Person:
Samaniego Alvarado, Esteban Patricio

Profile Picture

Email Address

Birth Date

1971-12-23

ORCID

0000-0002-8728-491X

Scopus Author ID

57201115383

Web of Science ResearcherID

Afiliación

Universidad de Cuenca, Cuenca, Ecuador
Universidad de Cuenca, Facultad de Ingeniería, Cuenca, Ecuador
Universidad de Cuenca, Departamento de Recursos Hídricos y Ciencias Ambientales, Cuenca, Ecuador

País

Ecuador

Research Projects

Organizational Units

OrgUnit Logo
Organizational Unit
Facultad de Ingeniería
La Facultad de Ingeniería, a inicios de los años 60, mediante resolución del Honorable Consejo Universitario, se formalizó la Facultad de Ingeniería de la Universidad de Cuenca, conformada por las escuelas de Ingeniería Civil y Topografía. Esta nueva estructura permitió una mayor especialización y fortalecimiento en áreas clave para el desarrollo regional. Cuenta con programas académicos reconocidos internacionalmente, que promueven y lideran actividades de investigación. Aplica un modelo educativo centrado en el estudiante y con procesos de mejora continua. Establece como prioridad una educación integra, la formación humanística es parte del programa de estudios que complementa a la sólida preparación científico-técnica. Las actividades culturales pertenecen a un programa permanente y activo al interior de nuestras dependencias, a la par de proyectos que desde el alumnado y bajo la supervisión de docentes cumplen con servicios de apoyo a nivel local y regional; promoviendo así una vinculación estrecha con la comunidad.

Job Title

Profesor (T)

Last Name

Samaniego Alvarado

First Name

Esteban Patricio

Name

Filters

2014 - 201932020 - 20234
Reset filters

Settings

Search Results

Now showing 1 - 7 of 7
  • Thumbnail Image
    Publication
    Evaluación de métodos de relleno para series temporales de precipitación y temperatura diarias: el caso de los Andes ecuatorianos
    (2014) Samaniego Alvarado, Esteban Patricio; Campozano Parra, Lenin Vladimir; Sánchez Cordero, Esteban Remigio; Avilés Añazco, Alex Manuel
    Continuous time series of precipitation and temperature considerably facilitate and improve the calibration and validation of climate and hydrologic models, used inter alia for the planning and management of earth’s water resources and for the prognosis of the possible effects of climate change on the rainfall-runoff regime of basins. The goodness-of-fit of models is among other factors dependent from the completeness of the time series data. Particular in developing countries gaps in time series data are very common. Since gaps can severely compromise data utility this research with application to the Andean Paute river basin examines the performance of 17 deterministic infill methods for completing time series of daily precipitation and mean temperature. Although sophisticated approaches for infilling gaps, such as stochastic or artificial intelligence methods exist, preference in this study was given to deterministic approaches for their robustness, easiness of implementation and computational efficiency. Results reveal that for the infilling of daily precipitation time series the weighted multiple linear regression method outperforms due to considering the ratio of the Pearson correlation coefficientto the distance, giving more weight to both, highly correlated and nearby stations. For mean temperature, the climatological mean of the day was clearly the best method, most likely due to the scarcity of weather stations measuring temperature, and because the few available stations are located at different elevations in the landscape, suggesting the need to address in future studies the impact of elevation on the interpolation.
  • Thumbnail Image
    Publication
    Comparative Study of UV Radiation Resistance and Reactivation Characteristics of E. coli ATCC 8739 and Native Strains: Implications for Water Disinfection
    (2023) Sánchez Cordero, Esteban Remigio; Samaniego Alvarado, Esteban Patricio; Duque Sarango, Paola Jackeline; Pinos Vélez, Verónica Patricia
    In certain countries where fresh water is in short supply, the effluents from wastewater treatment plants are being recycled for other uses. For quality assurance, tertiary disinfection treatments are required. This study aims to evaluate the inactivating efficacy with an ultraviolet (UV) system on fecal bacteria from effluents of urban wastewater treatment facilities and the post-treatment influence of the environmental illumination. The effect from different UV doses was determined for native and standardized lyophilized strains of Escherichia coli right after the irradiation as well as after 24 h of incubation under light or dark conditions. To achieve 3 log-reductions of the initial bacterial concentration, a UV dose of approximately 12 mJ cm−2 is needed for E. coli ATCC 8739 and native E. coli. However, there is a risk of the reactivation of 0.19% and 1.54% of the inactivated organisms, respectively, if the treated organisms are stored in an illuminated environment. This suggests that the post-treatment circumstances affect the treatment success; storing the treated water under an illuminated environment may pose a risk even if an effective inactivation was achieved during the irradiation.
  • Thumbnail Image
    Publication
    Smart grids: a multi-scale framework of analysis
    (IEEE, 2017) Espinoza Abad, Juan Leonardo; Samaniego Alvarado, Esteban Patricio; Jara Alvear, Jose Estuardo; Ochoa Tocachi, Diego Roberto; Espinoza Abad, Juan Leonardo
    Smart-grids are currently of great social and scientific interest. For certain social actors, they have generated a great deal of expectation. There are several aspects to take into account when advocating for a universal implementation of smart-grids. Here we concentrate in their possibilities regarding the co-evolution between technical integration and social interaction. We propose a framework to analyze the compromise between technical and economic efficiency on one hand and citizen participation on the other, which goes from the individual scale (a household) to the society level. In order to explore to what degree individual decisions and emergent cooperation could be coordinated with centralized decision making when the latter may imply higher technological efficiency, we analyze several case studies within the framework and explore the possibility of arriving at a power “ecosystem”.
  • Thumbnail Image
    Publication
    Local rainfall modelling based on global climate information: a data-based approach
    (2020) Mendoza Sigüenza, Daniel Emilio; Samaniego Alvarado, Esteban Patricio; Mora Serrano, Diego Esteban; Espinoza Mejía, Jorge Mauricio; Pacheco Tobar, Esteban Alonso; Avilés Añazco, Alex Manuel
    Modelling climate is complex due to multi-scale interactions and strong nonlinearities. However, climate signals are typically quasi-periodical and are likely to depend on exogenous-variables. Motivated by this insight, we propose a strategy to circumvent modelling complexity based on the following ideas. 1) The observed signals can be decomposed into non-stationary trends and quasi-periodicities through Dynamic-Harmonic-Regressions (DHR). 2) The main-frequencies and decomposed signals can be used for constructing a harmonic model with varying parameters depending on exogenous-variables. 3) The State-Dependent-Parameter (SDP) technique allows for the dynamical estimation of these parameters. The resulting DHR-SDP combined approach is applied to rainfall- monthly modelling, using global-climate signals as exogenous-variables. As a result, 1) the model yields better predictions than standard alternative techniques; 2) the model is robust regarding data limitations and useful for several-steps-ahead forecasting; 3) interesting relations between global-climate states and the local rainfall’s sea- sonality are obtained from the SDP estimated functions.
  • Thumbnail Image
    Publication
    Exploratory study of physic informed deep learning applied to a step-pool for different flow magnitudes
    (Springer Science and Business Media Deutschland GmbH, 2022) Cedillo Galarza, Juan Sebastián; Alvarado Martínez, Andrés Omar; Sánchez Cordero, Esteban Remigio; Samaniego Alvarado, Esteban Patricio
    Physical laws governing a certain phenomenon can be included in a deep-learning model within a new paradigm: the so-called physical informed deep learning (PIDL). Physical laws in hydraulics consist of partial differential equations (PDEs) resulting from balance laws. The potential use of PIDL in a step-pool reach having a complex flow and geometric characteristics is tested in this article. The studied morphology belongs to a hydraulic observatory in a mountain river in Ecuador where flow and geometric data are available. The water level profile of PIDL was compared to a stationary one-dimensional HEC-RAS model and water levels measured at three staff gauges in the reach. Saint–Venant equations, geometry data, and boundary conditions were used to implement a PIDL-based model. The chosen PIDL architecture is based on the one with the lowest value for the loss function. The resulting water level profile of the PIDL model does not have instabilities, and according to dimensionless RMSE is slightly less efficient in its predictions than the HEC RAS model. Moreover, the difference between HEC-RAS and PIDL water profile decreases as flow increases
  • Thumbnail Image
    Publication
    Effectiveness of causality-based predictor selection for statistical downscaling: a case study of rainfall in an Ecuadorian Andes basin
    (2022) Campozano Parra, Lenin Vladimir; Vázquez Patiño, Angel Oswaldo; Avilés Añazco, Alex Manuel; Samaniego Alvarado, Esteban Patricio
    Downscaling aims to take large-scale information and map it to smaller scales to reproduce local climate signals. An essential step in implementing a parsimonious downscaling model is the selection of a subset of relevant predictors that increase the simulation accuracy. According to relevant literature, predictive models that use predictors chosen through causality methods, not widely used in downscaling, are more interpretable and robust. Characteristics that do not necessarily pursue the selection methods are commonly used in machine learning (ML), where the primary objective is to maximize accuracy. This study then explores whether the improvement in interpretability and robustness of models that use causally selected predictors is not achieved at the expense of poor downscaling performance in terms of simulation accuracy. To this end, one set of three methods based on causality and one set of four standard predictor selection methods used in ML are compared, centered on the downscaling performance of rainfall in an Ecuadorian Andes basin. One causality-based selection method is based on Granger causality (GC), and two are based on a constraint-based algorithm for structure learning, namely the Peter and Clark algorithm (PCalg) and the based on PCalg and the momentary conditional independence (PCMCI). Regarding the methods used in ML, one is based on the variance of the predictors (variance threshold), two are based on univariate statistical tests (F test and mutual information), and one is based on the recursive elimination of predictors (RFE) according to their contribution to the prediction model. The number of methods in each set results from those that are considered standard in many ML libraries or have been used for regression or climatology (causality-based). The selected predictors are used to train statistical downscaling models based on the random forest and support vector machine learning algorithms to assess their performance and rank the selection methods. Among the most remarkable findings, the methods based on causality show a robust selection. For instance, the causality-based methods selected 53 different predictors in one of the considered stations, while the ML-based methods selected 73 for downscaling models with similar performance. Furthermore, when the predictors used in the best performing downscaling models are analyzed, it is shown that the different selection methods based on causality tend to choose a subset of predictors that do not differ greatly, contrary to the methods based on machine learning. This evidences a more concise selection of the causality-based methods. Finally, the evaluation metrics of the downscaling models showed that the best selection method is RFE, but the following best were consistently those based on causality (mainly PCMCI). The study evidences no significant performance detriment when using the causally selected predictors.
  • Thumbnail Image
    Publication
    Finding teleconnections from decomposed rainfall signals using dynamic harmonic regressions: a tropical andean case study
    (2019) Mendoza Sigüenza, Daniel Emilio; Samaniego Alvarado, Esteban Patricio; Mora Serrano, Diego Esteban; Espinoza Mejía, Jorge Mauricio; Campozano Parra, Lenin Vladimir
    Global climate is a multi-scale system whose subsystems interact complexly. Notably, the Tropical-Andean region has a strong rainfall variability because of the confluence of many global climate processes altered by morphological features. An approach for a synthetical climate description is the use of global indicators and their regional teleconnections. However, typically this is carried out using filters and correlations, which results in seasonal and inter-annual teleconnections information, which are difficult to integrate into a modeling framework. A new methodology, based on rainfall signal extraction using dynamic-harmonic-regressions (DHR) and stochastic-multiple-linear-regressions (SMLR) between rainfall components and global signals for searching intra-annual and inter-annual teleconnections, is proposed. DHR gives non-stationary inter-annual trends and intra-annual quasi-periodic oscillations for monthly rainfall measurements. Time-variable amplitudes of quasi-periodical oscillations are crucial for finding intra-annual teleconnections using SMLR, while trends are better suited for the case of inter-annual ones. The methodology is tested over a Tropical-Andean region in southern Ecuador. The following results were obtained: (1) trans-Niño-Index (TNI) and Tropical-South-Atlantic signals are strongly connected to inter-annual and intra-annual time-scales. (2) However, TNI progressively weakens its relation with intra-annual components; meanwhile, El-Niño-Southern-Oscillation 3 gains ground for such time-scales. (3) Finally, an inter-annual connection with the North-Atlantic-Oscillation (NAO) is revealed. These results are consistent with previous literature, although the TNI and NAO connections are interesting findings, taking into account the differences in the connected scales. These results show the methodology’s capability of unraveling global teleconnections in different space and time scales using attributes embedded in an integral mathematical framework, which could be interesting for other purposes—such as the analysis of climate mechanisms or climate modeling. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.