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Ensemble forecast spread induced by soil moisture changes over mid-south and neighbouring mid-western region of the USA

IGNACIO ARTURO QUINTANAR ISAIAS Rezaul Mahmood (2012)

This study investigated the potential impact of soil moisture perturbations on the statistical spread of an ensemble forecast for three different synoptic events during the summer of 2006. Soil moisture was perturbed from a control simulation to generate a 12 member ensemble with six drier and six moister soils. The impacts on the near-surface atmospheric conditions and on precipitation were analysed. It was found, as previous studies have confirmed, that soil moisture can change the spatial and temporal distribution of precipitation and of the overlying circulation. It was found that regardless of the conditions in synoptic forcing, temperature, relative humidity and horizontal wind field exhibited a spatial correlation coefficient (R) close to one with respect to the control simulation. Vertical velocity, however, showed a marked decrease in R down to 0.4 as the precipitation activity increased. For vertical velocity, however, this quantity grew to near 1.0 consistent with R near zero and standard deviations very close to that of the control. These results suggested a more complex picture in which soil moisture perturbations played a major role in modifying precipitation and the near-surface circulation but did not broaden the statistical spread of trajectories in phase space of all variables.

Article

CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA soil moisture ensemble forecast atmosphere-soil interactions spatial correlation pattern correlation

Ensemble forecast spread induced by soil moisture changes over mid-south and neighbouring mid-western region of the USA

IGNACIO ARTURO QUINTANAR ISAIAS Rezaul Mahmood (2012)

This study investigated the potential impact of soil moisture perturbations on the statistical spread of an ensemble forecast for three different synoptic events during the summer of 2006. Soil moisture was perturbed from a control simulation to generate a 12 member ensemble with six drier and six moister soils. The impacts on the near-surface atmospheric conditions and on precipitation were analysed. It was found, as previous studies have confirmed, that soil moisture can change the spatial and temporal distribution of precipitation and of the overlying circulation. It was found that regardless of the conditions in synoptic forcing, temperature, relative humidity and horizontal wind field exhibited a spatial correlation coefficient (R) close to one with respect to the control simulation. Vertical velocity, however, showed a marked decrease in R down to 0.4 as the precipitation activity increased. For vertical velocity, however, this quantity grew to near 1.0 consistent with R near zero and standard deviations very close to that of the control. These results suggested a more complex picture in which soil moisture perturbations played a major role in modifying precipitation and the near-surface circulation but did not broaden the statistical spread of trajectories in phase space of all variables.

Article

CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA soil moisture ensemble forecast atmosphere-soil interactions spatial correlation pattern correlation

A Predictive Model for Guillain–Barré Syndrome Based on Ensemble Methods

Juana Canul_Reich Oscar Chávez-Bosquez Betania Hernandez Ocaña (2018)

Nowadays, Machine Learning methods have proven to be highly effective on the identification of various types of diseases, in the form of predictive models. Guillain–Barré syndrome (GBS) is a potentially fatal autoimmune neurological disorder that has barely been studied with computational techniques and few predictive models have been proposed. In a previous study, single classifiers were successfully used to build a predictive model. We believe that a predictive model is imperative to carry out adequate treatment in patients promptly. We designed three classification experiments: (1) using all four GBS subtypes, (2) One versus All (OVA), and (3) One versus One (OVO). These experiments use a real-world dataset with 129 instances and 16 relevant features. Besides, we compare five state-of-the-art ensemble methods against 15 single classifiers with 30 independent runs. Standard performance measures were used to obtain the best classifier in each experiment. Derived from the experiments, we conclude that Random Forest showed the best results in four GBS subtypes classification, no ensemble method stood out over the rest in OVA classification, and single classifiers outperformed ensemble methods in most cases in OVO classification. This study presents a novel predictive model for classification of four subtypes of Guillain–Barré syndrome. Our model identifies the best method for each classification case. We expect that our model could assist specialized physicians as a support tool and also could serve as a basis to improved models in the future.

Article

CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA Guillain-Barré Ensemble Predictive model Classification

A Predictive Model for Guillain–Barré Syndrome Based on Ensemble Methods

Juana Canul_Reich Oscar Chávez-Bosquez Betania Hernandez Ocaña (2018)

Nowadays, Machine Learning methods have proven to be highly effective on the identification of various types of diseases, in the form of predictive models. Guillain–Barré syndrome (GBS) is a potentially fatal autoimmune neurological disorder that has barely been studied with computational techniques and few predictive models have been proposed. In a previous study, single classifiers were successfully used to build a predictive model. We believe that a predictive model is imperative to carry out adequate treatment in patients promptly. We designed three classification experiments: (1) using all four GBS subtypes, (2) One versus All (OVA), and (3) One versus One (OVO). These experiments use a real-world dataset with 129 instances and 16 relevant features. Besides, we compare five state-of-the-art ensemble methods against 15 single classifiers with 30 independent runs. Standard performance measures were used to obtain the best classifier in each experiment. Derived from the experiments, we conclude that Random Forest showed the best results in four GBS subtypes classification, no ensemble method stood out over the rest in OVA classification, and single classifiers outperformed ensemble methods in most cases in OVO classification. This study presents a novel predictive model for classification of four subtypes of Guillain–Barré syndrome. Our model identifies the best method for each classification case. We expect that our model could assist specialized physicians as a support tool and also could serve as a basis to improved models in the future.

Article

CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA Guillain-Barré Ensemble Predictive model Classification

General framework for class-specific feature selection

BARBARA BERENICE PINEDA BAUTISTA Jesús Ariel Carrasco Ochoa José Francisco Martínez Trinidad (2011)

Commonly, when a feature selection algorithm is applied, a single feature subset is selected for all the classes, but this subset could be inadequate for some classes. Class-specific feature selection allows selecting a possible different feature subset for each class. However, all the class-specific feature selection algorithms have been proposed for a particular classifier, which reduce their applicability. In this paper, a general framework for using any traditional feature selector for doing class-specific feature selection, which allows using any classifier, is proposed. Experimental results and a comparison against traditional feature selectors showing the suitability of the proposed framework are included.

Article

Class-specific feature selection Feature selection Supervised classification Classifier ensemble CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA MATEMÁTICAS CIENCIA DE LOS ORDENADORES CIENCIA DE LOS ORDENADORES

A multi-scale and multi-model gridded framework for forecasting crop production, risk analysis, and climate change impact studies

Vakhtang Shelia James Hansen Vaishali Sharda Cheryl Porter Pramod Aggarwal Gerrit Hoogenboom (2019)

Regional crop production forecasting is growing in importance in both, the public and private sectors to ensure food security, optimize agricultural management practices and use of resources, and anticipate market fluctuations. Thus, a model and data driven, easy-to-use forecasting and a risk assessment system can be an essential tool for end-users at different levels. This paper provides an overview of the approaches, algorithms, design, and capabilities of the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT) for gridded crop modeling and yield forecasting along with risk analysis and climate impact studies. CRAFT is a flexible and adaptable software platform designed with a user-friendly interface to produce multiple simulation scenarios, maps, and interactive visualizations using a crop engine that can run the pre-installed crop models DSSAT, APSIM, and SARRA-H, in concert with the Climate Predictability Tool (CPT) for seasonal climate forecasts. Its integrated and modular design allows for easy adaptation of the system to different regional and scientific domains. CRAFT requires gridded input data to run the crop simulations on spatial scales of 5 and 30 arc-minutes. Case studies for South Asia for two crops, including wheat and rice, shows its potential application for risk assessment and in-season yield forecasting.

Article

Crop modelling Decision support systems Yield forecasting Food security Ensemble Simulations AGRICULTURAL SCIENCES AND BIOTECHNOLOGY DECISION SUPPORT SYSTEMS YIELD FORECASTING FOOD SECURITY CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Acute leukemia classification by ensemble particle swarm model selection

Hugo Jair Escalante Balderas Manuel Montes y Gómez Jesús Antonio González Bernal María del Pilar Gómez Gil Leopoldo Altamirano Robles CARLOS ALBERTO REYES GARCIA CAROLINA RETA CASTRO ALEJANDRO ROSALES PEREZ (2012)

Objective: Acute leukemia is a malignant disease that affects a large proportion of the world population. Different types and subtypes of acute leukemia require different treatments. In order to assign the correct treatment, a physician must identify the leukemia type or subtype. Advanced and precise methods are available for identifying leukemia types, but they are very expensive and not available in most hospitals in developing countries. Thus, alternative methods have been proposed. An option explored in this paper is based on the morphological properties of bone marrow images, where features are extracted from medical images and standard machine learning techniques are used to build leukemia type classifiers. Methods and materials: This paper studies the use of ensemble particle swarm model selection (EPSMS), which is an automated tool for the selection of classification models, in the context of acute leukemia classification. EPSMS is the application of particle swarm optimization to the exploration of the search space of ensembles that can be formed by heterogeneous classification models in a machine learning toolbox. EPSMS does not require prior domain knowledge and it is able to select highly accurate classification models without user intervention. Furthermore, specific models can be used for different classification tasks. Results: We report experimental results for acute leukemia classification with real data and show that EPSMS outperformed the best results obtained using manually designed classifiers with the same data. The highest performance using EPSMS was of 97.68% for two-type classification problems and of 94.21% for more than two types problems. To the best of our knowledge, these are the best results reported for this data set. Compared with previous studies, these improvements were consistent among different type/subtype classification tasks, different features extracted from images, and different feature extraction regions. The performance improvements were statistically significant.Weimproved previous results by an average of 6% and there are improvements of more than 20% with some settings. In addition to the performance improvements, we demonstrated that no manual effort was required during acute leukemia type/subtype classification.

Conclusions: Morphological classification of acute leukemia usingEPSMSprovides an alternative to expensive diagnostic methods in developing countries. EPSMS is a highly effective method for the automated construction of ensemble classifiers for acute leukemia classification, which requires no significant user intervention. EPSMS could also be used to address other medical classification tasks.

Article

Ensemble learning Swarm optimization Full model selection Morphological classification Analysis of bone marrow cell images Acute leukemia classification CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA MATEMÁTICAS CIENCIA DE LOS ORDENADORES CIENCIA DE LOS ORDENADORES

Weather forecast sensitivity to changes in urban land covers using the WRF model for central México

ERIKA DANAE LOPEZ ESPINOZA Jorge Zavala-Hidalgo OCTAVIO GOMEZ RAMOS (2012)

The impact on temperature of the urban growth in central Mexico from 1993 to 2009 and the sensitivity of forecast to change in land cover are studied using high resolution numerical simulations. The mesoscale atmospheric Weather Research and Forecasting model (WRF) uses Global Land Cover Characteristics (GLCC) data created from NOAA-AVHRR satellite images from 1992 and 1993. However, from 1990 to 2010 the population of the country grew 29%, which represents an important increase in the extension of urban areas, particularly in the central part of the country, where the population in places like State of Mexico and Tlaxcala has grown around 34 and 33%, respectively. Due to the above, using the 2009 land use map of the Instituto Nacional de Estadística y Geografía (INEGI, by its abbreviation in Spanish), in this study an update of the 30” resolution urban coverage data used by the WRF model is performed. A sensitivity study is carried out for Mexico City and its suburbs, and for the cities of Puebla and Tlaxcala. Eight sites are analyzed where changes from vegetation cover to urban cover occur and temperature increases between 0.5 and 5.0 °C. The average of the maximum differences in temperature throughout the diurnal cycle is 2.61 °C and the mean of the differences in the whole period is 0.66 ºC. The maximum difference in temperature is registered between the 10:00 and 15:00 hours (local time). The average maximum temperature using new urban data is 26.96 °C, whereas using GLCC-1993 urban data is 25.63 ºC. The average increase in daily maximum temperature is 1.33 ºC, and for the daily minimum temperature is 0.12 ºC. The maximum temperature is reached between 13:00 and 15:00 hours, whereas the minimum temperature is reached between 4:00 and 6:00 hours. The mean daily range using new urban data is 16.0 °C whereas using GLCC-1993 data is 14.9 °C. Results show that the change from vegetal cover to urban increased the temperature in the study area

Article

CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA WRF land cover urban cover weather forecast temperature forecast Mexican Republic

Weather forecast sensitivity to changes in urban land covers using the WRF model for central México

ERIKA DANAE LOPEZ ESPINOZA Jorge Zavala-Hidalgo OCTAVIO GOMEZ RAMOS (2012)

The impact on temperature of the urban growth in central Mexico from 1993 to 2009 and the sensitivity of forecast to change in land cover are studied using high resolution numerical simulations. The mesoscale atmospheric Weather Research and Forecasting model (WRF) uses Global Land Cover Characteristics (GLCC) data created from NOAA-AVHRR satellite images from 1992 and 1993. However, from 1990 to 2010 the population of the country grew 29%, which represents an important increase in the extension of urban areas, particularly in the central part of the country, where the population in places like State of Mexico and Tlaxcala has grown around 34 and 33%, respectively. Due to the above, using the 2009 land use map of the Instituto Nacional de Estadística y Geografía (INEGI, by its abbreviation in Spanish), in this study an update of the 30” resolution urban coverage data used by the WRF model is performed. A sensitivity study is carried out for Mexico City and its suburbs, and for the cities of Puebla and Tlaxcala. Eight sites are analyzed where changes from vegetation cover to urban cover occur and temperature increases between 0.5 and 5.0 °C. The average of the maximum differences in temperature throughout the diurnal cycle is 2.61 °C and the mean of the differences in the whole period is 0.66 ºC. The maximum difference in temperature is registered between the 10:00 and 15:00 hours (local time). The average maximum temperature using new urban data is 26.96 °C, whereas using GLCC-1993 urban data is 25.63 ºC. The average increase in daily maximum temperature is 1.33 ºC, and for the daily minimum temperature is 0.12 ºC. The maximum temperature is reached between 13:00 and 15:00 hours, whereas the minimum temperature is reached between 4:00 and 6:00 hours. The mean daily range using new urban data is 16.0 °C whereas using GLCC-1993 data is 14.9 °C. Results show that the change from vegetal cover to urban increased the temperature in the study area

Article

CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA WRF land cover urban cover weather forecast temperature forecast Mexican Republic

Probabilistic description of rains and ENSO phenomenon in a coffee farm area in Veracruz, México

Jose Luis Bravo-Cabrera CARLOS GAY GARCIA ANA CECILIA CONDE ALVAREZ FRANCISCO ESTRADA PORRUA (2006)

We have analyzed 37 years of climate data to describe the behavior of the precipitation and its relation with temperature in coffee farm areas in the central part of the state of Veracruz, particularly in the municipalities of Coscomatepec and Huatusco. We analyze the tendencies of the annual averages of the precipitation. Monthly averages of the precipitation are related with monthly averages of the temperature as useful parameters to predict intense rains. Gamma distributions were adjusted to total monthly precipitation to approximate the probability of given intervals. Gumbel distributions were adjusted to daily extreme values for monthly intervals and to monthly extremes for annual intervals. The relation of the precipitation and the El Niño/Southern

Oscillation (ENSO) phenomenon is analyzed. The influence of ENSO over the precipitation was found to be significant and translated as a reduction of the midsummer drought.

Article

CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA Veracruz coffee farms precipitation trends probabilistic forecast enso influence extremeprecipitation values