Data mining has a significant role in hyrdrologic research. Among several methods of data mining, Bayesian network theory has great importance and wide applications as well. The drought indices are very useful tools for drought monitoring and forecasting. However, the multi-scaling nature of standardized type drought indices creates several problems in data analysis and reanalysis at regional level. This paper presents a novel framework of data mining for hydrological research—the Bayesian Integrated Regional Drought Time Scale (BIRDts). The mechanism of BIRDts gives effective and sufficient time scales by considering dependency/interdependency probabilities from Bayesian network algorithm. The resultant time scales are proposed for further investigation and research related to the hydrological process. Application of the proposed method consists of 46 meteorological stations of Pakistan. In this research, we have employed Standardized Precipitation Temperature Index (SPTI) drought index for 1-, 3-, 6-, 9-, 12-, 24-, and ()month time scales. Outcomes associated with this research show that the proposed method has rationale to aggregate time scales at regional level by configuring marginal posterior probability as weights in the selection process of effective drought time scales.

数据挖掘在水文研究中具有重要作用。在多种数据挖掘方法中，贝叶斯网络理论具有重要的意义和广泛的应用前景。干旱指数是用于干旱监测和预报的非常有用的工具。但是，标准化干旱指数的多尺度性质在区域层面的数据分析和再分析中产生了一些问题。本文提出了一种用于水文学研究的新型数据挖掘框架-贝叶斯综合区域干旱时间量表（BIRDts）。BIRDts的机制通过考虑来自贝叶斯网络算法的依赖性/相互依赖性概率，给出了有效且足够的时间尺度。提出的时间尺度将用于与水文过程有关的进一步调查和研究。该方法的应用包括巴基斯坦的46个气象站。在这项研究中，我们采用了1、3、6、9、12、24和24个月时间尺度的标准化降水温度指数（SPTI）干旱指数。与这项研究相关的结果表明，通过在有效干旱时间尺度的选择过程中将边际后验概率配置为权重，该方法具有在区域水平上汇总时间尺度的原理。