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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Autores principales:	Jiande Huang, Shuangyin Liu, Shahbaz Gul Hassan, Longqin Xu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/76c47b9e52a4437688ead59b54273621
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spelling	oai:doaj.org-article:76c47b9e52a4437688ead59b542736212021-12-02T20:04:53ZPollution index of waterfowl farm assessment and prediction based on temporal convoluted network.1932-620310.1371/journal.pone.0254179https://doaj.org/article/76c47b9e52a4437688ead59b542736212021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254179https://doaj.org/toc/1932-6203Environmental 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 (CO2), 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.Jiande HuangShuangyin LiuShahbaz Gul HassanLongqin XuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254179 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jiande Huang Shuangyin Liu Shahbaz Gul Hassan Longqin Xu Pollution index of waterfowl farm assessment and prediction based on temporal convoluted network.
description	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 (CO2), 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.
format	article
author	Jiande Huang Shuangyin Liu Shahbaz Gul Hassan Longqin Xu
author_facet	Jiande Huang Shuangyin Liu Shahbaz Gul Hassan Longqin Xu
author_sort	Jiande Huang
title	Pollution index of waterfowl farm assessment and prediction based on temporal convoluted network.
title_short	Pollution index of waterfowl farm assessment and prediction based on temporal convoluted network.
title_full	Pollution index of waterfowl farm assessment and prediction based on temporal convoluted network.
title_fullStr	Pollution index of waterfowl farm assessment and prediction based on temporal convoluted network.
title_full_unstemmed	Pollution index of waterfowl farm assessment and prediction based on temporal convoluted network.
title_sort	pollution index of waterfowl farm assessment and prediction based on temporal convoluted network.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/76c47b9e52a4437688ead59b54273621
work_keys_str_mv	AT jiandehuang pollutionindexofwaterfowlfarmassessmentandpredictionbasedontemporalconvolutednetwork AT shuangyinliu pollutionindexofwaterfowlfarmassessmentandpredictionbasedontemporalconvolutednetwork AT shahbazgulhassan pollutionindexofwaterfowlfarmassessmentandpredictionbasedontemporalconvolutednetwork AT longqinxu pollutionindexofwaterfowlfarmassessmentandpredictionbasedontemporalconvolutednetwork
_version_	1718375549626220544

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

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