RESUMO
Machine Learning (ML) techniques can be used to convert Big Data into valuable information for agri-environmental applications, such as predictive pest modeling. Lobesia botrana (Denis & Schiffermüller) 1775 (Lepidoptera: Tortricidae) is one of the main pests of grapevine, causing high productivity losses in some vineyards worldwide. This work focuses on the optimization of the Touzeau model, a classical correlation model between temperature and L. botrana development using data-driven models. Data collected from field observations were combined with 30 GB of registered weather data updated every 30 min to train the ML models and make predictions on this pest's flights, as well as to assess the accuracy of both Touzeau and ML models. The results obtained highlight a much higher F1 score of the ML models in comparison with the Touzeau model. The best-performing model was an artificial neural network of four layers, which considered several variables together and not only the temperature, taking advantage of the ability of ML models to find relationships in nonlinear systems. Despite the room for improvement of artificial intelligence-based models, the process and results presented herein highlight the benefits of ML applied to agricultural pest management strategies.
RESUMO
Air pollution due to the presence of small particles and gases in the atmosphere is a major cause of health problems. In urban areas, where most of the population is concentrated, traffic is a major source of air pollutants (such as nitrogen oxides or NO x and carbon monoxide or CO). Therefore, for smart cities, carrying out an adequate traffic monitoring is a key issue, since it can help citizens to make better decisions and public administrations to define appropriate policies. Thus, citizens could use these data to make appropriate mobility decisions. In the same way, a city council can exploit the collected data for traffic management and for the establishment of suitable traffic policies throughout the city, such as restricting the traffic flow in certain areas. For this purpose, a suitable modelling approach that provides the estimated/predicted values of pollutants at each location is needed. In this paper, an approach followed to model traffic flow and air pollution dispersion in the city of Zaragoza (Spain) is described. Our goal is to estimate the air quality in different areas of the city, to raise awareness and help citizens to make better decisions; for this purpose, traffic data play an important role. In more detail, the proposal presented includes a traffic modelling approach to estimate and predict the amount of traffic at each road segment and hour, by combining historical measurements of real traffic of vehicles and the use of the SUMO traffic simulator on real city roadmaps, along with the application of a trajectory generation strategy that complements the functionalities of SUMO (for example, SUMO's calibrators). Furthermore, a pollution modelling approach is also provided, to estimate the impact of traffic flows in terms of pollutants in the atmosphere: an R package called Vehicular Emissions INventories (VEIN) is used to estimate the amount of NO x generated by the traffic flows by taking into account the vehicular fleet composition (i.e., the types of vehicles, their size and the type of fuel they use) of the studied area. Finally, considering this estimation of NO x , a service capable of offering maps with the prediction of the dispersion of these atmospheric pollutants in the air has been established, which uses the Graz Lagrangian Model (GRAL) and takes into account the meteorological conditions and morphology of the city. The results obtained in the experimental evaluation of the proposal indicate a good accuracy in the modelling of traffic flows, whereas the comparison of the prediction of air pollutants with real measurements shows a general underestimation, due to some limitations of the input data considered. In any case, the results indicate that this first approach can be used for forecasting the air pollution within the city.