Non Linear Programming of Time Series Data to Minimize Eutrophication in Truckee River, Nevada

In Truckee River, Nevada high total phosphorus concentrations (TP) lead to Eutrophication and subsequent depletion of dissolved oxygen, increase in dissolved organic carbon and poor water quality. Identifying the exact pattern of relationship among the multiple independent variables that result in low TP is important to implement remediation methods. In this data mining study a non-linear model was developed to identify the relationship of multiple independent variables to TP in Truckee River, Nevada, sampled monthly (from January 1997 to October 2004) over six sites, which was minimized non-linearly with respect to TP. Independent variables included were alkalinity, total soluble phosphorus, stream flow, seasonality, man-made intervention, water pH, water temperature, dissolved organic carbon, and dissolved oxygen. SAS® procedure NLP was used to find the pattern of independent variables that minimize TP non-linearly below the compliance level (0.075mg/L) using least squares (LSQ) minimization. Fitted model predicted data closely explaining 96.7% of total variation. Residuals did not show specific pattern. All independent variables influenced TP significantly at 1% level. Overall LSQ minimization solution (0.0694 mg/L) to objective function was below the compliance level and observed (0.117 mg/L) and model predicted (0.113 mg/L) values for mean TP. Solutions to LSQ minimization were below observed mean TP at all sites.