Research on Drought Forecast Based on Rough Set Theory

Liu, Zhao; Qiao, Changlu

doi:10.1109/isise.2009.61

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…For example, Shan (2001) used a rough setbased approach to classify weather data. This theory was introduced for the prediction of drought by Liu and Qiao (2009). In addition, it was applied to assess the pollution sources of the atmospheric particulates of Jilin City both during the heating and non-heating periods (Fang et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Z-number-based AQI in rough set theoretic framework for interpretation of air quality for different thresholds of PM2.5 and PM10

Dutta

Pal

2022

Environ Monit Assess

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Kolkata has a reputation for being one of the world’s most polluted cities, particularly in the post-monsoon months of October, November, and December. Diwali, a Hindu festival, coincides with these months where a large number of firecrackers are set off followed by high emissions of air pollutants. As a result, the air quality index (AQI) deteriorates to “very poor” (301 ≤ AQI ≤ 400) and “poor” (201 ≤ AQI ≤ 300) categories. This situation stays for several days to a month. The present study aims to identify the thresholds for PM 2.5 and PM 10 that cause the AQI of Kolkata to deteriorate to “very poor” and “poor.” For this purpose, we have used a rough set theory-based condition-decision support system to predict the aforementioned categories of AQI. We have developed a Z-number-based novel quantification measure of semantic information of AQI to assess the reliability of the outcomes, as generated from the condition-decision-based decision rules, during post-monsoon season. The result reveals the best possible forecast of AQI with linguistic summarization of the reliability or confidence for different threshold ranges of PM 10 and PM 2.5 . Inverse-decision rules based on rough set theory are utilized to justify and validate the forecasts. The explainability of the condition-decision support system is demonstrated/visualized using a flow graph that maps rough-rule-based different decision paths between input and output with strength, certainty, and coverage. The investigation resulted in an advanced intelligent environmental decision support system (IEDSS) for air-quality prediction.

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Section: Introductionmentioning

confidence: 99%