DLGEA: a deep learning guided evolutionary algorithm for water contamination source identification

Qian，Kai; Jiang，Jie; Ding，Yulong; Yang，Shuang Hua

2021

10.1007/s00521-021-05894-y

NEURAL COMPUTING & APPLICATIONS   影响因子和分区

0941-0643

1433-3058

Water distribution network (WDN) is one of the most essential infrastructures all over the world and ensuring water quality is always a top priority. To this end, water quality sensors are often deployed at multiple points of WDNs for real-time contamination detection and fast contamination source identification (CSI). Specifically, CSI aims to identify the location of the contamination source, together with some other variables such as the starting time and the duration. Such information is important in making an efficient plan to mitigate the contamination event. In the literature, simulation-optimisation methods, which combine simulation tools with evolutionary algorithms (EAs), show great potential in solving CSI problems. However, the application of EAs for CSI is still facing big challenges due to their high computational cost. In this paper, we propose DLGEA, a deep learning guided evolutionary algorithm to improve the efficiency by optimising the search space of EAs. Firstly, based on a large number of simulated contamination events, DLGEA trains a deep neural network (DNN) model to capture the relationship between the time series of sensor data and the contamination source nodes. Secondly, given a contamination event, DLGEA guides the initialisation and optimise the search space of EAs based on the top K contamination nodes predicated by the DNN model. Empirically, based on two benchmark WDNs, we show that DLGEA outperforms the CSI method purely based on EAs in terms of both the average topological distance and the accumulated errors between the predicted and the real contamination events.

Contamination source identification Deep neural networks Evolutionary algorithms Time series analysis Water distribution networks Water quality sensors

SCI ; EI

英语

第一 ; 通讯

National Natural Science Foundation of China[61873119] ; National Key R&D Program of China[2019YFC0810705] ; Science and Technology Innovation Commission of Shenzhen[KQJSCX20180322151418232]

Computer Science

Computer Science, Artificial Intelligence

WOS:000629917800005

SPRINGER LONDON LTD

20211310130788

Contamination ; Deep neural networks ; Evolutionary algorithms ; Learning algorithms ; Neural networks ; Water distribution systems ; Water pollution ; Water quality

Water Analysis:445.2 ; Water Supply Systems:446.1 ; Water Pollution:453

ENGINEERING

2-s2.0-85102925094

Scopus

Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China

Qian，Kai,Jiang，Jie,Ding，Yulong,et al. DLGEA: a deep learning guided evolutionary algorithm for water contamination source identification[J]. NEURAL COMPUTING & APPLICATIONS,2021,33:11889-11903.

Qian，Kai,Jiang，Jie,Ding，Yulong,&Yang，Shuang Hua.(2021).DLGEA: a deep learning guided evolutionary algorithm for water contamination source identification.NEURAL COMPUTING & APPLICATIONS,33,11889-11903.

Qian，Kai,et al."DLGEA: a deep learning guided evolutionary algorithm for water contamination source identification".NEURAL COMPUTING & APPLICATIONS 33(2021):11889-11903.