Abstract:In this study, calcareous coral sand samples from four stations on an island in the South China Sea were measured by common methods. Based on test parameters, numerical simulations of freshwater storage capacity were conducted by FEMWATER. The result indicates that the surface of the dredged calcareous coral sand in the South China Sea is mainly composed of medium and fine grains, and the porosity is mostly between 0. 35 and 0. 51. Meanwhile, the permeability coefficient and specific yield vary widely in different place, from 0. 023-12. 73 m/d and 0. 01-0. 10, respectively. There is a close relationship between the thickness and resource storage of fresh water lens and the hydrological parameters of calcareous coral sand. Among these parameters, the specific yield has the greatest influence on the resource storage of freshwater lens on the coral islands. Compared with the natural calcareous coral sand of Xisha Islands, the calcareous coral sand of this study is finer in size and particles are mostly flaky with poor roundness, which reflects the differences in hydrodynamic conditions and reef-building organisms.

Abstract:In order to eliminate the impact of emergency and spatial variability of urban waterlogging events on causal analysis, a framework for constructing the event logic graph and analyzing the causes of waterlogging based on the graph was proposed in this study. The rule template library was used to extract sentences containing causal events in Chinese urban waterlogging corpus, the events in the causal sentences were extracted based on the deep neural network fusion method with voting mechanism, and after that manual rules were combined to construct the event logic graph for urban waterlogging. Then, the event logic graph was used to generate scenes centered on the waterlogging point, which was further exploited to automatically generate and train the discrete dynamic Bayesian network. Finally, this study performed causal analysis based on this network. The result shows that the event logic graph for urban waterlogging well represents the mechanism of urban waterlogging evolution. In addition, the comparison of inferred results and real results shows that this method can accurately find the causes and eliminate the influences of pseudo-positive causes.

Abstract:In order to improve the prediction accuracy of river flow, the support vector machine(SVM)and the AR model are coupled to construct the SVM model for the river flow prediction with three-core hybrid kernel function. Taking the monthly runoff in the Weihe River Basin as an example, firstly through the time series analysis, the runoff data of Weihe River Basin was divided into trend data, seasonal data and random fluctuation data. A data set suitable for support vector machine algorithms was constructed with the AR model, and then was divided into the training set and the test set by 4∶1. Secondly, with the linear combination, this study constructed a three-core hybrid kernel function composed of polynomial kernel function, radial basis kernel function and Sigmoid kernel function. On the training set, the genetic algorithm was used to determine the relevant parameters, and predictions were conducted on the test set. It was found that when the genetic algorithm is used to determine the parameters, it will bring greater uncertainty with greater differences in results, thus it need more discussions on the parameter uncertainty brought by genetic algorithm. Through the function construction and statistical analysis, the general method and process of parameter selection of the three-core hybrid kernel function are given and verified. With this method, the uncertainty of the genetic algorithm can be reduced on the test set, and a more accurate flow prediction result can be achieved. The mean square error between the predicted flow rate and the actual flow rate is reduced from about 150 to about 130.

Abstract:In order to make full use of the accumulated monitoring data in the field of flood control security, accurately understand the situation of flood control security and form a scientific comprehensive decision, the construction and visualization methods of situation map for the flood control security of river basin was proposed. From characteristics, relationships and behaviors of spatial-temporal objects, based on the ontological method of situational theory, this study clarified the concept of relevant situational awareness to built up an indicator system of flood control situation and put forward the trend graph modeling and visualization methods, for the flood control security of river basin. Finally, the proposed methods were applied in the Danjiangkou Reservoir Basin, which has enlightenment and reference significance in the flood control security.

Abstract:In order to explore the eutrophication evolution mechanism and identify the key factors of water environment that affect the eutrophication and cyanobacteria growth of Taihu Lake, the data preparation and data cleaning for the data of the multi-source monitoring sequence of Taihu Lake from 2006 to 2018 were carried out. The K-means clustering method was used to obtain the discrete Boolean association rule for the mining of candidate data sets, and a mining model of association rule for key factors of Taihu Lake was constructed based on the Apriori algorithm, from which the key factors of water environment that affect the eutrophication of Taihu Lake were identified. The results showed that the mass concentration of chlorophyll a, which characterizes the degree of eutrophication in Taihu Lake, has different degrees of correlation with total phosphorus, ammonia nitrogen, pH and permanganate index. Among them, the mass concentration of chlorophyll a in the range of 0-18. 36 mg/m³ has the strongest correlation with the total phosphorus in the range of 0-0. 045 mg/L. From the perspective of water environment management, if the total phosphorus concentration in Taihu Lake is controlled below 0. 045 mg/L, the probability that the mass concentration of chlorophyll a in the whole lake is below 18. 36 mg/m³ would be the highest, which can effectively control the number of cyanobacteria in the overall state of less and further avoid the large-scale outbreak of cyanobacteria bloom in Taihu Lake.

Abstract:By representing hydraulic events via constructing multiple features of the watershed spatio-temporal scene, this study proposed a construction method of watershed scene pattern library via the spatio-temporal multiple features. The original hydrological data was firstly divided into events to remove the spatio-temporal redundancy of scene element data. Based on the analysis of element association relation, the corresponding features of scene elements were constructed via multiple ways. Afterwards, key features of watershed scene were selected by the feature selection algorithm to realize the scene initialization. Finally, the initial scene was regarded as the feature space, where the cluster extraction of scene pattern and scene pattern library construction could be carried out. Experimental results show that the proposed method can not only extract the key spatio-temporal scene data of hydrological events, but also mine scene patterns to form a scene pattern library, thus providing accurate and efficient prediction results for the hydrological event with small dataset.

Abstract:Focusing on the low accuracy and insufficient foreseen period of traditional runoff forecast, this study proposed a runoff forecast method based on the similarity of rainfall and runoff. Data mining was used to search for the similar historical rainfall and runoff process, and the most likely runoff hydrograph in the later period was predicted. To prolong the runoff foreseen period to seven days, the real-time rainfall forecast information was inputted into the model and three rolling forecast schemes were proposed. The forecast models could be adaptively switched according to real-time rainfall conditions to further improve the forecast accuracy. The application in Dadu River showed that the Nash coefficients of forecasting the third day and seventh day were greater than 0. 9 and 0. 8, and the average relative errors were less than 10% and 15%, respectively. The research is of great significance to improve the forecast accuracy, extend the foreseen period, and promote the management and operation level of the reservoir group.

Abstract:Aiming at the problem that two-dimensional hydrodynamic model is too time-consuming to be applied in a large-scale area or case with fine resolution for the urban flood simulation, an urban inundation model was constructed by coupling the SWMM model and the LISFLOOD-FP model. Then the GPU-based parallel computing technology was adopted to accelerate the constructed inundation model. Taking the flood simulation of urban area in Xiangshui County of Yancheng City as an example, the efficiency of the parallelized algorithm was analyzed. The results show that the GPU-based parallel computing technology can significantly improve the model efficiency, and the parallelized model could simulate a 12-hour flood event in 8 minutes at 5 m resolution, which can be used for quick response to urban flood emergencies. The efficiency of the parallelized algorithm was more obvious at a higher spatial resolution, and the highest speedup of 10. 86 times was achieved at 2 m resolution. In order to maximize the computing efficiency of GPU, a large amount of computation is required in each time step, and the additional time caused by frequent data transmission between host and GPU should be minimized.

Abstract:This study analyzes the spatiotemporal pattern of major indicators of urban WUE. It also explores the synchronization between WUE and water-saving degree, as well as the matching degree between WUE, water pressure and economic development level. The results show that, the water consumption per 10 000 yuan of industrial added value decreased by 80% in 2017 compared with that in 1998 in China, with less spatial discrepancy in regions. Domestic water consumption per capita increases by 30% during the same period, and is slightly better than the median of the similar economic and social development countries. By 2017, the reuse rate of industrial water in half cities increases to 80%, and the average of the leakage rate of water supply pipe networks is about 15%. There are still problems of unbalanced spatial matching either between WUE and water pressure, or between WUE and economic development level. To the east of the “Heihe-Tengchong Line”, the central China and southeast China are with abundant water resources, large population, well-developed economy, high water pressure but low WUE, and still have great water-saving potential.

Abstract:Based on atmospheric-hydrological data and satellite remote sensing data from 2014 to 2018, the support vector machine(SVM), long short-term memory model(LSTM), extreme gradient boosting(XGBoost)model were applied to predict the cyanobacterial bloom area in fields including the whole region, Gonghu Bay, southern coastal region and central north-western region of Taihu Lake. The results demonstrated that the XGBoost regression model had better accuracy than SVM and LSTM regression model in the whole region and subdivided regions. Compared with the observed cyanobacterial bloom area, simulated areas of SVM regression model and XGBoost regression model were lower in the Taihu Lake under different time scales, while the development tendency of cyanobacterial blooms was effectively simulated. In addition, the XGBoost classification model had better accuracy than SVM and LSTM classification model for the whole region and the central north-western region of Taihu Lake. Three classification models had high accuracy in the Gonghu Bay and the southern coastal region of Taihu Lake. Finally, taking the atmospheric-hydrological data and water quality data of the same day and one day advanced as model inputs, the XGBoost regression model has high accuracy and robustness in cyanobacterial bloom area simulation, which had a promising application prospect for the cyanobacterial bloom prediction.

Abstract:Two-dimensional physical model tests are designed to study the soil motion characteristics around offshore windfarm monopile foundations. Periodical loading induced by wind, wave, current, and rotor motion in the field is simplified to lateral cyclic loading exerted on the top of monopile models in the tests. PIV and PTV technology were used to measure the motion process of vibrating monopile and its surrounding soil. The properties of the soil subsidence and convective motion around the monopiles under different test conditions were obtained. The results show that each characteristic scale of soil deformation approximately has a linear relation with the loading frequency and displacement amplitude of pile-top. The characteristic scale of soil deformation increases with the increase of the loading frequency and the displacement amplitude. At the quasi-equilibrium stage, the depth and width ratios of the subsided hole and convection area are 0. 24-0. 34 and 1. 47-2. 34, respectively. The convective motion of soil is driven by the frictional shear stress generated by the monopile-soil interaction. The velocity of the sand convection motions is around 10^-4 cm/s, which is generally three orders of magnitude smaller than the vibration velocity of monopile.

Abstract:Based on the images of pollution mass continuously collected in the breaking zone, this paper studied the transport and diffusivity of pollutant in the surf zone under the oblique incidence of regular waves on the plane beach. Firstly, the experiment and image processing methods were introduced, and the centroid point and dispersion degree of the pollution mass were obtained. The transport velocity of pollution mass in the longshore direction and the cross-shore direction were obtained by following the change of centroid point in horizontal two-dimensional space and the alignment fitting. Then the relationship between the transport velocity in the longshore direction and the maximum time-averaged current velocity in the long-shore direction were analyzed. The cross-shore diffusion coefficient was estimated by assuming a Gaussian diffusion process in this study, and five horizontal transport velocities of pollution mass and five cross-shore diffusion coefficients were obtained and analyzed. The results show that the alongshore transport velocity of pollution mass is about 33% of the maximum time-averaged current velocity in the long-shore direction, and the transport velocity as well as the diffusion coefficient in the cross-shore direction is respectively within the range of 0. 008-0. 03 m/s and 0. 16×10^-3-2. 6×10^-3 m²/s.