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Towards mathematics, computers and environment : a disasters perspective
Santos L., Negri R., de Carvalho T., Springer International Publishing, New York, NY, 2019. 258 pp.  Type: Book (978-3-030212-04-9)
Date Reviewed: Feb 16 2021

This edited collection of papers, written by scientists from Latin America, focuses on various types of quantitative and stochastic models, including their potential to “improve predictability of natural disasters,” along with various research methodologies, for example, machine learning and statistical approaches. As such, the book’s 12 papers cover numerical modeling, theory and observations, and monitoring and analysis.

It is important to mention that all of the modeling papers pay special attention to multi-scale and multi-length entities of the phenomena. In fact, a majority of the models described are coupled linear or nonlinear models targeting complex environment systems (for example, atmosphere-mean). For instance, in Part 1’s discussion of large-scale ocean-atmosphere phenomena, collectively known as ENSO, a stochastic view of ENSO is introduced. The idea arises from the hypothesis that a large, complex ocean-atmospheric system may not be unstable, but instead is driven by noise. That view leads to natural time-scale separation (slow-fast). Consequently, ENSO phenomena are shown to behave like a slow-time stochastic oscillator, and studied using a set of coupled models (some linear). However, the authors outline the uncertainties in this hypothesis caused by variability in atmosphere stochastic perturbations. They later define the dynamical regime in which the coupled system stays. Thus, the inability to distinguish between a system that is marginally stable versus one that is slightly unstable, along with the lack of long-term data, makes robust predictions of ENSO phenomena difficult.

In a similar way, when modeling frosts over Southeast South America, the variability time scales for atmospheric motion, and the consequent uncertainties of these interactions, require further data acquisition and deeper analysis.

Other presented works rely on more robust statistics (Bayesian approaches) and time-series analysis to predict natural disasters. These works complement the machine learning methods for risk assessment presented in Lima Santos et al.’s paper, and constitute a valuable conglomerate of approaches based on modeling, data, and soft computing.

Overall, the book provides a good selection of valuable papers in the area of environmental research. The main research methods used to study complex and multi-scale systems are clearly presented. The target audience includes students of sustainable development, researchers, and even science enthusiasts.

Reviewer:  Alexander Tzanov Review #: CR147188 (2106-0145)
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