Support Vector Regression Models of Stormwater Quality for a Mixed Urban Land Use

• Published in: Hydrology 2023-03, Vol.10 (3), p.66
• Main Authors: Kshirsagar, Mugdha P., Khare, Kanchan C.
• Format: Article
• Language: English
• Subjects: Algorithms; Ammonia; Basins; Biodiversity; Chemical oxygen demand; Cities; Coefficients; Creeks & streams; Decision making; Dissolved oxygen; Dissolved solids; Drainage; Environmental aspects; Garbage; Hydrology; Kernel functions; Laboratories; Land use; Machine learning; mixed land use; Model accuracy; Multiple criterion; nonpoint pollutants; Nonpoint source pollution; Oxygen; Oxygen requirement; Parameters; Phosphates; Point source pollution; Pollutants; Pollution sources; Population; Precipitation; Principal components analysis; Rain; Rain-water (Water-supply); Rainfall; Regression analysis; Regression models; Rivers; Roadsides; Runoff; Solid suspensions; Storms; Stormwater; Stormwater management; Stormwater quality; Stream pollution; support vector; Support vector machines; Suspended particulate matter; Total dissolved solids; Total suspended solids; Turbidity; Urban areas; Urban land use; urban stormwater quality; Urbanization; Water harvesting; Water pollution; Water quality; water quality index; Wind
• ISSN: 2306-5338; 2306-5338
• DOI: 10.3390/hydrology10030066

The present study is an attempt to model the stormwater quality of a stream located in Pune, India. The city is split up into twenty-three basins (named A to W) by the Pune Municipal Corporation. The selected stream lies in the haphazardly expanded peri-urban G basin. The G basin has constructed stormwater drains which open up in this selected open stream. The runoff over the regions picks up the non-point source pollutants which are also added to the selected stream. The study becomes more complex as the stream is misused to dump trash materials, garbage and roadside litter, which adds to the stormwater pollution. Experimental investigations include eleven distinct locations on a naturally occurring stream in the G basin. Stormwater samples were collected for twenty-two storm events, for the monsoon season over four years from 2018–2021, during and after rainfall. The physicochemical characteristics were analyzed for twelve water quality parameters, including pH, Conductivity, Turbidity, Total solids (TS), Total Suspended Solids (TSS), Total Dissolved Solids (TDS), Bio-chemical Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Phosphate, Ammonia and Nitrate. The Water Quality Index (WQI) ranged from 46.9 to 153.9 and from 41.20 to 87.70 for samples collected during and immediately after the rainfall, respectively. Principal Component Analysis was used to extract the most significant stormwater quality parameters. To understand the non-linear complex relationship of rainfall characteristics with significant stormwater pollutant parameters, a Support Vector Regression (SVR) model with Radial Basis Kernel Function (RBF) was developed. The Support Vector Machine is a powerful supervised algorithm that works best on smaller datasets but on complex ones with the help of kernel tricks. The accuracy of the model was evaluated based on normalized root-mean-square error (NRMSE), coefficient of determination (R2) and the ratio of performance to the interquartile range (RPIQ). The SVR model depicted the best performance for parameter TS with NRMSE (0.17), R2 (0.82) and RPIQ (2.91). The unit increase or decrease in the coefficients of rainfall characteristics displays the weighted deviation in the values of pollutant parameters. Non-linear Support Vector Regression models confirmed that both antecedent dry days and rainfall are correlated with significant stormwater quality parameters. The conclusions drawn can provide effective information to