Transferable soft-sensors for predicting nitrate in diverse watersheds

• PR-TR predicts nitrate using minimal data and low-cost surrogate sensors. • Achieved NSE ∼0.51 with only 15 samples when site difference was <100%. • Outperformed ANN and MLR that required >40 samples for similar accuracy. • Identified suitable models without needing nitrate data from new sites. Understanding the spatial and temporal dynamics of nitrates is crucial to mitigate pollution that causes eutrophication and poor aquatic health. However, in-situ sensors for direct nitrate detection are often limited by high costs, frequent maintenance requirements, and low sensitivity. Soft-sensing has emerged as a promising alternative, where nitrates are predicted using surrogate sensors using models or machine learning. This study addresses a central challenge with soft-sensors: their transferability to sites with limited or no training data. We propose a transferable framework that predicts nitrate concentrations using only a small number of training data points together with simple, low-cost sensors such as electrical conductivity, temperature, and turbidity. The approach selects a pre-trained model (PR-TR) from a large model library using only historical surrogate data, with site similarity determined through Euclidean distance and a relative difference metric. For sites with relative differences below 100%, the PR-TR model achieved an average NSE of 0.51 using only 15 data points. For more dissimilar sites, higher data requirements and careful tuning of the learning rate (0.01) were essential, yet PR-TR still outperformed traditional approaches. Compared with artificial neural networks (ANN) and multiple linear regression (MLR), which required more than 40 data points to reach similar performance, PR-TR delivered robust and efficient predictions using significantly fewer data samples. The model selection process identified suitable PR-TR models capable of achieving positive NSE values even without nitrate data from the validation site. These findings demonstrate that PR-TR offers a practical, data-efficient method for reliable water quality monitoring.