Ali Shojaeizadeh

The i-DST is a planning-level stormwater decision support tool that helps practitioners select the most appropriate suites of green, grey, and/or |

The objective of this research is to develop a module for the design of best management practices based on percent pollutant removal. The module is a part of the site-scale integrated decision support tool (i-DSTss) that was developed for stormwater management. The current i-DSTss tool allows for the design of best management practices based on flow reduction. The new water quality module extends the capability of the i-DSTss tool by adding new procedures for the design of best management practices based on treatment performance. The water quality module can be used to assess the treatment of colloid/total suspended solid and dissolved pollutants. We classify best management practices into storage-based (e.g., pond) and infiltration-based (e.g., bioretention and permeable pavement) practices for design purposes. Several of the more complex stormwater tools require expertise to build and operate. The i-DSTss and its component modules including the newly added water quality module are built on an accessible platform (Microsoft Excel VBA) and can be operated with a minimum skillset. Predictions from the water quality module were compared with observed data, and the goodness-of-fit was evaluated. For percent total suspended solid removal, both R² and Nash–Sutcliffe efficiency values were greater than 0.7 and 0.6 for infiltration-based and storage-based best management practices, respectively, demonstrating a good fit for both types of best management practices. For percent total phosphorous and Escherichia. coli removal, R² and Nash–Sutcliffe efficiency values demonstrated an acceptable fit. To enhance usability of the tool by a broad range of users, the tool is designed to be flexible allowing user interaction through a graphical user interface.

Machine Learning (ML) algorithms provide an alternative for the prediction of pollutant concentration. We compared eight ML algorithms (Linear Regression (LR), uniform weighting k-Nearest Neighbor (UW-kNN), variable weighting k-Nearest Neighbor (VW-kNN), Support Vector Regression (SVR), Artificial Neural Network (ANN), Regression Tree (RT), Random Forest (RF), and Adaptive Boosting (AdB)) to evaluate the feasibility of ML approaches for estimation of Total Suspended Solids (TSS) using the national stormwater quality database. Six factors were used as features to train the algorithms with TSS concentration as the target parameter: Drainage area, land use, percent of imperviousness, rainfall depth, runoff volume, and antecedent dry days. Comparisons among the ML methods demonstrated a higher degree of variability in model performance, with the coefficient of determination (R²) and Nash–Sutcliffe (NSE) values ranging from 0.15 to 0.77. The Root Mean Square (RMSE) values ranged from 110 mg/L to 220 mg/L. The best fit was obtained using the AdB and RF models, with R² values of 0.77 and 0.74 in the training step and 0.67 and 0.64 in the prediction step. The NSE values were 0.76 and 0.72 in the training step and 0.67 and 0.62 in the prediction step. The predictions from AdB were sensitive to all six factors. However, the sensitivity level was variable.

A site-scale integrated decision support tool (i-DSTss) is developed for selection and sizing of stormwater Best Management Practices (BMPs). The tool has several component modules—hydrology, BMP selection, BMP sizing, and life-cycle cost analysis (LCCA)—integrated into a single platform. The hydrology module predicts runoff from small catchment on event and continuous basis using the Green-Ampt and Curve Number methods. The module predicted runoff from a small residential area and a parking lot with R² value of 0.77 and 0.74, respectively. The BMP selection module recommends a BMP type appropriate for a site based on economic, technical, social and environmental criteria using a multi-criteria optimization approach. The BMP sizing module includes sizing options for green roofs, infiltration-based BMPs, and storage-based BMPs. A mass balance approach is implemented for all types of BMPs. The tool predicted outflow rates from a permeable pavement with R² value of 0.89. A cost module is included where capital, operation and maintenance, and rehabilitation costs are estimated based on BMP size obtained from the sizing module. The i-DSTss is built on an accessible platform (Microsoft Excel VBA) and can be operated with a basic skillset. The i-DSTss is intended for designers, regulators, and municipalities for quick analysis of scenarios involving interaction among several factors.