Predicting WNV circulation in Italy using earth observation data and extreme gradient boosting model

West Nile Disease (WND) is one of the most spread zoonosis in Italy and Europe caused by a vector-borne virus. Its transmission cycle is well understood, with birds acting as the primary hosts and mosquito vectors transmitting the virus to other birds, while humans and horses are occasional dead-end hosts. Identifying suitable environmental conditions across large areas containing multiple species of potential hosts and vectors can be difficult. The recent and massive availability of Earth Observation data and the continuous development of innovative Machine Learning methods can contribute to automatically identify patterns in big datasets and to make highly accurate identification of areas at risk. In this paper, we investigated the West Nile Virus (WNV) circulation in relation to Land Surface Temperature, Normalized Difference Vegetation Index and Surface Soil Moisture collected during the 160 days before the infection took place, with the aim of evaluating the predictive capacity of lagged remotely sensed variables in the identification of areas at risk for WNV circulation. WNV detection in mosquitoes, birds and horses in 2017, 2018 and 2019, has been collected from the National Information System for Animal Disease Notification. An Extreme Gradient Boosting model was trained with data from 2017 and 2018 and tested for the 2019 epidemic, predicting the spatio-temporal WNV circulation two weeks in advance with an overall accuracy of 0.84. This work lays the basis for a future early warning system that could alert public authorities when climatic and environmental conditions become favourable to the onset and spread of WNV

Density gradient measurement of a premixed hydrogen-air flame with Background Oriented Schlieren technique

In recent years, hydrogen is experiencing renewed attention because of its capability to replace fossil fuels in traditional combustion-based technologies. Hydrogen-air flames are characterized by low emission in the visible spectrum and thus are particularly difficult to be visualized. In the present study, Background Oriented Schlieren (BOS) is used to visualize a premixed hydrogen-air flame and images acquired during the experimental campaign are analysed by a cross-correlation algorithm to obtain a displacement map induced by the flame presence. Further studies will be carried out to determine the density gradient and the temperature distribution

Neural networks for the dimensionality reduction of GOME measurement vector in the estimation of ozone profiles

Dimensionality reduction can be of crucial importance in the application of inversion schemes to atmospheric remote sensing data. In this study the problem of dimensionality reduction in the retrieval of ozone concentration profiles from the radiance measurements provided by the instrument Global Ozone Monitoring Experiment (GOME) on board of ESA satellite ERS-2 is considered. By means of radiative transfer modelling, neural networks and pruning algorithms, a complete procedure has been designed to extract the GOME spectral ranges most crucial for the inversion. The quality of the resulting retrieval algorithm has been evaluated by comparing its performance to that yielded by other schemes and co-located profiles obtained with lidar measurements

Gome ozone profiles retrieved by neural network techniques: A global validation with lidar measurements

Ozone profiles retrieved from Global Ozone Monitoring Experiment (GOME, flying on ERS-2 satellite) spectra from July 1995 to June 2003 by means of 2 independent neural network (NN) schemes have been validated with ozone lidar measurements performed at different stations belonging to the network for the detection of atmospheric composition changes (NDACC). The retrieval and the whole validation have been carried out by using the performances and resources of the European project Enabling Grid for E-sciencE (EGEE) and of a local Grid at the European Space Research Institute of the European Space Agency (ESRIN/ESA). Roughly 1800 collocated profiles have been found, in tropical, mid-latitude and high-latitude regions; for each lidar station the differences between GOME and lidar profiles have been evaluated and the global performance of the proposed NN approaches has been critically discussed. The results indicate the potentialities for obtaining reliable ozone field analysis on global scale, including detailed altitude resolved trend analysis