Laundry, a mundane yet essential routine task in daily life, frequently presents significant challenges in achieving consistent and satisfactory cleaning results due to a multitude of variables, including the number of items being washed, the selection of detergent, the type of fabric, and the solubility of stains. To address these challenges and enhance laundry efficiency, this study endeavors to develop a predictive model utilizing the random forest algorithm. This sophisticated model is designed to predict cleaning effectiveness across a diverse range of fabrics and stains, thereby providing valuable insights into the optimal detergent selection and wash durations for various scenarios. By leveraging the observed nonlinear relationship between washing frequency and stain persistence, the present paper offers practical and actionable guidance for achieving efficient and sustainable laundry practices. The ultimate goal is to reduce water wastage, optimize washing costs, and ultimately, make laundry a more streamlined and eco-friendly process.

Wendong Li. Predictive study of laundry problem based on randomised forest model. Academic Journal of Engineering and Technology Science (2024) Vol. 7, Issue 6: 94-101. https://doi.org/10.25236/AJETS.2024.070614.

[1] Zhang Lei, Chen Jia. Current Trends and Challenges in Laundry Management [J]. Journal of Sustainable Development, 2021, 14(2): 1-12.

[2] Wang Qing, Liu Wei. Physicochemical Properties and Efficacy of Modern Laundry Detergents [J]. Journal of Applied Chemistry, 2020, 13(4): 345-356.

[3] Li Ming, Chen Ying. Water Conservation Strategies in Laundry Operations [J]. Sustainable Water Resources Management, 2022, 8(1): 17-28.

[4] Liu Yang, Zhang Hua. Mathematical Modeling of Laundry Detergent Efficiency [J]. Journal of Mathematical Modeling and Algorithms, 2023, 17(3): 293-308.

[5] Smith A., Brown R. Contemporary Laundry Challenges and Solutions [J]. Textile Research Journal, 2019, 89(16): 3245-3256.

[6] Chen Tao, Wang Xiaoming. Innovative Approaches to Solving Hospital Laundry Problems [J]. Healthcare Management Forum, 2021, 34(4): 189-197.

[7] Xu Wei, Tu Jia, Xu Nan, et al. Predicting Daily Heating Energy Consumption in Residential Buildings through Integration of Random Forest Model and Meta-heuristic Algorithms [J]. Energy, 2024, 301131726

[8] Sun Shengnan, Yuan Zhogang, Liu Zhao, et al. Intra-differential Prediction of Pressure Difference in Cement Vertical Grinding Based on Random Forest Regression Algorithm [J]. Journal of Luoyang Institute of Science and Technology (Natural Science Edition), 2024, 34(2): 44-50.

[9] Li Ping, Wang Dawei. Recent Advances in Smart Laundry Systems [J]. Smart Cities, 2022, 5(3): 123-136.

[10] Yang Qihang, Song Yuehong, Jia Ruilong. Teaching Reform of Physical Chemistry Curriculum Oriented by Applied and Innovative Talents [J]. Education Development and Innovation, 2024, 2(2): 1-14.