Carlos Minutti

Dr. Carlos Minutti

orcid
avatar
Co-authors
avatar
Following
avatar
Followers

Research Focus & Metrics

Explore full metrics on Scilit

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
14 September 2017 in Water Resources Research
Verified
Journal Article
Closed access
PDF file
GRGustavo Ramos
Gustavo Ramos
JCJesús Carrera
Jesús Carrera
SGSusana Gómez
Susana Gómez
CMCarlos Minutti
Carlos Minutti
RCRodolfo Camacho
Rodolfo Camacho
25 August 2020 in Water Resources Research
Verified
Journal Article
Open access
PDF file
CMCarlos Minutti
Carlos Minutti
WIWalter A. Illman
Walter A. Illman
SGSusana Gomez
Susana Gomez
This study proposes a new inverse algorithm to estimate the hydraulic conductivity (K) distribution based on a Gaussian Mixture Model that significantly reduces the number of parameters to be estimated during the inversion process. Moreover, a new objective function that increases the sensitivity of parameters using the spatial derivatives of hydraulic heads is introduced, and the algorithm is further improved by including a Bayes estimator that takes advantage of different possible solutions. The developed approach is tested through multiple synthetic experiments consisting of 250 randomly generated K fields resulting in different levels of heterogeneity and the use of different number of pumping tests, with a total of 1,000 cases of two‐dimensional configuration. A large number of cases are considered to ensure that our findings and conclusions are not based on a single realization. Results revealed significant improvements to K estimates, computational time, and predictions of independently conducted tests not used in the calibration effort when compared to a geostatistical inverse approach. Overall, our results reveal that the Gaussian Mixture inversion approach is able to achieve similar or higher levels of accuracy using half of the pumping tests and 20% of the computational time compared to a geostatistical inversion approach.
21 October 2021
Verified
Book Chapter
Closed access
PDF file
CMCarlos Minutti
Carlos Minutti
MVMagali Arellano Vazquez
Magali Arellano Vazquez
MZMarlene Zamora-Machado
Marlene Zamora-Machado
30 June 2025 in Mathematical and Computational Applications
Verified
Journal Article
Open access
PDF file
CMCarlos Minutti
Carlos Minutti
Miguel Felix MataMiguel Felix Mata
Miguel Felix Mata
MVMagali Arellano Vazquez
Magali Arellano Vazquez
BEBoris Escalante-Ramirez
Boris Escalante-Ramirez
JOJimena Olveres
Jimena Olveres
This study investigates the combined effects of air pollution and socioeconomic factors on disease incidence and severity, addressing gaps in prior research that often analyzed these factors separately. Using data from 86,170 hospitalizations in Mexico City (2015–2019), we employed multivariate statistical methods (PCA and factor analysis) to construct composite measures of social and economic status and grouped correlated pollutants. Logistic and negative binomial regression models assessed their associations with hospitalization risk and frequency. Results showed that economic status significantly influenced diabetes complications, while social factors affected prenatal care-related diseases and hypertension. The PM10–PM2.5–CO group increased the incidence of asthma, influenza, and epilepsy, whereas NO2–NOx impacted diabetes complication severity and influenza. Nonlinear effects and interactions (e.g., age and weight) were also identified, highlighting the need for integrated analyses in environmental health research.
8 June 2019
Verified
Book Chapter
Closed access
PDF file
CMCarlos Minutti
Carlos Minutti
WIWalter A. Illman
Walter A. Illman
SGSusana Gomez
Susana Gomez
22 April 2025 in IEEE Latin America Transactions
Verified
Journal Article
Open access
MVMagali Arellano Vazquez
Magali Arellano Vazquez
MMMarlene Zamora Machado
Marlene Zamora Machado
MRMiguel Robles
Miguel Robles
Oscar Alfredo Jaramillo-SalgadoOscar Alfredo Jaramillo-Salgado
Oscar Alfredo Jaramillo-Salgado
CMCarlos Minutti
Carlos Minutti
Accurate detection and classification of wind states is crucial for accurately assessing and predicting wind energy production, fire spread behavior, air quality monitoring, and understanding complex meteorological phenomena. The complex nature of wind necessitates advanced data analysis techniques to extract meaningful patterns from meteorological datasets. This study presents a stochastic wind classification and identification analysis based on a Gaussian Mixture Model (GMM) clustering method applied to a case study in La Rumorosa'', Mexican California. By analyzing four meteorological variables (relative humidity, atmospheric pressure, wind speed, and wind direction) over five years, the method automatically identifies distinct wind conditions that can be defined as climate states, including well-known regional phenomena like Santa Ana winds and local orographic winds. Accurate detection of wind states enables better forecasting of wind energy potential at favorable sites, wildfire risk management through predicted fire behavior and monitoring pollutant/allergen dispersal patterns. The proposed approach offers a reliable, computationally efficient method for detecting wind patterns, extending to different geographical regions impacted by diverse wind phenomena.