ArticlePeerJ2026
A novel GAM-based method for predicting phosphate (PO
Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Phosphate (PO Background: Phosphate pollution in irrigation areas, particularly in regions with shallow groundwater and intensive agriculture, poses serious environmental and agricultural risks, including eutrophication and water-quality degradation. Conventional methods for phosphate monitoring are often time-consuming, costly, and spatially limited, making them unsuitable for real-time applications. Furthermore, the complex, nonlinear interactions between phosphate concentrations and environmental variables, including nitrate, nitrite, pH, EC, flow rate, and precipitation, challenge traditional predictive approaches. While various machine learning models have been explored for phosphorus prediction, their computational demands and overfitting risks often limit their field-level applicability. Therefore, this study aimed to develop a robust, efficient, and interpretable method for predicting phosphate concentrations using a GAM and leveraging daily environmental data collected in a Mediterranean irrigation district in Türkiye. Methods: Daily water samples were collected at the outlet of the L4 agricultural catchment in the Akarsu Irrigation District (AID) on the Lower Seyhan Plain, Türkiye, during the 2022 and 2023 water years. The area is characterized by intensively managed irrigated cropland and shallow groundwater conditions. A total of 522 daily observations were compiled, including PO Results: This study developed a data-driven method for estimating PO
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