基于粗集－模糊推理的径流多因素分级预报模型

针对径流多因素分级预报中因子的确定问题,将粗集理论引入到模糊推理多因素分级预报中,建立粗集-模糊推理径流分级预报模型。利用属性约简算法及相对分类精度确定预报因子及分级,以最小决策规则集作为推理规则进行分级预报。并结合大伙房水库年径流预报实例进一步分析因子分级和相对分类精度对预报结果的影响。结果表明,采用粗集理论筛选因子确定推理规则进行模糊推理预报,提高了预报级别合格率。预报结果在提供径流级别的同时,可给出预报区段值,为水库制定年度控制运用计划提供了丰富的参考信息。

A Multi-factor Classified Runoff Forecast Model Based on Rough-fuzzy Inference Method

Rough set theory and the fuzzy inference techniques are integrated into the multi-factor medium and long-term hydrological classification forecast to solve the difficult problem of factors' choice.The concept of relative classification accuracy and the attributes reduction solution are used to choose the best number of classification and the appropriate forecast factors.The minimal decision solution is regarded as the inference rules to forecast the runoff.With the case of Dahuofang reservoir in China,the influence of the classification and relative accuracy on forecast results are analyzed.The results indicate that the model can effectively solve forecast problem related to complex factors selection.The forecast precision is improved with rough set theory and the forecast result presents not only the level of classification but also the interval value of runoff which can offer abundant reference to make annual plan.