A neural network-based prediction model in water monitoring networks

To improve the prediction accuracy of ammonia nitrogen in water monitoring networks, the combination of a bio-inspired algorithm and back propagation neural network (BPNN) has often been deployed. However, due to the limitations of the bio-inspired algorithm, it would also fall into the local optima...

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Autores principales:	Xiaohong Ji, Ying Pan, Guoqing Jia, Weidong Fang
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	ammonia nitrogen prediction back propagation neural network improved seagull optimization algorithm neural network seagull optimization algorithm water monitoring Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/56e2819a9fc84021b0c270cbc14599ea
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id	oai:doaj.org-article:56e2819a9fc84021b0c270cbc14599ea
record_format	dspace
spelling	oai:doaj.org-article:56e2819a9fc84021b0c270cbc14599ea2021-11-06T07:19:14ZA neural network-based prediction model in water monitoring networks1606-97491607-079810.2166/ws.2021.046https://doaj.org/article/56e2819a9fc84021b0c270cbc14599ea2021-08-01T00:00:00Zhttp://ws.iwaponline.com/content/21/5/2347https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798To improve the prediction accuracy of ammonia nitrogen in water monitoring networks, the combination of a bio-inspired algorithm and back propagation neural network (BPNN) has often been deployed. However, due to the limitations of the bio-inspired algorithm, it would also fall into the local optimal. In this paper, the seagull optimization algorithm (SOA) was used to optimize the structure of BPNN to obtain a better prediction model. Then, an improved SOA (ISOA) was proposed, and the common functional validation method was used to verify its optimization performance. Finally, the ISOA was applied to improve BPNN, which is known as the improved seagull optimization algorithm–back propagation (ISOA–BP) model. The simulation results showed that the prediction accuracy of ammonia nitrogen was greatly improved and the proposed model can be better applied to the prediction of complex water quality parameters in water monitoring networks. HIGHLIGHTS The structure of BPNN was optimized to obtain a better prediction model by using the seagull optimization algorithm (SOA).; We proposed an improving SOA (ISOA) and used the common functional validation method to verify its optimization performance.; The ISOA was used to improve BPNN, via the improved seagull optimization algorithm–back propagation (ISOA–BP) model.;Xiaohong JiYing PanGuoqing JiaWeidong FangIWA Publishingarticleammonia nitrogen predictionback propagation neural networkimproved seagull optimization algorithmneural networkseagull optimization algorithmwater monitoringWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 5, Pp 2347-2356 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ammonia nitrogen prediction back propagation neural network improved seagull optimization algorithm neural network seagull optimization algorithm water monitoring Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	ammonia nitrogen prediction back propagation neural network improved seagull optimization algorithm neural network seagull optimization algorithm water monitoring Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Xiaohong Ji Ying Pan Guoqing Jia Weidong Fang A neural network-based prediction model in water monitoring networks
description	To improve the prediction accuracy of ammonia nitrogen in water monitoring networks, the combination of a bio-inspired algorithm and back propagation neural network (BPNN) has often been deployed. However, due to the limitations of the bio-inspired algorithm, it would also fall into the local optimal. In this paper, the seagull optimization algorithm (SOA) was used to optimize the structure of BPNN to obtain a better prediction model. Then, an improved SOA (ISOA) was proposed, and the common functional validation method was used to verify its optimization performance. Finally, the ISOA was applied to improve BPNN, which is known as the improved seagull optimization algorithm–back propagation (ISOA–BP) model. The simulation results showed that the prediction accuracy of ammonia nitrogen was greatly improved and the proposed model can be better applied to the prediction of complex water quality parameters in water monitoring networks. HIGHLIGHTS The structure of BPNN was optimized to obtain a better prediction model by using the seagull optimization algorithm (SOA).; We proposed an improving SOA (ISOA) and used the common functional validation method to verify its optimization performance.; The ISOA was used to improve BPNN, via the improved seagull optimization algorithm–back propagation (ISOA–BP) model.;
format	article
author	Xiaohong Ji Ying Pan Guoqing Jia Weidong Fang
author_facet	Xiaohong Ji Ying Pan Guoqing Jia Weidong Fang
author_sort	Xiaohong Ji
title	A neural network-based prediction model in water monitoring networks
title_short	A neural network-based prediction model in water monitoring networks
title_full	A neural network-based prediction model in water monitoring networks
title_fullStr	A neural network-based prediction model in water monitoring networks
title_full_unstemmed	A neural network-based prediction model in water monitoring networks
title_sort	neural network-based prediction model in water monitoring networks
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/56e2819a9fc84021b0c270cbc14599ea
work_keys_str_mv	AT xiaohongji aneuralnetworkbasedpredictionmodelinwatermonitoringnetworks AT yingpan aneuralnetworkbasedpredictionmodelinwatermonitoringnetworks AT guoqingjia aneuralnetworkbasedpredictionmodelinwatermonitoringnetworks AT weidongfang aneuralnetworkbasedpredictionmodelinwatermonitoringnetworks AT xiaohongji neuralnetworkbasedpredictionmodelinwatermonitoringnetworks AT yingpan neuralnetworkbasedpredictionmodelinwatermonitoringnetworks AT guoqingjia neuralnetworkbasedpredictionmodelinwatermonitoringnetworks AT weidongfang neuralnetworkbasedpredictionmodelinwatermonitoringnetworks
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A neural network-based prediction model in water monitoring networks

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