Multi-Objective Optimal Allocation of River Basin Water Resources under Full Probability Scenarios Considering Wet–Dry Encounters: A Case Study of Yellow River Basin

Wet–dry encounters between basins and regions have an important impact on the allocation of water resources. This study proposes a multi-objective allocation model for basin water resources under full probability scenarios considering wet–dry encounters (FPS-MOWAM) to solve the problem of basin water resource allocation. In the FPS-MOWAM model, the sub-regions were merged by precipitation correlation analysis. Next, the joint probability distribution of basin runoff and region precipitation was constructed using copula functions. The possible wet–dry encounter scenarios and their probabilities were then acquired. Finally, the multi-objective allocation model of water resources was constructed using the full probability scenario for wet–dry encounters in each region. The FPS-MOWAM is calculated by the NSGA-II algorithm and the optimal water resource allocation scheme was selected using the fuzzy comprehensive evaluation method. Using the Yellow River Basin as an example, the following conclusions were obtained: (1) the Yellow River Basin can be divided into four sub-regions based on precipitation correlations: Qh-Sc (Qinghai, Sichuan), Sg-Nx-Nmg (Gansu, Ningxia, Inner Mongolia), Sxq-Sxj (Shaanxi, Shanxi), and Hn-Sd (Henan, Shandong), (2) the inconsistencies in synchronous–asynchronous encounter probabilities in the Yellow River Basin were significant (the asynchronous probabilities were 0.763), whereas the asynchronous probabilities among the four regions were 0.632, 0.932, and 0.763 under the high, medium, and low flow conditions in the Yellow River Basin respectively, and (3) the allocation of water resources tends to increase with time, allocating the most during dry years. In 2035, the expected economic benefits are between 11,982.7 billion CNY and 12,499.6 billion CNY, while the expected water shortage rate is between 2.02% and 3.43%. In 2050, the expected economic benefits are between 21,291.4 billion CNY and 21,781.3 billion CNY, while the expected water shortage rate is between 1.28% and 6.05%

Flood Risk Analysis of Different Climatic Phenomena during Flood Season Based on Copula-Based Bayesian Network Method: A Case Study of Taihu Basin, China

We propose a flood risk management model for the Taihu Basin, China, that considers the spatial and temporal differences of flood risk caused by the different climatic phenomena. In terms of time, the probability distribution of climatic phenomenon occurrence time was used to divide the flood season into plum rain and the typhoon periods. In terms of space, the Taihu Basin was divided into different sub-regions by the Copula functions. Finally, we constructed a flood risk management model using the Copula-based Bayesian network to analyze the flood risk. The results showed the plum rain period occurs from June 24 to July 21 and the typhoon period from July 22 to September 22. Considering the joint distribution of sub-region precipitation and the water level of Taihu Lake, we divided the Taihu Basin into three sub-regions (P-I, P-II, and P-III) for risk analysis in the plum rain period. However, the Taihu Basin was used as a whole for flood risk analysis in the typhoon period. Risk analysis indicated a probability of 2.4%, and 0.8%, respectively, for future adverse drainage during the plum rain period and the typhoon period, the flood risk increases rapidly with the rising water level in the Taihu Lake

Embedding differential privacy in decision tree algorithm with different depths

Differential privacy (DP) has become one of the most important solutions for privacy protection in recent years. Previous studies have shown that prediction accuracy usually increases as more data mining (DM) logic is considered in the DP implementation. However, although one-step DM computation for decision tree (DT) model has been investigated, existing research has not studied the scenarios when the DP is embedded in two-step DM computation, three-step DM computation until the whole model DM computation. It is very challenging to embed DP in more than two steps of DM computation since the solution space exponentially increases with the increase of computational complexity. In this work, we propose algorithms by making use of Markov Chain Monte Carlo (MCMC) method, which can efficiently search a computationally infeasible space to embed DP into DT generation algorithm. We compare the performance when embedding DP in DT with different depths, i.e., one-step DM computation (previous work), two-step, three-step and the whole model. We find that the deep combination of DP and DT does help to increase the prediction accuracy. However, when the privacy budget is very large (e.g., ϵ = 10), this may overwhelm the complexity of DT model, and the increasing trend is not obvious. We also find that the prediction accuracy decreases with the increase of model complexity