An evolutionary algorithm (EA)-assisted spatial sampling methodology is proposed to assist decision makers in sensor network (SN) deployments. We incorporated an interpolation technique with leave-one-out cross-validation (LOOCV) to assess the representativeness of a particular SN design. For the validation of our method, we utilized Tasmania's South Esk Hydrological Model developed by the Commonwealth Scientific and Industrial Research Organisation, which includes a range of environmental variables describing the landscape. We demonstrated that our proposed methodology is capable of assisting in the initial design of SN deployment. Ordinary Kriging is shown to be the best suited spatial interpolation algorithm for the EA's LOOCV under the current empirical study.

Item Type:	Refereed Article
Keywords:	evolutionary algorithm (EA), inverse distance weighting (IDW), leave-one-out cross-validation (LOOCV), multiobjective, optimization, ordinary Kriging (OK), sensor network (SN) deployment, spatial data interpolation, spatial sampling
Research Division:	Environmental Sciences
Research Group:	Pollution and contamination
Research Field:	Pollution and contamination not elsewhere classified
Objective Division:	Environmental Management
Objective Group:	Other environmental management
Objective Field:	Other environmental management not elsewhere classified
UTAS Author:	Susanto, F (Mr Ferry Susanto)
UTAS Author:	Budi, S (Mr Setia Budi)
UTAS Author:	Engelke, U (Dr Ulrich Engelke)
ID Code:	117725
Year Published:	2016
Web of Science® Times Cited:	8
Deposited By:	Engineering
Deposited On:	2017-06-26
Last Modified:	2017-10-19
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