Machine learning-based modeling and analysis of surface cover impacts on hydrological parameters influencing urban runoff
Year: 2026
Type: article
Abstract: Urban development and the expansion of impervious surfaces have increased the volume and intensity of surface runoff, leading to flash floods. This study aims to evaluate the impact of various surface covers and develop an accurate model for predicting runoff parameters. A rainfall simulator was used to experimentally investigate four hydrological parameters: peak flow (PF), runoff coefficient (C), time to runoff (TR), and base time (TB). Ten cover types, including soils, turfgrass, rosemary plants, and gravel reinforced with geocell, were examined at slopes of 0% and 5% under six rainfall intensities. Hydrological parameters were modeled using seven machine learning algorithms: multilayer perceptron neural network (MLP), random forest (RF), gradient boosting (GB), bagging, support vector regression (SVR), XGBoost, and LightGBM. Experimental results showed that covers with three geocell layers (GGE3) and plant-geocell combinations effectively reduced runoff, decreasing PF by up to 70% and C by up to 65%, while increasing TR by over 80 minutes. Under heavy rainfall, GGE3 reduced TB by approximately 30% compared to impervious covers, improving surface drainage. In modeling, XGBoost demonstrated superior performance in predicting PF, C, and TB, achieving R2 = 0.993, MAE ≈ 0.025, and NRMSE < 9% for C. For TB, XGBoost and GB yielded R2 > 0.97 and NRMSE < 8%, showing high accuracy. For TR prediction, SVR in the testing phase achieved R2 = 0.973 and MAE < 4 minutes, outperforming other models.
Source: Results in Engineering
Institutions: Semnan University, K. N. Toosi University of Technology
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,940
OpenAlex ID: W7128691576