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

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.

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

“…The MLR is a statistical technique that uses several explanatory variables (independent variables) to predict the outcome of a response variable (dependent variable). The MLR is a widely used method to construct regression models for various applications [72,[84][85][86][87]. The goal of the MLR is to model the linear relationship between the explanatory (independent) variables and response (dependent) variable (FSV).…”

The Impact of Catchment Characteristics and Weather Conditions on Heavy Metal Concentrations in Stormwater—Data Mining Approach

“…The hydrographs analysed for the study showed different patterns, depending on the maximum flow values and runoff duration in the sewer. The highest flow (Q) values were observed in the Jesionowa SWTP (maximum 4.53 m 3 •s −1 ), which results not only from precipitation characteristics, but also from the catchment size (Table 1). For Witosa SWTP and IX Wieków Kielc SWTP, Q values amounted to slightly over 0.5 m 3 •s −1 .…”

“…The highest flow (Q) values were observed in the Jesionowa SWTP (maximum 4.53 m 3 •s −1 ), which results not only from precipitation characteristics, but also from the catchment size (Table 1). For Witosa SWTP and IX Wieków Kielc SWTP, Q values amounted to slightly over 0.5 m 3 •s −1 . Hydrographs duration values ranged from 60 to 850 min.…”

“…Initially, to develop the ANN model for predicting HM concentrations, the independent variables describing catchment characteristics, precipitation and weather conditions [1,3,10,22] were adopted. Prior to further analyses, those data were normalised and standardised [23].…”

“…Consequently, they negatively affect the aquatic environment of the receiving waters. Various mathematical models are currently developed in order to predict negative stormwater impacts on the natural environment [1][2][3]. They are useful when making decisions, in adherence to sustainable development principles, which concern land use and urban space management.…”