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Estimating the air exchange rates in naturally ventilated cattle houses using Bayesian-optimized GBDT

文献类型： 外文期刊

作者： Ding, Luyu 1 ; E, Lei 1 ; Lyu, Yang 1 ; Yao, Chunxia 1 ; Li, Qifeng 1 ; Huang, Shiwei 4 ; Ma, Weihong 1 ; Yu, Ligen 1 ; Gao, Ronghua 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

2.Natl Innovat Ctr Digital Technol Anim Husb, Beijing 100097, Peoples R China

3.Beijing Technol Innovat Strateg Alliance Intellige, Beijing 100097, Peoples R China

4.China Agr Univ, Coll Water Resources & Civil Engn, Dept Agr Struct & Bioenvironm Engn, Beijing 100083, Peoples R China

关键词： natural ventilation; Bayesian; GBDT; air exchange rate; cattle house

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.4； 五年影响因子：2.8 ）

ISSN： 1934-6344

年卷期： 2023 年 16 卷 1 期

页码：

收录情况： SCI

摘要： It is challenging to estimate the air exchange rate (AER) dynamically in naturally ventilated livestock buildings such as dairy houses due to the influence of complex and variable outdoor environmental factors, large opening ratios, and the confusion of inflow and outflow at openings. This makes it difficult to efficiently regulate the opening ratio to meet the ventilation requirements in naturally ventilated livestock buildings. In this study, the air exchange rates of naturally ventilated cattle houses (NVCHs) in different seasons and opening ratios were obtained through field measurements and computational fluid dynamics (CFD) simulations. A fast and efficient machine learning framework was proposed and examined to predict AER based on the gradient boosting decision tree (GBDT) combined with Bayesian optimization. Compared with commonly used machine learning models such as multilayer perceptrons (MLPs) and support vector machines (SVMs), the proposed GBDT model has higher prediction accuracy and can avoid falling easily into local optima. Compared with the existing mechanical model based on the Bernoulli equation, the proposed GBDT model showed a slightly higher prediction than the mechanistic model and was much easier to use in AER estimation when inputting easily collected environmental factors in practical applications. Using Bayesian optimization could dramatically reduce the computing time when determining the optimal hyperparameter for establishing the GBDT model, dramatically saving on computing resources. Based on the Bayesian optimized GBDT model, the desirable opening ratio of the side curtain can be determined for automatically regulating the AER of cattle houses in future applications.

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