Evaluating polynomial and Gaussian fitting techniques for accurate sub-index pH modeling in water quality index development
Abstract This study proposes and evaluates curve-fitting models—specifically polynomial and Gaussian techniques—to accurately compute the pH sub-index as part of a Water Quality Index (WQI). This modeling is essential for enhancing the reliability of pH representation in real-time Internet of Things...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
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| Series: | Discover Water |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43832-025-00246-6 |
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| Summary: | Abstract This study proposes and evaluates curve-fitting models—specifically polynomial and Gaussian techniques—to accurately compute the pH sub-index as part of a Water Quality Index (WQI). This modeling is essential for enhancing the reliability of pH representation in real-time Internet of Things (IoT)-based water quality monitoring systems. By improving the pH sub-index formulation, the research aims to address one of the core components in WQI computations under dynamic environmental conditions. This work represents a critical step in the second phase of developing the IoT-based Water Quality Index (IoTWQI) framework. The resulting models are validated and compared for their suitability in sensor-based environments. The findings highlight the Gaussian Model 1 as the most accurate choice for normally distributed data (RMSE = 2.15), whereas the 6th-degree Polynomial Model remains effective in capturing complex nonlinear variations (RMSE = 4.26). These insights contribute directly to improving real-time WQI calculation performance in IoT ecosystems. |
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| ISSN: | 2730-647X |