Abstract
Weather forecasting is a logical process that consists in evaluating the predictions provided by a set of stochastic models, compare these and take a conclusion about the weather in a given area and a given interval of time. Meteorological forecasting provides reliable predictions about the weather within a given interval of time. The automation of the forecasting process would be helpful in a number of contexts. For instance, when forecasting about underpopulated or small geographic areas is out of the human forecasters’ tasks but is central, e.g., for tourism. In this paper, we start to deal with these challenging tasks by developing a defeasible reasoner for meteorological forecasting, which we evaluate against a real-world example with applications to tourism and holiday planning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
An extended version of this work is available online at https://arxiv.org/abs/1901.09867.
References
Agresta, A., et al.: An ontology framework for flooding forecasting. In: Murgante, B., et al. (eds.) ICCSA 2014. LNCS, vol. 8582, pp. 417–428. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09147-1_30
Aschieri, F., Zorzi, M.: Non-determinism, non-termination and the strong normalization of system T. In: Hasegawa, M. (ed.) TLCA 2013. LNCS, vol. 7941, pp. 31–47. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38946-7_5
Aschieri, F., Zorzi, M.: On natural deduction in classical first-order logic: Curry-Howard correspondence, strong normalization and Herbrand’s theorem. Theor. Comput. Sci. 625, 125–146 (2016)
Carr III, L., Elsberry, R., Peak, J.: Beta test of the systematic approach expert system prototype as a tropical cyclone track forecasting aid. Weather Forecast. 16(3), 355–368 (2001)
Conway, B.: Expert systems and weather forecasting. Meteorol. Mag. 118(1399), 23–30 (1989)
Cristani, M., Domenichini, F., Olivieri, F., Tomazzoli, C., Zorzi, M.: It could rain: weather forecasting as a reasoning process. In: Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference KES-2018, Procedia Computer Science, vol. 126, pp. 850–859 (2018)
Cristani, M., Olivieri, F., Tomazzoli, C., Zorzi, M.: Towards a logical framework for diagnostic reasoning. In: Jezic, G., Chen-Burger, Y.-H.J., Howlett, R.J., Jain, L.C., Vlacic, L., Šperka, R. (eds.) KES-AMSTA-18 2018. SIST, vol. 96, pp. 144–155. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-92031-3_14
Cristani, M., Tomazzoli, C., Karafili, E., Olivieri, F.: Defeasible reasoning about electric consumptions. In: 30th IEEE International Conference on Advanced Information Networking and Applications, AINA 2016, pp. 885–892 (2016)
Desmarais, M.C., de Verteuil, F., Zwack, P., Jacob, D.: Stratus: a prototype expert advisory system for terminal weather forecasting, pp. 150–154 (1990)
Domańska, D., Wojtylak, M.: Application of fuzzy time series models for forecasting pollution concentrations. Expert Syst. Appl. 39(9), 7673–7679 (2012)
Goldberg, E.: FoG. Synthesizing forecast text directly from weather maps, pp. 156–162 (1993)
Governatori, G., Olivieri, F., Calardo, E., Rotolo, A., Cristani, M.: Sequence semantics for normative agents. In: Baldoni, M., Chopra, A.K., Son, T.C., Hirayama, K., Torroni, P. (eds.) PRIMA 2016. LNCS (LNAI), vol. 9862, pp. 230–246. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44832-9_14
Governatori, G., Olivieri, F., Scannapieco, S., Cristani, M.: The hardness of revising defeasible preferences. In: Bikakis, A., Fodor, P., Roman, D. (eds.) RuleML 2014. LNCS, vol. 8620, pp. 168–177. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09870-8_12
Governatori, G., Olivieri, F., Scannapieco, S., Rotolo, A., Cristani, M.: The rationale behind the concept of goal. Theory Pract. Log. Program. 16(3), 296–324 (2016)
Kulkarni, A., Mukhopadhyay, D.: Internet of things based weather forecast monitori ng system. IJEEI 9(3), 555–557 (2018)
Masini, A., Viganò, L., Zorzi, M.: Modal deduction systems for quantum state transformations. J. Mult.-valued Log. Soft Comput. 17(5–6), 475–519 (2011)
Moninger, W.: Shootout-89, an evaluation of knowledge-based weather forecasting systems. Mach. Intell. Pattern Recogn. 10(C), 457–458 (1990)
Olivieri, F., Governatori, G., Scannapieco, S., Cristani, M.: Compliant business process design by declarative specifications. In: Boella, G., Elkind, E., Savarimuthu, B.T.R., Dignum, F., Purvis, M.K. (eds.) PRIMA 2013. LNCS (LNAI), vol. 8291, pp. 213–228. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-44927-7_15
Prawitz, D.: Ideas and Results in Proof Theory. Studies in Logic and the Foundations of Mathematics, vol. 63, pp. 235–307. Elsevier, Amsterdam (1971)
Tomazzoli, C., Cristani, M., Karafili, E., Olivieri, F.: Non-monotonic reasoning rules for energy efficiency. J. Ambient Intell. Smart Environ. 9(3), 345–360 (2017)
Viganò, L., Volpe, M., Zorzi, M.: Quantum state transformations and branching distributed temporal logic. In: Kohlenbach, U., Barceló, P., de Queiroz, R. (eds.) WoLLIC 2014. LNCS, vol. 8652, pp. 1–19. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44145-9_1
Viganò, L., Volpe, M., Zorzi, M.: A branching distributed temporal logic for reasoning about entanglement-free quantum state transformations. Inf. Comput. 255, 311–333 (2017)
Cristani, M., Bertolaso, A., Scannapieco, S., Tomazzoli, C.: Future paradigms of automated processing of business documents. Int. J. Inf. Manag. 40, 47–65 (2019)
Zorzi, M., Combi, C., Lora, R., Pagliarini, M., Moretti, U.: Automagically encoding adverse drug reactions in MedDRA. In: 2015 International Conference on Healthcare Informatics, ICHI 2015, Dallas, TX, USA, 21–23 October 2015, pp. 90–99. IEEE Computer Society (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Cristani, M., Domenichini, F., Tomazzoli, C., Zorzi, M. (2019). “It Could Be Worse, It Could Be Raining”: Reliable Automatic Meteorological Forecasting for Holiday Planning. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-22999-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22998-6
Online ISBN: 978-3-030-22999-3
eBook Packages: Computer ScienceComputer Science (R0)