Decision Support System Using the Expert-Fuzzy Method in Validating Real-Time Acid Rain Measurements

Abstract The measurement of acid rain is a very important instrument for evaluating the environmental effects of air pollution on ecosystems. However, the existing manual methods use few parameters, leading to inefficacy and incomplete data for suitable decision-making. A new system for real-time information is proposed in this study, which employs the Expert-Fuzzy method within a Decision Support System (DSS) that enhances the precision and consistency of acid rain measurements. Such improvements include individual sensor range detection, outlier identification, dynamic windowing, and averaging from their integration into Raspberry Pi, an advanced statistical technique. These enhancements improve confidence intervals and precision in the real-time validation of data. From May 7 until June 7 2023, field testing was taking place at two locations (-6.9729594 107.6274528; − 6.969282, 107.6255821) where key parameters like pH, temperature, and conductivity, among others, were being monitored to ensure a confidence level of approximately 95%. The little discrepancies between raw data and validated allude to this system’s precision since after validation, pH readings were 5.99, conductivity measured 14.47 ms/cm, and temperatures showed 25.01℃. The more support for the stability of these measurements from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, which traces back the sources of possible neutralization pollutants. This research shows that acid rain classification and decision-making can be enhanced by combining expert knowledge with fuzzy logic. Using the Expert-Fuzzy method, environmental monitoring measurements can be made more precise.