Stormwater Runoff Treatment Using Rain Garden: Performance Monitoring and Development of Deep Learning-Based Water Quality Prediction Models
Twenty-three rainfall events were monitored to determine the characteristics of the stormwater runoff entering a rain garden facility and evaluate its performance in terms of pollutant removal and volume reduction. Data gathered during the five-year monitoring period were utilized to develop a deep...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/13/24/3488 |
| _version_ | 1797499914630987776 |
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| author | Minsu Jeon Heidi B. Guerra Hyeseon Choi Donghyun Kwon Hayong Kim Lee-Hyung Kim |
| author_facet | Minsu Jeon Heidi B. Guerra Hyeseon Choi Donghyun Kwon Hayong Kim Lee-Hyung Kim |
| author_sort | Minsu Jeon |
| collection | DOAJ |
| description | Twenty-three rainfall events were monitored to determine the characteristics of the stormwater runoff entering a rain garden facility and evaluate its performance in terms of pollutant removal and volume reduction. Data gathered during the five-year monitoring period were utilized to develop a deep learning-based model that can predict the concentrations of Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Total Nitrogen (TN), and Total Phosphorus (TP). Findings revealed that the rain garden was capable of effectively reducing solids, organics, nutrients, and heavy metals from stormwater runoff during the five-year period when hydrologic and climate conditions have changed. Volume reduction was also high but can decrease over time due to the accumulation of solids in the facility which reduced the infiltration capacity and increased ponding and overflows especially during heavy rainfalls. A preliminary development of a water quality prediction model based on long short-term memory (LSTM) architecture was also developed to be able to potentially reduce the labor and costs associated with on-site monitoring in the future. The LSTM model predicted pollutant concentrations that are close to the actual values with a mean square error of 0.36 during calibration and a less than 10% difference from the measured values during validation. The study showed the potential of using deep learning architecture for the prediction of stormwater quality parameters entering rain gardens. While this study is still in the preliminary stage, it can potentially be improved for use in performance monitoring, decision-making regarding maintenance, and design of similar technologies in the future. |
| first_indexed | 2024-03-10T03:54:18Z |
| format | Article |
| id | doaj.art-94f7fa71d98148c785643d311d76a539 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T03:54:18Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-94f7fa71d98148c785643d311d76a5392023-11-23T11:00:03ZengMDPI AGWater2073-44412021-12-011324348810.3390/w13243488Stormwater Runoff Treatment Using Rain Garden: Performance Monitoring and Development of Deep Learning-Based Water Quality Prediction ModelsMinsu Jeon0Heidi B. Guerra1Hyeseon Choi2Donghyun Kwon3Hayong Kim4Lee-Hyung Kim5Civil and Environmental Engineering Department, Kongju National University, Cheonan 31080, KoreaCivil and Environmental Engineering Department, Kongju National University, Cheonan 31080, KoreaCivil and Environmental Engineering Department, Kongju National University, Cheonan 31080, KoreaUrban System Engineering Department, Kongju National University, Cheonan 31080, KoreaDepartment of Construction Environment Research, Land & Housing Institute, Daejeon 34047, KoreaCivil and Environmental Engineering Department, Kongju National University, Cheonan 31080, KoreaTwenty-three rainfall events were monitored to determine the characteristics of the stormwater runoff entering a rain garden facility and evaluate its performance in terms of pollutant removal and volume reduction. Data gathered during the five-year monitoring period were utilized to develop a deep learning-based model that can predict the concentrations of Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Total Nitrogen (TN), and Total Phosphorus (TP). Findings revealed that the rain garden was capable of effectively reducing solids, organics, nutrients, and heavy metals from stormwater runoff during the five-year period when hydrologic and climate conditions have changed. Volume reduction was also high but can decrease over time due to the accumulation of solids in the facility which reduced the infiltration capacity and increased ponding and overflows especially during heavy rainfalls. A preliminary development of a water quality prediction model based on long short-term memory (LSTM) architecture was also developed to be able to potentially reduce the labor and costs associated with on-site monitoring in the future. The LSTM model predicted pollutant concentrations that are close to the actual values with a mean square error of 0.36 during calibration and a less than 10% difference from the measured values during validation. The study showed the potential of using deep learning architecture for the prediction of stormwater quality parameters entering rain gardens. While this study is still in the preliminary stage, it can potentially be improved for use in performance monitoring, decision-making regarding maintenance, and design of similar technologies in the future.https://www.mdpi.com/2073-4441/13/24/3488deep learninglong short-term memoryrain gardenurban stormwater runoff |
| spellingShingle | Minsu Jeon Heidi B. Guerra Hyeseon Choi Donghyun Kwon Hayong Kim Lee-Hyung Kim Stormwater Runoff Treatment Using Rain Garden: Performance Monitoring and Development of Deep Learning-Based Water Quality Prediction Models Water deep learning long short-term memory rain garden urban stormwater runoff |
| title | Stormwater Runoff Treatment Using Rain Garden: Performance Monitoring and Development of Deep Learning-Based Water Quality Prediction Models |
| title_full | Stormwater Runoff Treatment Using Rain Garden: Performance Monitoring and Development of Deep Learning-Based Water Quality Prediction Models |
| title_fullStr | Stormwater Runoff Treatment Using Rain Garden: Performance Monitoring and Development of Deep Learning-Based Water Quality Prediction Models |
| title_full_unstemmed | Stormwater Runoff Treatment Using Rain Garden: Performance Monitoring and Development of Deep Learning-Based Water Quality Prediction Models |
| title_short | Stormwater Runoff Treatment Using Rain Garden: Performance Monitoring and Development of Deep Learning-Based Water Quality Prediction Models |
| title_sort | stormwater runoff treatment using rain garden performance monitoring and development of deep learning based water quality prediction models |
| topic | deep learning long short-term memory rain garden urban stormwater runoff |
| url | https://www.mdpi.com/2073-4441/13/24/3488 |
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