Artificial neural networks for monitoring network optimisation—a practical example using a national insect survey

A - Papers appearing in refereed journals

Bourhis, Y., Bell, J. R., Van Den Bosch, F. and Milne, A. E. 2021. Artificial neural networks for monitoring network optimisation—a practical example using a national insect survey. Environmental Modelling and Software. 135, p. 104925. https://doi.org/10.1016/j.envsoft.2020.104925

AuthorsBourhis, Y., Bell, J. R., Van Den Bosch, F. and Milne, A. E.
Abstract

Monitoring networks are improved by additional sensors. Optimal configurations of sensors give better representations of the process of interest, maximising its exploration while minimising the need for costly infrastructure. By modelling the monitored process, we can identify gaps in its representation, i.e. uncertain predictions, where additional sensors should be located. Here, with data collected from the Rothamsted Insect Survey network, we train an artificial neural network to predict the seasonal aphid arrival from environmental variables. We focus on estimating prediction uncertainty across the UK to guide the addition of a sensor to the network. We first illustrate how to estimate uncertainty in neural networks, hence making them relevant for model-based monitoring network optimisation. Then we highlight critical areas of agricultural importance where additional traps would improve decision support and crop protection in the UK. Possible applications include most ecological monitoring and surveillance activities, but also the weather or pollution monitoring.

KeywordsUncertainty estimation ; Artificial neural network ; Surveillance; Monitoring network; Aphid
Year of Publication2021
JournalEnvironmental Modelling and Software
Journal citation135, p. 104925
Digital Object Identifier (DOI)https://doi.org/10.1016/j.envsoft.2020.104925
Web address (URL)https://www.sciencedirect.com/science/article/pii/S1364815220309828?dgcid=coauthor
Open accessPublished as non-open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeBBSRC Strategic Programme in Smart Crop Protection
The Rothamsted Insect Survey - National Capability [2017-2022]
Output statusPublished
Publication dates
Online05 Nov 2020
Publication process dates
Accepted31 Oct 2020
PublisherElsevier
ISSN1364-8152

Permalink - https://repository.rothamsted.ac.uk/item/98262/artificial-neural-networks-for-monitoring-network-optimisation-a-practical-example-using-a-national-insect-survey

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