Abstract
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.
Highlights
As an important part of the natural environment, water environment plays a vital role in human life
We propose a water quality prediction method based on multi-source transfer learning (MSTL), for the purpose of making full use of the adjacency effect of water quality information
Step 8: At the j-th nearby monitoring point, input the water quality information of the previous d moments of the current time of the target monitoring point into the optimized water quality prediction framework based on MSTL, obtain the prediction result through distributed computing
Summary
As an important part of the natural environment, water environment plays a vital role in human life. We proposed a water quality prediction framework based on MSTL, effectively using the water quality information of multiple nearby monitoring points with distributed computing. We establish the distributed water quality prediction models based on ESN at multiple nearby monitoring points in the framework, effectively using the temporality of water quality information. We propose a water quality prediction method based on MSTL, for the purpose of making full use of the adjacency effect of water quality information. Afterwards, according to the aligned features of water quality samples, the water quality prediction models based on ESN at multiple nearby monitoring points are established with distributed computing, and the prediction results of distributed water quality prediction models are integrated This framework successfully solves the problem of an insufficient number of training samples of the target monitoring point.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
