An Enhanced Multiple Regression Model for Predicting Rainfall-Derived Infiltration/Inflow (RDII)
Abstract:A multiple regression model has been developed to predict Rainfall-Derived Infiltration/Inflow (RDII) as a function of Rainfall (R) and Antecedent Moisture Conditions (AMC). The model, which uses previously monitored flows and rainfall, provides a relatively simple procedure for predicting either continuous or event-based RDII as a function of measured rainfall. The AMC is quantified through an Antecedent Precipitation Index (API). The cumulative rain over a relative short period accounts for direct rain response. In this case, the flow monitoring areas are small (≤ 30 hectares or 75 acres approximately), the direct rain response corresponds to cumulative rain over one hour and API is calculated over a period of seven days. This paper presents an update from a previous multiple linear regression method (Sadri and Graham; 2012), whereby both linear and exponential relationships between RDII, rain, and API are assessed. The model also introduces an error function.
The enhanced model has been tested in various study areas in Vaughan, Ontario, Canada, using both multiple linear and multiple exponential regression techniques. Site-specific rain data, from the closest rain gauges, were available for both calibration and validation. Predicted RDII was compared with field observed RDII both graphically and through statistical testing. In general, the exponential model amplified the RDII peak and closely matched areas with higher proportions of direct connections and shorter recession response as compared with the linear model. The exponential model was therefore found to be a better fit for areas with higher peak RDII flows. The linear model better predicted areas with longer recession. The statistical tests were performed to establish the predictive capabilities of the model by comparing observed to projected RDII values. These results confirm the advantage of the exponential relationship, over the linear relationship particularly in areas with higher direct RDII response. This also demonstrates a significant improvement in accuracy through reduction in MSE values. The next steps to improve the predictive model is to introduce a seasonally adjusted API function that will relate the different antecedent moisture content representation.
Document Type: Research Article
Publication date: 2012-01-01
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