Optimizing an Environmental Surveillance Network with Gaussian Process Entropy

Finding an optimal design for the environmental surveillance network is a realistic need for any ecosystem manager. There are two main factors related to the optimization of a surveillance network: number of sampling points (deciding the sampling density) and locations of such sampling points. This paper aims at proposing an agent-based model to add k measuring devices into a current surveillance network. The simulation is used to verify multiple possibilities of a heterogeneous environment. A Correlation & Disk graph-based Surveillance Network (CDSN) is also implemented and then optimized. Gaussian process is used to model the measuring data and different covariance functions (e.g. variogram in geostatistics). The experimental results of the model are performed for the insect monitoring in Mekong Delta region, Vietnam.