A Web-Based Tool to Interpolate Nitrogen Loading Using a Genetic Algorithm

Abstract

Water quality data may not be collected at a high frequency, nor over the range of streamflow data. For instance, water quality data are often collected monthly, biweekly, or weekly, since collecting and analyzing water quality samples are costly compared to streamflow data. Regression models are often used to interpolate pollutant loads from measurements made intermittently. Web-based Load Interpolation Tool (LOADIN) was developed to provide user-friendly interfaces and to allow use of streamflow and water quality data from U.S. Geological Survey (USGS) via web access. LOADIN has a regression model assuming that instantaneous load is comprised of the pollutant load based on streamflow and the pollutant load variation within the period. The regression model has eight coefficients determined by a genetic algorithm with measured water quality data. LOADIN was applied to eleven water quality datasets from USGS gage stations located in Illinois, Indiana, Michigan, Minnesota, and Wisconsin states with drainage areas from 44 km2 to 1,847,170 km2. Measured loads were calculated by multiplying nitrogen data by streamflow data associated with measured nitrogen data. The estimated nitrogen loads and measured loads were evaluated using Nash-Sutcliffe Efficiency (NSE) and coefficient of determination (R2). NSE ranged from 0.45 to 0.91, and R2 ranged from 0.51 to 0.91 for nitrogen load estimation.

Keywords

• Genetic Algorithm
• Web-based Load Interpolation Tool (LOADIN)
• Pollutant load estimation
• Regression model
• Water quality data