Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest

Abstract

Despite advancements in sensor technology, monitoring nutrients in situ and in real-time is still challenging and expensive. Soft sensors, based on data-driven models, offer an alternative to direct nutrient measurements. However, the high demand for data required for their development poses logistical issues with data handling. To address this, the study aimed to determine the optimal subset of predictors and the sampling frequency for developing nutrient soft sensors using random forest. The study used water quality data at 15-min intervals from 2 automatic stations on the Main River, Germany, and included dissolved oxygen, temperature, conductivity, pH, streamflow, and cyclical time features as predictors. The optimal subset of predictors was identified using forward subset selection, and the models fitted with the optimal predictors produced R2 values above 0.95 for nitrate, orthophosphate, and ammonium for both stations. The study then trained the models on 40 sampling frequencies, ranging from monthly to 15-min intervals. The results showed that as the sampling frequency increased, the model’s performance, measured by RMSE, improved. The optimal balance between sampling frequency and model performance was identified using a knee-point determination algorithm. The optimal sampling frequency for nitrate was 3.6 and 2.8 h for the 2 stations, respectively. For orthophosphate, it was 2.4 and 1.8 h. For ammonium, it was 2.2 h for 1 station. The study highlights the utility of surrogate models for monitoring nutrient levels and demonstrates that nutrient soft sensors can function with fewer predictors at lower frequencies without significantly decreasing performance.

The dependence of forecasts on sampling frequency as a guide to optimizing monitoring in community ecology

“…Regarding forecasting, in various scientific fields (e.g., climatology, hydrology, and landslide risk management), it has been found that forecasts benefit from high frequency data (e.g., Arhab & Huang, 2023;Bozzano et al, 2018;Leyton & Fritsch, 2004;Liu & Han, 2013). However, in ecology the few studies that investigated how forecasting is affected by sampling frequency found that sampling more often could both improve and worsen forecasts (Derot et al, 2020;Wauchope et al, 2019).…”

The dependence of forecasts on sampling frequency as a guide to optimizing monitoring in community ecology

“…climatology, hydrology, landslide risk management), it has been found that they benefit from high frequency data (e.g. Liu and Han 2013;Leyton and Fritsch 2004;Bozzano, Mazzanti, and Moretto 2018;Arhab and Huang 2023).…”

“…climatology, hydrology, landslide risk management), it has been found that they benefit from high frequency data (e.g. Liu and Han 2013; Leyton and Fritsch 2004; Bozzano, Mazzanti, and Moretto 2018; Arhab and Huang 2023). However, in ecology the few studies that investigated how forecasting is affected by sampling frequency found that sampling more often could both improve and worsen forecasts (Wauchope et al 2019; Derot, Yajima, and Schmitt 2020).…”