Abstract
In recent years the explosion in high-performance computing systems and high-capacity storage has led to an exponential increase in the amount of information, generating the phenomenon of big data and the development of automatic processing models like machine learning analysis. In this paper a machine learning time series analysis was experimentally developed in relation to the paroxysmal meteorological event “cloudburst” characterized by a very intense storm, concentrated in a few hours and highly localized. These extreme phenomena such as hail, overflows and sudden floods are found in both urban and rural areas. The predictability over time of these phenomena is very short and depends on the event considered, therefore it is useful to add data driven methods to the deterministic modeling tools to get the anticipated predictability of the event, also known as nowcasting. The detailed knowledge of these phenomena, together with the development of simulation models for the propagation of cloudbursts, can be a useful tool for monitoring and mitigating risk in civil protection contingency plans.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Simas, F., Barros, R., Salvador, L., Weber, M., Amorim, S.: A data exchange tool based on ontology for emergency response systems. In: Garoufallou, E., Virkus, S., Siatri, R., Koutsomiha, D. (eds.) MTSR 2017. CCIS, vol. 755, pp. 74–79. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70863-8_7
Li, J., Liu, L., Le Duy, T., Liu, J.: Accurate data-driven prediction does not mean high reproducibility. Nat. Mach. Intell. (2020). https://doi.org/10.1038/s42256-019-0140-2
Almanacco della Scienza CNR, n. 9 (2017)
Rezzani A.: Big Data. Maggioli editore (2013)
Wan, Z., Vlachas, P., Koumoutsakos, P., Sapis, T.: Data assisted reduced-order modeling of extreme events in complex dynamical systems. Plos One 13, e0197704 (2018)
Ozdemir, S.: Data Science, Apogeo (2018)
Virkus, S., Garoufallou, E.: Data science from a perspective of computer science. In: Garoufallou, E., Fallucchi, F., William De Luca, E. (eds.) MTSR 2019. CCIS, vol. 1057, pp. 209–219. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36599-8_19
Zschocke, T., Villagrán de León, J.C., Beniest, J.: Enriching the description of learning resources on disaster risk reduction in the agricultural domain: an ontological approach. In: Sánchez-Alonso, S., Athanasiadis, Ioannis N. (eds.) MTSR 2010. CCIS, vol. 108, pp. 320–330. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16552-8_29
Hosni, H., Vulpiani, A.: Forecasting in light of big data. Physics 31, 557–569 (2017)
Gründer-Fahrer, S., Schlaf, A., Wustmann, S.: How social media text analysis can inform disaster management. In: Rehm, G., Declerck, T. (eds.) GSCL 2017. LNCS (LNAI), vol. 10713, pp. 199–207. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73706-5_17
Pathak, J., et al.: Hybrid forecasting of chaotic processes: using machine learning in conjunction with a knowledge-based model. American Institute of Physics (2018)
Mandrioli, D.: Paola Spoletini P.: Informatica Teorica. CittàStudi edizioni (2011)
Melucci, M.: Information retrieval. Franco Angeli (2013)
Russell, S., Norvig, P.: Intelligenza Artificiale, vol. 1-2. Pearson, London (2010)
Sipser, M.: Introduzione alla teoria della computazione, Maggioli Editore (2016)
Fallucchi, F., Tarquini, M., De Luca, E.W.: Knowledge management for the support of logistics during humanitarian assistance and disaster relief (HADR). In: Díaz, P., Bellamine Ben Saoud, N., Dugdale, J., Hanachi, C. (eds.) ISCRAM-med 2016. LNBIP, vol. 265, pp. 226–233. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47093-1_19
David, S., Hrubes, P., Moran, S., Shipilka, A., Yehudayoff, A.: Learnability can be undecidable. Nat. Mach. Intell. 1, 44–48 (2019)
Raschka, S.: Machine Learning con Python. Apogeo (2015)
Libelli, S.M.: Modelli matematici per l’ecologia. Pitagora editrice (1989)
Banbura, M., Giannone, D., Modugno, M., Reichlin, L.: Now-casting and the real-time data flow. European central bank (2013)
Santos, L., Sicilia, M., Padrino, S.: Ontologies for emergency response: effect-based assessment as the main ontological commitment. MTSR, 93-104 (2011)
Comincioli, V.: Problemi e modelli matematici nelle scienze applicate. Casa editrice Ambrosiana (1993)
Report annuario dei dati ambientali. ISPRA (2017)
Cammarata, S.: Reti neuronali. Dal Perceptron alle reti caotiche e neurofuzzy. Etas Libri (1997)
Cusani, R., Inzerilli, T.: Teoria dell’Informazione e Codici. Ed. Ingegneria 2000 (2008)
Bergomi, M.G., Frosini, P., Giorgi, D., Quercioli, N.: Towards a topologicalgeometrical theory of group equivariant non-expansive operators for data analysis and machine learning. Nat. Mach. Intell. 1(9), 423–433 (2019)
Hickey, J.: Using machine learning to “Nowcast” precipitation in high resolution. Google AI Blog (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest. Authors declare that there are no conflicts of interest regarding the publication of this paper.
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Fallucchi, F., Scano, R., De Luca, E.W. (2021). Machine Learning Models Applied to Weather Series Analysis. In: Garoufallou, E., Ovalle-Perandones, MA. (eds) Metadata and Semantic Research. MTSR 2020. Communications in Computer and Information Science, vol 1355. Springer, Cham. https://doi.org/10.1007/978-3-030-71903-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-71903-6_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71902-9
Online ISBN: 978-3-030-71903-6
eBook Packages: Computer ScienceComputer Science (R0)