and flood grades. Results demonstrated that KNN-FWA-ELM model had better simulation performance and higher simulation accuracy than the ELM model for flood forecasting, and the qualified rate was 17.39% higher than the ELM model. KNN-FWA-ELM model was superior to the ELM model in three periods and the simulation performance of three flood grades, and the simulation performance of KNN-FWA-ELM model was better in HSP1 stage floods and great floods.

The water resources carrying capacity (WRCC) shows remarkable fuzziness and randomness, which causes the uncertainty and instability of the WRCC assessment (WRCCA). In order to solve these problems, we proposed a novel hybrid approach for WRCCA, in which the fuzzy comprehensive evaluation (FCE) and analytical hierarchy process (AHP) methods were integrated with the cloud model (CM). Firstly, an evaluation indicator system of WRCC was constructed. Secondly, the AHP and FCE methods were subsequently improved with the CM. The CM was used to scale the relative importance and aggregate the judgment matrices, where the weights of the clouds were obtained. These integrations of AHP and CM greatly reduced the randomness in the weight calculation; the CM was used to describe the comment sets, calculate the membership degree matrices and determine the assignment clouds, the evaluation sets and the WRCCA index clouds were obtained. These integrations of FCE and CM effectively blurred the boundary fuzziness and gave more intuitive results. Finally, the hybrid FCE-AHP-CM approach was applied to a case study. It was concluded that the novel approach has particular advantages in dealing with the fuzziness and randomness comprehensively, and therefore could assess the WRCC and enhance the robustness and intuition of WRCCA results.

Establishing a model predicting river flow can effectively reduce huge losses caused by floods. This paper proposes a multi-step time series forecasting model based on multiple input and multiple output strategies, and this model is applied to the flood forecasting process of a river basin in Shanxi, which effectively improves the engineering application value of the flood forecasting model based on deep learning. The experimental results show that after considering the seasonal characteristics of the river channel and screening the influencing factors, a simple neural network model can accurately predict the peak value, the peak time and flood trends. On this basis, we proposed the MSBP (Multi-step Back Propagation) model, which can accurately predict the flow trend of the river basin 20 h in advance, and the NSE (Nash Efficiency) is 0.89. The MSBP model can improve the reliability of flood forecasting and increase the internal interpretability of the model, which is of great significance for effectively improving the effect of flood forecasting.

Abstract:Basins located in loess hilly-gully regions often suffer flood disasters during the flood season. Meanwhile, the underlying surface of the region can increase the rainfall losses, thereby reducing the flood volume. Therefore, the prediction of rainfall losses on the underlying surface is necessary for scientifically and reasonably forecasting the flood volume. The relationship between the rainfall losses and underlying characteristics was investigated and a method for predicting the rainfall losses using HEC-HMS was presented in this paper with a case study in the Gedong basin, a typical loess hilly region of western Shanxi Province in northern China. Results showed that HEC-HMS could be applied to loess hilly-gully regions. The loss computation results suggested that the losses of sub-basins varied with the density of rainfall. The analysis of influences of rainfall losses, including forestland percentage and slope, indicated that the former had a positive impact, while the latter had a negative influence. The impact of forestland percentage is larger than that of slope. Furthermore, with the increase of forestland percentage, its correlation with rainfall losses was enhanced, and the correlation coefficient ranged between 0.64 and 0.84 from the 1970s to the 2010s.