Title: Neural network-assisted expensive optimisation algorithm for pollution source rapid positioning of drinking water

Authors: Yingkang Hu; Xuesong Yan

Addresses: School of Environmental Studies, China University of Geosciences, Wuhan, Hubei, China ' School of Computer Science, China University of Geosciences, Wuhan, Hubei, China

Abstract: Pollution source positioning is a complicated problem because urban water supply networks contain a huge number of nodes and it is also a computationally expensive problem. Surrogate model-based intelligent optimisation algorithms can effectively solve such problems. In this study, multiple offline neural network models were constructed using big data technology, which saves time otherwise needed for online model construction. Moreover, a variety of model management strategies are proposed and their validities are experimentally confirmed. Based on this, a neural network-assisted optimisation algorithm is proposed to rapid position of pollution source. The experimental results shown this novel algorithm can greatly reduce computing time while ensuring positioning accuracy.

Keywords: pollution source positioning; water quality sensor network; surrogate model; intelligent optimisation algorithms; neural network.

DOI: 10.1504/IJBIC.2021.116615

International Journal of Bio-Inspired Computation, 2021 Vol.17 No.4, pp.227 - 235

Received: 10 Dec 2020
Accepted: 20 Jan 2021
Published online: 28 Jul 2021 *

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