The interpretability of AI is just as important as its performance. In the LegalAI field, there have been efforts to enhance the interpretability of models, but a trade-off between interpretability and prediction accuracy remains inevitable. In this paper, we introduce a novel framework called LK-IB for compulsory measure prediction (CMP), one of the critical tasks in LegalAI. LK-IB leverages Legal Knowledge and combines an Interpretable model and a Black-box model to balance interpretability and prediction performance. Specifically, LK-IB involves three steps: (...) (1) inputting cases into the first module, where first-order logic (FOL) rules are used to make predictions and output them directly if possible; (2) sending cases to the second module if FOL rules are not applicable, where a case distributor categorizes them as either “simple” or “complex“; and (3) sending simple cases to an interpretable model with strong interpretability and complex cases to a black-box model with outstanding performance. Experimental results demonstrate that the LK-IB framework provides more interpretable and accurate predictions than other state-of-the-art models. Given that the majority of cases in LegalAI are simple, the idea of model combination has significant potential for practical applications. (shrink)

The Biliu River originates from the southern foot of Qinling Mountain in Gaizhou city, with an elevation of 1047 m, and is the largest river in Dalian. The hydrological elements mainly include rainfall, runoff, temperature, evaporation, and other time series associated with the hydrological cycle. Among them, runoff is the most visible output performance, and the direct source of runoff is during rainfall. This paper establishes a reservoir scheduling model that considers the influence of multiple uncertainty factors and analyzes the (...) influence of mixed uncertainty on reservoir scheduling and Xingli’s objectives based on probability box theory. In terms of uncertainties, the uncertainty of hydrological model parameters and the randomness of precipitation processes are mainly considered, with the former having an impact on river runoff simulation and the latter having an impact on both river runoff simulation and crop irrigation water demand. In the case of the Jing River basin, for example, the results show that, compared to the stochasticity of the precipitation process, the variation in precipitation has a significant effect on irrigation water demand in maize, followed by the frequency of precipitation, and the interaction between the two is not significant. (shrink)

The Biliu River is the largest river in Dalian. The occurrence of floods and droughts in this basin has extremely important impacts on local industry, agriculture, and urban development. For a long time, the annual distribution of precipitation in the Biliu River Basin is extremely uneven, the river runoff varies greatly from year to year and season to year, floods and droughts occur frequently, and serious soil erosion results in fragile ecological environment and severe shortage of water resources. In this (...) paper, the spatial and temporal changes of rainfall and runoff in the Biliu River Basin are studied through the coupling of the MIKE 11 model and the MIKE SHE model. The hydrological changes in the Biliu River Basin are simulated. The coupled model is verified by monthly runoff data from 1996 to 2015, and the simulation values are found to be true. The values match well. Based on the cyclical pattern of precipitation and runoff in the Biliu River Basin, the rainfall and runoff data in the Biliu River Basin from 2016 to 2030 are derived. The MIKE SHE/MIKE 11 coupling model is used to predict the Biliu River from 2016 to 2030. The results show that flood disasters are expected to occur in August 2020, July 2025, and July 2030, which can provide a basis for hydrological management in the Biliu River Basin. (shrink)