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

10.1101/2020.05.14.095539 ◽

2020 ◽

Author(s):

Yoann Bourhis ◽

James R. Bell ◽

Frank van den Bosch ◽

Alice E. Milne

Keyword(s):

Neural Networks ◽

Crop Protection ◽

Monitoring Network ◽

Ecological Monitoring ◽

Pollution Monitoring ◽

Monitoring Networks ◽

Insect Survey ◽

Network Optimisation ◽

Artificial Neural ◽

The Uk

AbstractMonitoring 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.

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Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

Sensors ◽

10.3390/s21051770 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1770

Author(s):

Javier González-Enrique ◽

Juan Jesús Ruiz-Aguilar ◽

José Antonio Moscoso-López ◽

Daniel Urda ◽

Lipika Deka ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Short Term Memory ◽

Prediction Method ◽

Monitoring Network ◽

Exogenous Variables ◽

Prediction Horizon ◽

Artificial Neural ◽

Input Variables ◽

Analytical Tools

This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to create the forecasting models. Additionally, a new prediction method was proposed combining LSTMs using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two different strategies were followed regarding the input variables: using NO2 from the station or employing NO2 and other pollutants data from any station of the network plus meteorological variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window sizes. Several feature ranking methods were used to select the top lagged variables and include them in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The exogenous variables inclusion enhanced the model’s performance, especially for t + 4 (ρ ≈ 0.68 to ρ ≈ 0.74) and t + 8 (ρ ≈ 0.59 to ρ ≈ 0.66). The proposed LSTM-CVT method delivered promising results as the best performing models per prediction horizon employed this new methodology. Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% of the cases.

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Contributing towards Representative PM Data Coverage by Utilizing Artificial Neural Networks

Applied Sciences ◽

10.3390/app11188431 ◽

2021 ◽

Vol 11 (18) ◽

pp. 8431

Author(s):

Chris G. Tzanis ◽

Anastasios Alimissis

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Wind Speed ◽

Urban Areas ◽

Environmental Control ◽

Monitoring Network ◽

Data Availability ◽

Pm10 And Pm2.5 ◽

Artificial Neural

Atmospheric aerosol particles have a significant impact on both the climatic conditions and human health, especially in densely populated urban areas, where the particle concentrations in several cases can be extremely threatening (increased anthropogenic emissions). Most large cities located in high-income countries have stations responsible for measuring particulate matter and various other parameters, collectively forming an operating monitoring network, which is essential for the purposes of environmental control. In the city of Athens, which is characterized by high population density and accumulates a large number of economic activities, the currently operating monitoring network is responsible, among others, for PM10 and PM2.5 measurements. The need for satisfactory data availability though can be supported by using machine learning methods, such as artificial neural networks. The methodology presented in this study uses a neural network model to provide spatiotemporal estimations of PM10 and PM2.5 concentrations by utilizing the existing PM data in combination with other climatic parameters that affect them. The overall performance of the predictive neural network models’ scheme is enhanced when meteorological parameters (wind speed and temperature) are included in the training process, lowering the error values of the predicted versus the observed time series’ concentrations. Furthermore, this work includes the calculation of the contribution of each predictor, in order to provide a clearer understanding of the relationship between the model’s output and input. The results of this procedure showcase that all PM input stations’ concentrations have an important impact on the estimations. Considering the meteorological variables, the results for PM2.5 seem to be affected more than those for PM10, although when examining PM10 and PM2.5 individually, the wind speed and temperature contribution is on a similar level with the corresponding contribution of the available PM concentrations of the neighbouring stations.

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