Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques

Abstract

Rain garden are effective in reducing storm water runoff, whose efficiency depends upon several parameters such as soil type, vegetation and metrological factors. Evaluation of rain gardens has been done by various researchers. However, knowledge for sound design of rain gardens is still very limited, particularly the accurate modeling of infiltration rate and how much it differs from infiltration of natural ground surface. The present study uses experimentally observed infiltration rate of rain gardens with different types of vegetation (grass, candytuft, marigold and daisy with different plant densities) and flow conditions. After that, modeling has been done by the popular infiltration model i.e. Philip's model (which is valid for natural ground surface) and soft computing tools viz. Gradient Boosting Machine (GBM) and Deep Learning (DL). Results suggest a promising performance (in terms of CC, RMSE, MAE, MSE and NSE) by GBM and DL in comparison to the relation proposed by Philip's model (1957). Most of the values predicted by both GBM and DL are within scatter limits of ±5%, whereas the values by Philips model are within the range of ±25% error lines and even outside. GBM performs better than DL as the values of the correlation coefficients and Nash-Sutcliffe model efficiency (NSE) coefficient are the highest and the root mean square error is the lowest. The results of the study will be useful in selection of plant type and their density of the rain garden in the urban area.

Clear Water Scour Depth Prediction using Gradient Boosting Machine and Deep Learning

“…The details of these parameters are summarized in Table 2. These parameters are obtained by trial and errors process (Kumar and Singh, 2021). Table 3 provides the result obtained in terms of R and R 2 values with training as well as testing datasets in prediction of clear water scour depth around spur dikes.…”

Improving the Efficiency and Environmental Friendliness of Urban Stormwater Management by Enhancing the Water Filtration Model in Rain Gardens

“…Infiltration is the main hydrological process in rain garden structures, which determines the productivity and efficiency of rainwater management [31]. Models designed to describe the infiltration process aim to provide a reliable tool for estimating the design parameters of specific rain garden designs rather than focusing on estimating catchment-level capacities.…”

Stochastic Analysis of the Drawdown Time of Infiltration Basins in the Presence of Heterogeneous Soils

“…An important quantity for the design and assessment of infiltration basins is the drawdown time τ , that is, the time that is needed to completely empty the basin after it has been filled with water, for instance, after a flood event or by a managed discharge (e.g., at the outlet of a treatment plant or from a river diversion). The drawdown time should be sufficiently short to make the storage available for new infiltration water in a reasonable time; this is particularly needed if the basin is also employed for flood protection (e.g., Asleson et al., 2009; Chescheir et al., 1990; Kumar & Singh, 2021). However, τ is limited by the hydraulic conductivity of the soil, as well as by the clogging of the bottom of the basin (Rodríguez‐Escales et al., 2018).…”