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The water supply in megacities can be affected by the living habits and population mobility, so the fluctuation degree of daily water supply data is acute, which presents a great challenge to the water demand prediction. This is because that non-stationarity of daily data can have a large influence on the generalization ability of models. In this study, the Hodrick-Prescott (HP) and wavelet transform (WT) methods were used to carry out decomposition of daily data to solve the non-stationarity problem. The bidirectional long short term memory (BLSTM), seasonal autoregressive integrated moving average (SARIMA) and Gaussian radial basis function neural network (GRBFNN) were developed to carry out prediction of different subseries. The ensemble learning was introduced to improve the generalization ability of models, and prediction interval was generated based on student's t-test to cope with the variation of water supply laws. This study method was applied to the daily water demand prediction in Shenzhen and cross-validation was performed. The results show that WT is superior to HP decomposition method, but maximum decomposition level of WT should not be set too high, otherwise the trend characteristics of subseries will be weakened. Although the corona virus disease 2019 (COVID-19) outbreak caused a variation in water supply laws, this variation is still within the prediction interval. The WT and coupling models accurately predict water demand and provide the optimal mean square error (0.17%), Nash-Sutcliffe efficiency (97.21%), mean relative error (0.1), mean absolute error (3.32%), and correlation coefficient (0.99). [ABSTRACT FROM AUTHOR]

Since water supply association analysis plays an important role in attribution analysis of water supply fluctuation, how to carry out effective association analysis has become a critical problem. However, the current techniques and methods used for association analysis are not very effective because they are based on continuous data. In general, there is different degrees of monotone relationship between continuous data, which makes the analysis results easily affected by monotone relationship. The multicollinearity between continuous data distorts these analytical methods and may generate incorrect results. Meanwhile, we cannot know the association rules and value interval between features and water supply. Therefore, the lack of an effective analysis method hinders the water supply association analysis. Association rules and value interval of features obtained from association analysis are helpful to grasp cause of water supply fluctuation and know the fluctuation interval of water supply, so as to provide better support for water supply dispatching. But the association rules and value interval between features and water supply are not fully understood. In this study, a data mining method coupling kmeans clustering discretization and apriori algorithm was proposed. The kmeans was used for data discretization to obtain the one-hot encoding that can be recognized by apriori, and the discretization can also avoid the influence of monotone relationship and multicollinearity on analysis results. All the rules eventually need to be validated in order to filter out spurious rules. The results show that the method in this study is an effective association analysis method. The method can not only obtain the valid strong association rules between features and water supply, but also understand whether the association relationship between features and water supply is direct or indirect. Meanwhile, the method can also obtain value interval of features, the association degree between features and confidence probability of rules. [ABSTRACT FROM AUTHOR]

• Continuous sedimentation may lead to the unsustainable development of estuary. • An artificial island scheme was proposed to reduce sediment deposition of Shenzhen Bay. • A 2D numerical model was used to study the hydrodynamics and sediment deposition. • The scheme could make ecological compensation and achieve sustainability in the Bay. Morphodynamics is essential to the sustainable development of estuaries, and plays an importance role in sediment movement, material and energy exchange, and ecosystem health. The divergent morphology of estuary commonly traps sediment and causes sediment deposition, which threats flood control, navigation and ecological environment, and finally leads to the unsustainability of estuarine development. Shenzhen Bay, China is highly anthropogenic influenced waters between Shenzhen and Hong Kong, two densely populated and highly urbanized cities. Due to an expansion in the middle of the Bay, Shenzhen Bay has been experiencing persistent siltation. In order to keep the sustainability of the Bay, an artificial island scheme (AIS) was proposed on the tidal flat of the expansion area, which aimed to fundamentally control or reduce sedimentation by changing the morphology of the Bay, and at the same time, made ecological compensation for habitat loss. The hydrodynamic and sediment transport models were applied to evaluate the impact of the AIS on tidal current, sediment control and siltation. The numerical results indicated that the proposed AIS increased the tidal current around the island, thus reducing the sediment transport into the inner bay and effectively mitigating the sedimentation in the siltation areas. On the other hand, the island was proposed to be designed as an appropriate ecological site to provide multiple functions, such as foraging, nesting, and breeding of estuarine species, especially important migratory birds, to improve health and sustainable development. This idea of estuary governance is also applicable to other estuaries with similar conditions to promote the sustainable management of estuaries. [ABSTRACT FROM AUTHOR]