Abstract
We use in an explicit manner the well-known input-output methodology used in the Volterra theory to the concrete case to estimate the risks that are continuously expected due to the climatic variations in the Peruvian Northeast Coast as consequence of the arrival of phenomena such as the well-known “El Niño”. We have interpreted the Volterra series as a methodological tool to calculate probabilities of risk. Thus the resulting Volterra output is therefore seen as a type of risk’s probability by which a peripheral area of a large city might be affected by flooding. Under this view, the estimation of the risk depends entirely on the calculation of the parameters of the Volterra theory. The full estimation of the risk’s level has used a family of input functions focused on Lorentzian and Gaussian profiles. For this end we used Google images by which we have focused our attention to that populations located near to rivers that are under permanent risk in summer times. This methodology can be finally seen as a scheme for disaster anticipation. We paid attention in those zones located in Tumbes city which have been affected by river overflow along the north coast of Peru in previous summer times.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gorder, P.F.: Modeling El Niño: a force behind world weather. Comput. Sci. Eng. 7(1), 5–7 (2005)
Takahashi, K., Martínez, A.G.: The very strong coastal El Niño in 1925 in the far-eastern Pacific. Clim. Dyn. 2017, 1–27 (2017). https://doi.org/10.1007/s00382-017-3702-1
Rugh, W.J.: Nonlinear System Theory, The Volterra/Wiener Approach. Johns Hopkins University Press, Baltimore (1981)
Boyd, S., Chua, L.: Fading memory and the problem of approximating nonlinear operators with Volterra series. IEEE Trans. Circ. Syst. CAS–32(11), 1150–1161 (1985)
Boyd, S., Chua, L.O., Desoer, C.A.: Analytical foundations of Volterra series. J. Math. Control Inf. 1, 243–282 (1984)
Boyd, S.P.: Volterra series: engineering fundamentals. Ph.D. dissertation, Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA (1985)
Brockett, R.W.: Convergence of Volterra series on infinite intervals and bilinear approximations. In: Lakshmikanthan, V. (ed.) Nonlinear Systems and Applications, pp. 39–46. Academic, New York (1977)
Antzoulakos, D.: Derivation of the probability distribution functions for succession quota random variables. Ann. Inst. Stat. Math. 48(3), 551–561 (1996)
Casti, J.L.: Nonlinear System Theory. Mathematics in Science and Engineering, vol. 175. Academic, Orlando (1985)
Shaikhet, L.: Stability in probability of nonlinear stochastic Volterra difference equations with continuous variable. In: Stochastic Analysis and Applications, vol. 25, no. 6, pp. 1151–1165 (2007)
Crouch, P.E., Collingwood, P.C.: The observation space and realizations of finite Volterra series. SIAM J. Control Optim. 25(2), 316–333 (1987)
Google Maps: www.google.com/maps
Jing, X.J., Lang, Z.Q., Billings, S.A.: Magnitude bounds of generalized frequency response functions for nonlinear Volterra systems described by Narx model. Automatica 44, 838–845 (2008)
Gilbert, E.G.: Functional expansions for the response of nonlinear differential systems. IEEE Trans. Autom. Control AC-22(6), 909–921 (1977)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Nieto-Chaupis, H. (2018). Nonlinear Methodologies for Climate Studies in the Peruvian Northeast Coast. In: Orjuela-Cañón, A., Figueroa-García, J., Arias-Londoño, J. (eds) Applications of Computational Intelligence. ColCACI 2018. Communications in Computer and Information Science, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-030-03023-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-03023-0_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03022-3
Online ISBN: 978-3-030-03023-0
eBook Packages: Computer ScienceComputer Science (R0)