Evaluating the Effectiveness of IDW Model in Developing Spatial Distribution Maps of Water Quality in the Soai Rap - Long Tau River
DOI:
https://doi.org/10.63746/njtd.v22i4.3500Keywords:
Surface water quality, Interpolation models, IDW, Human impact, Mangrove forestAbstract
Surface water quality of Can Gio mangrove forest, one of the world's biosphere reserves, plays important role for the regional ecosystem. However, there has been no assessment in water quality progression within the forest's core area. The Inverse Distance Weighting (IDW) method, which is a widely applied regression model, therefore has not been applied to evaluate its effectiveness in the study area. This study aims to assess the applicability of the IDW model in developing spatial distribution maps of surface water quality in the Soai Rap - Long Tau River system by collecting and analyzing 25 water samples along a 70 km waterway. Monitored parameters include pH, BOD, COD, Total Phosphorus, Total Nitrogen, E-Coliform, Coliform, and Salinity. Results reveal that BOD and COD concentrations exceed national standards by 1.4 and 1.5 times, respectively, indicating organic pollution, while other parameters remain within acceptable limits, suggesting minimal microbial and agricultural contamination. In Soai Rap - Long Tau area, IDW-based spatial maps show higher pollution levels in densely populated, industrial, and transport-affected zones, confirming the influence of human activities on water quality. In the study area, statistical validation indicates that the IDW model performs well for most parameters, with correlation coefficients (R) above 0.5, especially for Salinity (R2 = 0.84), demonstrating strong model fit. For pH, although error metrics (MAE, MBE, and RMSE) indicate reasonable accuracy, the low R value suggests poor spatial correlation. The study recommends denser sampling for parameters like pH and further refinement of spatial models to improve prediction reliability. Overall, the IDW model proves useful for visualizing surface water quality patterns and identifying pollution hotspots in the Can Gio mangrove area.
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