Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques
Rain garden is effective in reducing storm water runoff, whose efficiency depends upon several parameters such as soil type, vegetation and meteorological factors. Evaluation of rain gardens has been done by various researchers. However, knowledge for sound design of rain gardens is still very limit...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2021-11-01
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| Series: | Water Science and Technology |
| Subjects: | |
| Online Access: | http://wst.iwaponline.com/content/84/9/2366 |
| _version_ | 1818569152681476096 |
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| author | Sandeep Kumar K. K. Singh |
| author_facet | Sandeep Kumar K. K. Singh |
| author_sort | Sandeep Kumar |
| collection | DOAJ |
| description | Rain garden is effective in reducing storm water runoff, whose efficiency depends upon several parameters such as soil type, vegetation and meteorological 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 its density in the rain garden of the urban area. HIGHLIGHTS
Performance of rain garden for different vegetation has been studied.;
Soft computing techniques has been used to analyze the performance of rain garden.;
Results suggest that soft computing techniques model better than existing conventional models.; |
| first_indexed | 2024-12-14T06:44:22Z |
| format | Article |
| id | doaj.art-3acb241d87f846e5888e7ad2a466fc88 |
| institution | Directory Open Access Journal |
| issn | 0273-1223 1996-9732 |
| language | English |
| last_indexed | 2024-12-14T06:44:22Z |
| publishDate | 2021-11-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Science and Technology |
| spelling | doaj.art-3acb241d87f846e5888e7ad2a466fc882022-12-21T23:13:06ZengIWA PublishingWater Science and Technology0273-12231996-97322021-11-018492366237910.2166/wst.2021.444444Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniquesSandeep Kumar0K. K. Singh1 Department of Civil Engineering, NIT Kurukshetra, Kurukshetra, India Department of Civil Engineering, NIT Kurukshetra, Kurukshetra, India Rain garden is effective in reducing storm water runoff, whose efficiency depends upon several parameters such as soil type, vegetation and meteorological 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 its density in the rain garden of the urban area. HIGHLIGHTS Performance of rain garden for different vegetation has been studied.; Soft computing techniques has been used to analyze the performance of rain garden.; Results suggest that soft computing techniques model better than existing conventional models.;http://wst.iwaponline.com/content/84/9/2366deep learninggradient boosting machinephilip's modelrain gardensimulation of infiltration rate |
| spellingShingle | Sandeep Kumar K. K. Singh Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques Water Science and Technology deep learning gradient boosting machine philip's model rain garden simulation of infiltration rate |
| title | Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques |
| title_full | Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques |
| title_fullStr | Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques |
| title_full_unstemmed | Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques |
| title_short | Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques |
| title_sort | rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques |
| topic | deep learning gradient boosting machine philip's model rain garden simulation of infiltration rate |
| url | http://wst.iwaponline.com/content/84/9/2366 |
| work_keys_str_mv | AT sandeepkumar raingardeninfiltrationratemodelingusinggradientboostingmachineanddeeplearningtechniques AT kksingh raingardeninfiltrationratemodelingusinggradientboostingmachineanddeeplearningtechniques |