Artificial neural networks for monitoring network optimisation—a practical example using a national insect survey. (January 2021) - Bourhis, Yoann; Bell, James R.; van den Bosch, Frank; Milne, Alice 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. Highlights: Sensor networks are used to monitor biological processes, such as aphid arrival. Artificial neural networks can provide spatial predictions from point observations. We show how artificial neural networks can also provide uncertainty estimates. Model uncertainty highlights gaps where new sensors should be located.