Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm
This study examines restroom cleanliness in public facilities, department stores, supermarkets, and schools by using water droplet volumes around washbowls as an indicator of usage. Rising cleaning costs due to labour shortages necessitate more efficient restroom maintenance. Quantifying water dropl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2186 |
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| author | Sumio Kurose Hironori Moriwaki Tadao Matsunaga Sang-Seok Lee |
| author_facet | Sumio Kurose Hironori Moriwaki Tadao Matsunaga Sang-Seok Lee |
| author_sort | Sumio Kurose |
| collection | DOAJ |
| description | This study examines restroom cleanliness in public facilities, department stores, supermarkets, and schools by using water droplet volumes around washbowls as an indicator of usage. Rising cleaning costs due to labour shortages necessitate more efficient restroom maintenance. Quantifying water droplet accumulation and predicting cleaning schedules can help optimise cleaning frequency. To achieve this, water droplet volumes were measured at specific time intervals, with significant variations indicating increased restroom usage and potential dirt buildup. For real-world assessment, acrylic plates were placed on both sides of washbowls in public restrooms. These plates were collected every hour over five days and analysed using near-infrared photography to track changes in water droplet areas. The collected data informed the development of a prediction system based on the decision tree method, implemented via the LightGBM framework. This paper presents the developed prediction system, which utilises in situ water droplet volume measurements, and evaluates its accuracy in forecasting restroom cleaning needs. |
| format | Article |
| id | doaj-art-784e0d165b0a42efa065a4d94a81370f |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-784e0d165b0a42efa065a4d94a81370f2025-08-20T03:03:23ZengMDPI AGSensors1424-82202025-03-01257218610.3390/s25072186Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM AlgorithmSumio Kurose0Hironori Moriwaki1Tadao Matsunaga2Sang-Seok Lee3School of Engineering, Tottori University, 4-101 Koyama-Minami, Tottori 680-8552, JapanAsuka Bisou Co., Ltd., 296-1 Daigo, Kashihara 634-0072, JapanSchool of Engineering, Tottori University, 4-101 Koyama-Minami, Tottori 680-8552, JapanSchool of Engineering, Tottori University, 4-101 Koyama-Minami, Tottori 680-8552, JapanThis study examines restroom cleanliness in public facilities, department stores, supermarkets, and schools by using water droplet volumes around washbowls as an indicator of usage. Rising cleaning costs due to labour shortages necessitate more efficient restroom maintenance. Quantifying water droplet accumulation and predicting cleaning schedules can help optimise cleaning frequency. To achieve this, water droplet volumes were measured at specific time intervals, with significant variations indicating increased restroom usage and potential dirt buildup. For real-world assessment, acrylic plates were placed on both sides of washbowls in public restrooms. These plates were collected every hour over five days and analysed using near-infrared photography to track changes in water droplet areas. The collected data informed the development of a prediction system based on the decision tree method, implemented via the LightGBM framework. This paper presents the developed prediction system, which utilises in situ water droplet volume measurements, and evaluates its accuracy in forecasting restroom cleaning needs.https://www.mdpi.com/1424-8220/25/7/2186restroom dirtinessLightGBMdata augmentation techniqueswater droplet volume predictioncleaning schedule prediction |
| spellingShingle | Sumio Kurose Hironori Moriwaki Tadao Matsunaga Sang-Seok Lee Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm Sensors restroom dirtiness LightGBM data augmentation techniques water droplet volume prediction cleaning schedule prediction |
| title | Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm |
| title_full | Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm |
| title_fullStr | Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm |
| title_full_unstemmed | Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm |
| title_short | Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm |
| title_sort | predicting restroom dirtiness based on water droplet volume using the lightgbm algorithm |
| topic | restroom dirtiness LightGBM data augmentation techniques water droplet volume prediction cleaning schedule prediction |
| url | https://www.mdpi.com/1424-8220/25/7/2186 |
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