Browsing by Author "Ochoa Sanchez, Ana Elizabeth"

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Forecasting of a complex phenomenon using stochastic data-based techniques under non-conventional schemes: the SARS-CoV-2 virus spread case
(2022) Guaman Mendoza, Daniel Eduardo; Ochoa Sanchez, Ana Elizabeth; Samaniego Alvarado, Esteban Patricio
Epidemics are complex dynamical processes that are difficult to model. As revealed by the SARS-CoV-2 pandemic, the social behavior and policy decisions contribute to the rapidly changing behavior of the virus' spread during outbreaks and recessions. In practice, reliable forecasting estimations are needed, especially during early contagion stages when knowledge and data are insipient. When stochastic models are used to address the problem, it is necessary to consider new modeling strategies. Such strategies should aim to predict the different contagious phases and fast changes between recessions and outbreaks. At the same time, it is desirable to take advantage of existing modeling frameworks, knowledge and tools. In that line, we take Autoregressive models with exogenous variables (ARX) and Vector autoregressive (VAR) techniques as a basis. We then consider analogies with epidemic's differential equations to define the structure of the models. To predict recessions and outbreaks, the possibility of updating the model's parameters and stochastic structures is considered, providing non-stationarity properties and flexibility for accommodating the incoming data to the models. The Generalized-Random-Walk (GRW) and the State-Dependent-Parameter (SDP) techniques shape the parameters' variability. The stochastic structures are identified following the Akaike (AIC) criterion. The models use the daily rates of infected, death, and healed individuals, which are the most common and accurate data retrieved in the early stages. Additionally, different experiments aim to explore the individual and complementary role of these variables. The results show that although both the ARX-based and VAR-based techniques have good statistical accuracy for seven-day ahead predictions, some ARX models can anticipate outbreaks and recessions. We argue that short-time predictions for complex problems could be attained through stochastic models that mimic the fundamentals of dynamic equations, updating their parameters and structures according to incoming data.
Quantification of rainfall interception in the high Andean tussock grasslands
(2018) Ochoa Sanchez, Ana Elizabeth; Célleri Alvear, Rolando Enrique; Crespo Sánchez, Patricio Javier
The páramo ecosystem provides most of the water for the tropical Andean highlands in South America. Although the comprehension of this environment has increased lately, there remains an urgent need to quantify the processes involved in the hydrological cycle. Interception loss (IL) is one of the least studied processes in the páramo, and more generally, in grasslands globally. The main objective of this study was to quantify IL at event scale by estimating it indirectly from precipitation (P) and effective rainfall (ER). Furthermore, the following questions were assessed(a) how much of the P becomes ER?(b) what is the impact on IL calculations of using a rain gauge instead of a disdrometer?(c) which meteorological variables are related to the IL process? and (d) is it possible to estimate IL from meteorological variables? High percentages of IL in relation to P were found (10 …