Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System

Water environmental Internet of Things (IoT) system, which is composed of multiple monitoring points equipped with various water quality IoT devices, provides the possibility for accurate water quality prediction. In the same water area, water flows and exchanges between multiple monitoring points,...

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Autores principales:	Jian Zhou, Jian Wang, Yang Chen, Xin Li, Yong Xie
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	water quality prediction multi-source transfer learning echo state network adjacency effect distributed computing environmental IoT system Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/9a50795a31034613b0f65ac6dbf6e98f
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id	oai:doaj.org-article:9a50795a31034613b0f65ac6dbf6e98f
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spelling	oai:doaj.org-article:9a50795a31034613b0f65ac6dbf6e98f2021-11-11T19:13:55ZWater Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System10.3390/s212172711424-8220https://doaj.org/article/9a50795a31034613b0f65ac6dbf6e98f2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7271https://doaj.org/toc/1424-8220Water environmental Internet of Things (IoT) system, which is composed of multiple monitoring points equipped with various water quality IoT devices, provides the possibility for accurate water quality prediction. In the same water area, water flows and exchanges between multiple monitoring points, resulting in an adjacency effect in the water quality information. However, traditional water quality prediction methods only use the water quality information of one monitoring point, ignoring the information of nearby monitoring points. In this paper, we propose a water quality prediction method based on multi-source transfer learning for a water environmental IoT system, in order to effectively use the water quality information of nearby monitoring points to improve the prediction accuracy. First, a water quality prediction framework based on multi-source transfer learning is constructed. Specifically, the common features in water quality samples of multiple nearby monitoring points and target monitoring points are extracted and then aligned. According to the aligned features of water quality samples, the water quality prediction models based on an echo state network at multiple nearby monitoring points are established with distributed computing, and then the prediction results of distributed water quality prediction models are integrated. Second, the prediction parameters of multi-source transfer learning are optimized. Specifically, the back propagates population deviation based on multiple iterations, reducing the feature alignment bias and the model alignment bias to improve the prediction accuracy. Finally, the proposed method is applied in the actual water quality dataset of Hong Kong. The experimental results demonstrate that the proposed method can make full use of the water quality information of multiple nearby monitoring points to train several water quality prediction models and reduce the prediction bias.Jian ZhouJian WangYang ChenXin LiYong XieMDPI AGarticlewater quality predictionmulti-source transfer learningecho state networkadjacency effectdistributed computingenvironmental IoT systemChemical technologyTP1-1185ENSensors, Vol 21, Iss 7271, p 7271 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	water quality prediction multi-source transfer learning echo state network adjacency effect distributed computing environmental IoT system Chemical technology TP1-1185
spellingShingle	water quality prediction multi-source transfer learning echo state network adjacency effect distributed computing environmental IoT system Chemical technology TP1-1185 Jian Zhou Jian Wang Yang Chen Xin Li Yong Xie Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System
description	Water environmental Internet of Things (IoT) system, which is composed of multiple monitoring points equipped with various water quality IoT devices, provides the possibility for accurate water quality prediction. In the same water area, water flows and exchanges between multiple monitoring points, resulting in an adjacency effect in the water quality information. However, traditional water quality prediction methods only use the water quality information of one monitoring point, ignoring the information of nearby monitoring points. In this paper, we propose a water quality prediction method based on multi-source transfer learning for a water environmental IoT system, in order to effectively use the water quality information of nearby monitoring points to improve the prediction accuracy. First, a water quality prediction framework based on multi-source transfer learning is constructed. Specifically, the common features in water quality samples of multiple nearby monitoring points and target monitoring points are extracted and then aligned. According to the aligned features of water quality samples, the water quality prediction models based on an echo state network at multiple nearby monitoring points are established with distributed computing, and then the prediction results of distributed water quality prediction models are integrated. Second, the prediction parameters of multi-source transfer learning are optimized. Specifically, the back propagates population deviation based on multiple iterations, reducing the feature alignment bias and the model alignment bias to improve the prediction accuracy. Finally, the proposed method is applied in the actual water quality dataset of Hong Kong. The experimental results demonstrate that the proposed method can make full use of the water quality information of multiple nearby monitoring points to train several water quality prediction models and reduce the prediction bias.
format	article
author	Jian Zhou Jian Wang Yang Chen Xin Li Yong Xie
author_facet	Jian Zhou Jian Wang Yang Chen Xin Li Yong Xie
author_sort	Jian Zhou
title	Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System
title_short	Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System
title_full	Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System
title_fullStr	Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System
title_full_unstemmed	Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System
title_sort	water quality prediction method based on multi-source transfer learning for water environmental iot system
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9a50795a31034613b0f65ac6dbf6e98f
work_keys_str_mv	AT jianzhou waterqualitypredictionmethodbasedonmultisourcetransferlearningforwaterenvironmentaliotsystem AT jianwang waterqualitypredictionmethodbasedonmultisourcetransferlearningforwaterenvironmentaliotsystem AT yangchen waterqualitypredictionmethodbasedonmultisourcetransferlearningforwaterenvironmentaliotsystem AT xinli waterqualitypredictionmethodbasedonmultisourcetransferlearningforwaterenvironmentaliotsystem AT yongxie waterqualitypredictionmethodbasedonmultisourcetransferlearningforwaterenvironmentaliotsystem
_version_	1718431599727476736

Water Quality Prediction Method Based on Multi-Source Transfer Learning for Water Environmental IoT System

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