Pollution index of waterfowl farm assessment and prediction based on temporal convoluted network

Environmental quality is a major factor that directly impacts waterfowl productivity. Accurate prediction of pollution index (PI) is the key to improving environmental management and pollution control. This study applied a new neural network model called temporal convolutional network and a denoisin...

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Bibliographic Details
Published in:PloS one 2021-07, Vol.16 (7), p.e0254179
Main Authors: Huang, Jiande, Liu, Shuangyin, Hassan, Shahbaz Gul, Xu, Longqin
Format: Article
Language:English
Subjects:
Agricultural engineering
Agriculture
Algorithms
Ammonia
Big Data
Biology and Life Sciences
Carbon dioxide
Computer and Information Sciences
Earth Sciences
Ecology and Environmental Sciences
Environmental aspects
Environmental management
Environmental quality
Evaluation
Farms
Forecasting
Health care
Higher education
Humidity
Information science
Information technology
Laboratories
Mathematical models
Neural networks
Noise
Noise reduction
Particulates
Physical Sciences
Pollutants
Pollution control
Pollution index
Poultry
Prediction models
Relative humidity
Research and Analysis Methods
Technological change
Time series
Ventilation
Waterfowl
Wavelet transforms
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Summary:Environmental quality is a major factor that directly impacts waterfowl productivity. Accurate prediction of pollution index (PI) is the key to improving environmental management and pollution control. This study applied a new neural network model called temporal convolutional network and a denoising algorithm called wavelet transform (WT) for predicting future 12-, 24-, and 48-hour PI values at a waterfowl farm in Shanwei, China. The temporal convoluted network (TCN) model performance was compared with that of recurrent architectures with the same capacity, long-short time memory neural network (LSTM), and gated recurrent unit (GRU). Denoised environmental data, including ammonia, temperature, relative humidity, carbon dioxide (CO.sub.2 ), and total suspended particles (TSP), were used to construct the forecasting model. The simulation results showed that the TCN model in general produced a more precise PI prediction and provided the highest prediction accuracy for all phases (MAE = 0.0842, 0.0859, and 0.1115; RMSE = 0.0154, 0.0167, and 0.0273; R2 = 0.9789, 0.9791, and 0.9635). The PI assessment prediction model based on TCN exhibited the best prediction accuracy and general performance compared with other parallel forecasting models and is a suitable and useful tool for predicting PI in waterfowl farms.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0254179