Urban stormwater thermal gene expression models for protection of sensitive receiving streams

Abstract

Thermal impact of typical high-density residential, industrial, and commercial land uses is a major concern for the health of aquatic life in urban watersheds, especially in smaller, cold, and coolwater streams. This is the first study of its kind that provides simple easy-to-use equations, developed using gene expression programming (GEP) that can guide the assessment and the design of urban stormwater management systems to protect thermally sensitive receiving streams. We developed 3 GEP models using data collected during 3 years (2009-2011) from 4 urban catchments; the first GEP model predicts event mean temperature at the inlet of the pond; the second model predicts the stormwater temperature at the outlet of the pond; and the third model predicts the temperature of the stormwater after flowing through a cooling trench and before discharging to the receiving stream. The new models have high correlation coefficients of 0.90-0.94 and low prediction uncertainty of less than 4% of the median value of the predicted runoff temperatures. Sensitivity analysis shows that climatic factors have the highest influence on the thermal enrichment followed by the catchment characteristics and the key design variables of the stormwater pond and the cooling trench. The general method presented here is easily transferable to other regions of the world (but not necessarily the exact equations developed here); also through sensitivity and parametric analysis, we gained insight on the key factors and their relative importance in modelling thermal enrichment of urban stromwater runoff.

Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction

“…Machine learning is frequently used to model the complex non-linear relationships in natural systems, including soil moisture, soil temperature, and stream habitat assessments [36][37][38][39]. Both ANN and GEP models have been developed to model the impact of anthropogenic heat sources, such as stormwater management ponds and impervious surfaces, on the receiving stream temperature [40][41][42][43].…”

Hydraulic Modeling and Evaluation Equations for the Incipient Motion of Sandbags for Levee Breach Closure Operations

“…Using the calibrated and developed equations for sandbag incipient motion together with the experimental measurements, a quantitative assessment for the uncertainty in the prediction of critical velocity can be presented. The uncertainty analysis defines the prediction error in log cycles as [55][56][57][58][59][60][61][62][63][64][65] e i = log 10 (P i ) − log 10 (T i ) (10) where e i is the prediction error, P i is the predicted value of parameter, and T i is the measured value of the parameter. Data were then used to calculate main indicators defined as mean prediction error e = ∑ n i = 1 e i , the width of uncertainty band, B ub = ± 1.96S e and the confidence band around the predicted value:…”

Urban Stormwater Characterization, Control and Treatment

“…They applied long-term monitoring data and a finite-element model to demonstrate the potential to mitigate for flashiness-induced intrusion of high conductivity stormwater into alluvial aquifers through stormwater volume control. Sattar et al (2017) precipitation events. Based on differences in pollutant washoff behavior by land use, the authors recommended stormwater treatment volumes equal to 10% runoff volume capture for NH3, SRP, total phosphorus (TP) and Zn in residential and commercial land uses and 30-50% in industrial land uses.…”