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Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm.

Sumio Kurose¹, Hironori Moriwaki², Tadao Matsunaga¹

¹School of Engineering, Tottori University, 4-101 Koyama-Minami, Tottori 680-8552, Japan.

Sensors (Basel, Switzerland)

|April 12, 2025

Summary

This study uses water droplet volume on washbowls to predict restroom usage and optimize cleaning schedules, addressing rising costs and labor shortages for efficient facility maintenance.

Keywords:

LightGBM cleaning schedule prediction data augmentation techniques restroom dirtiness water droplet volume prediction

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High Throughput Analysis of Liquid Droplet Impacts

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Published on: March 6, 2020

Area of Science:

Environmental Science
Facility Management
Data Science

Background:

Rising cleaning costs and labor shortages necessitate efficient restroom maintenance strategies.
Accurate prediction of restroom usage is crucial for optimizing cleaning schedules and resource allocation.

Purpose of the Study:

To develop and evaluate a prediction system for restroom cleaning needs based on water droplet accumulation.
To quantify restroom usage patterns using water droplet volumes as an indicator.

Main Methods:

Acrylic plates were installed around washbowls in public restrooms to collect water droplets.
Near-infrared photography was used to analyze changes in water droplet areas over hourly intervals for five days.
A prediction system was developed using the decision tree method and the LightGBM framework.

Main Results:

Significant variations in water droplet volumes correlated with increased restroom usage and potential dirt buildup.
The developed prediction system demonstrated accuracy in forecasting restroom cleaning needs based on in situ measurements.

Conclusions:

Water droplet volume analysis provides a viable method for assessing restroom usage and informing cleaning schedules.
The LightGBM-based prediction system offers an efficient tool for optimizing facility maintenance and reducing costs.