Multiple Linear Regression Models for Predicting Nonpoint-Source Pollutant Discharge from a Highland Agricultural Region

Jae Cho,Jong Lee

doi:10.3390/w10091156

Abstract

Sediment runoff from dense highland field areas greatly affects the quality of downstream lakes and drinking water sources. In this study, multiple linear regression (MLR) models were built to predict diffuse pollutant discharge using the environmental parameters of a basin. Explanatory variables that influence the sediment and pollutant discharge can be identified with the model, and such research could play an important role in limiting sediment erosion in the dense highland field area. Pollutant load per event, event mean concentration (EMC), and pollutant load per area were estimated from stormwater survey data from the Lake Soyang basin. During the wet season, heavy rains cause large amounts of suspended sediment and the occurrence of such rains is increasing due to climate change. The explanatory variables used in the MLR models are the percentage of fields, subbasin area, and mean slope of subbasin as topographic parameters, and the number of preceding dry days, rainfall intensity, rainfall depth, and rainfall duration as rainfall parameters. In the MLR modeling process, four types of regression equations with and without log transformation of the explanatory and response variables were examined to identify the best performing regression model. The performance of the MLR models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), coefficient of variation of the root mean square error (CV(RMSE)), the ratio of the RMSE to the standard deviation of the observed data (RSR) and the Nash–Sutcliffe model efficiency (NSE). The performance of the MLR models of pollutant load except total nitrogen (TN) was good under the condition of RSR, and satisfactory for the NSE and R2. In the EMC and load/area models, the performance for suspended solids (SS) and total phosphorus (TP) was good for the RSR, and satisfactory for the NSE and R2. The standardized coefficients for the models were analyzed to identify the influential explanatory variables in the models. In the final performance evaluation, the results of jackknife validation indicate that the MLR models are robust.

Highlights

In Lake Soyang basin of South Korea, large amounts of sediment are discharged from highland agricultural field regions in the wet season
The subbasin slope had a negative correlation with the response variables of event mean concentration (EMC) and load/area
These results suggest that the coefficient of regression for the explanatory variables could be statistically acceptable and that multicollinearity was not present in the established models

Summary

Introduction

In Lake Soyang basin of South Korea, large amounts of sediment are discharged from highland agricultural field regions in the wet season. To develop environmental preservation measures that protect water resources from the turbid water problem and diffuse pollution, prediction models are necessary to estimate the amount of pollutants discharged from subbasins. A multiple linear regression (MLR) model is established to predict the pollutant runoff discharge using environmental parameters, such as land use, rainfall, and topography. In South Korea, rainfall events of 200 mm or more occurred only once annually, on average, until the end of the 1970s, but increased to a frequency of two per year in the 1980s and thereafter occurred five times in both 1984 and 1998. Conditions in Lake Soyang, located in the upper reaches of the Han River, greatly affect the water quality of the water supply of the capital region of

Methods

Results

Conclusion