Urban stormwater has become a persistent concern on a global scale due to its adverse environmental implications. It is the prime vector of aquatic contaminants worldwide that causes pollutants when water bodies drain. Bioretention systems are increasingly used to alleviate setbacks associated with stormwater run-off in urban locales. It has played a substantial role in the implementation of low impact development (LID), a concept that addresses urban stormwater problems caused by land changes and development. The use of LID technologies is an innovative approach. However, it is beset with challenges, such as the insufficiency of data on rainfall distribution and difficulty in interpreting data. To address these research gaps, the present study developed a fuzzy rough set data algorithm for bioretention systems. Event mean concentration calculations and fuzzification of rainfall were performed to produce a rough set-based decision rule. Using the Weibull probability distribution, fuzzification of rainfall and parameter data, rule induction, and Preece testing, bioretention design considerations were determined. The bioretention characterizations generated evident pollutants present in the catch basin before and after filtration. In addition, the bioretention characterization conducted in this study was able to reduce the number of tests needed for rainfall identification based on the different attributes.

Bioretention systems, one of the types of Low Impact Development (LID) best management practices are dependent on localized climate and catchment characteristics. Frequency and patterns of the daily rainfall data from 3,337 storm events from 1990-2020 were described using Weibull probability distribution. In this study, the rainfall depth of storm events that influenced the design of bioretention systems were analyzed using Weibull distribution and fuzzy logic. These lead to a better understanding of the trends in annual and daily precipitation that affects bioretention rainfall events used for the computation of water quality volume (WQV). Furthermore, the fuzzified data may be used for studies on rough set-based data models for bioretention cells. The result showed that a fuzzy value of 11 to 18 prevailed in all bioretention rainfall events. Identifying the fuzzy value of 18 as the 90 ^th percentile of cumulative rainfall establishes it as a critical classifier to be used in modelling and designing LID technologies that can effectively capture and store most of the annual runoff and efficiently treat pollutant load.