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

## TL;DR

This paper introduces a system that predicts restroom dirtiness by measuring water droplet volume using the LightGBM algorithm to optimize cleaning schedules.

## Contribution

A novel prediction system using water droplet volume data and LightGBM to forecast restroom cleaning needs.

## Key findings

- Water droplet accumulation correlates with restroom usage and potential dirtiness.
- The LightGBM-based system accurately predicts cleaning needs based on droplet measurements.
- Near-infrared photography effectively tracks changes in droplet volume over time.

## Abstract

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.