@article{1448,
  keywords = {Air quality, ambient air, artificial neural network, concentration (composition), early warning system, learning, methodology, monitoring system, numerical model, Particulate Matter, prediction, real time, spatiotemporal analysis, Italy},
  author = {E. Chianese and F. Camastra and A. Ciaramella and T.C. Landi and A. Staiano and A. Riccio},
  title = {Spatio-temporal learning in predicting ambient particulate matter concentration by multi-layer perceptron},
  year = {2019},
  journal = {Ecological Informatics},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058133403&doi=10.1016%2fj.ecoinf.2018.12.001&partnerID=40&md5=8d57f3944344272db464b27ecca3a4a3},
  doi = {10.1016/j.ecoinf.2018.12.001},
}