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

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Developing nutrient soft sensors is now more feasible. This study optimized predictor selection and sampling frequency, showing effective nutrient monitoring with reduced data needs and costs.

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Area of Science:

  • Environmental Science
  • Water Quality Monitoring
  • Sensor Technology

Background:

  • Real-time, in situ nutrient monitoring remains challenging and costly.
  • Soft sensors, driven by data models, offer a promising alternative to direct measurements.
  • High data requirements for soft sensor development present logistical hurdles.

Purpose of the Study:

  • To optimize predictor subsets and sampling frequencies for nutrient soft sensors.
  • To develop effective nutrient monitoring using random forest models.
  • To reduce data handling complexities in soft sensor development.

Main Methods:

  • Utilized 15-min interval water quality data from two automatic stations on the Main River, Germany.
  • Employed random forest models with dissolved oxygen, temperature, conductivity, pH, streamflow, and time features as predictors.
  • Applied forward subset selection and knee-point determination for optimization.

Main Results:

  • Models achieved R² > 0.95 for nitrate, orthophosphate, and ammonium with optimal predictors.
  • Increased sampling frequency improved model performance (RMSE).
  • Identified optimal sampling frequencies: Nitrate (3.6/2.8h), Orthophosphate (2.4/1.8h), Ammonium (2.2h).

Conclusions:

  • Nutrient soft sensors are effective for water quality monitoring.
  • Optimized models function well with fewer predictors and lower sampling frequencies.
  • This approach reduces data handling burdens and costs in environmental monitoring.