123 research outputs found
Detecting low frequency cycles in rainfall series from colombian coffee-growing area by using descriptive methods
Descriptive statistical methods were used for improving climatic variability scenarios regarding rainfall using time series from five representative pluviometric stations (Miguel Valencia, Naranjal, Cenicafé, La Bella and Paraguaicito); such scenarios are used to make decisions regarding coffee-growing. The purpose was to find signs of cyclic behaviour besides those associated with El Niño Southern Oscillation (ENSO) which happens every 3.5 to 4.5 years. Signals were found of decadal to interdecadal cycle (10 to 30 years), modulating known ENSO effects related to periodic changes in the Pacific Ocean and solar activity
Detecting low frequency cycles in rainfall series from Colombian coffee-growing area by using descriptive methods
ABSTRACT Descriptive statistical methods were used for improving climatic variability scenarios regarding rainfall using time series from five representative pluviometric stations (Miguel Valencia, Naranjal, Cenicafé, La Bella and Paraguaicito); such scenarios are used to make decisions regarding coffee-growing. The purpose was to find signs of cyclic behaviour besides those associated with El Niño Southern Oscillation (ENSO) which happens every 3.5 to 4.5 years. Signals were found of decadal to interdecadal cycle (10 to 30 years), modulating known ENSO effects related to periodic changes in the Pacific Ocean and solar activity. RESUMEN Con el objeto de mejorar la generación de escenarios de variabilidad climática, que son utilizados para tomar decisiones en el cultivo del café, se exploran en forma descriptiva las series temporales de lluvia medidas en cinco estaciones pluviométricas representativas de la zona cafetera colombiana (Miguel Valencia, Naranjal, La Bella y Paraguaicito) para encontrar señales de comportamiento cíclico, además del asociado con la Oscilación del Sur El Niño (ENSO), que es de 3.5 a 4.5 años. Se encontraron señales de ciclos de periodo decadal a interdecadal (10 a 30 años) que modulan los efectos ya conocidos del ENSO y que están asociados a cambios periódicos en el Océano Pacífico y la actividad solar. Palabras claves: El Niño Oscilación del Sur (ENOS), Oscilación Decadal del Pacífico (ODP), series de tiempo, semivariograma
Early warning system for coffee rust disease based on error correcting output codes: a proposal
Colombian coffee producers have had to face the severe consequences of the coffee rust disease since it was first reported in the country in 1983. Recently, machine learning researchers have tried to predict infection through classifiers such as decision trees, regression Support Vector Machines (SVM), non-deterministic classifiers and Bayesian Networks, but it has been theoretically and empirically demonstrated that combining multiple classifiers can substantially improve the classification performance of the constituent members. An Early Warning System (EWS) for coffee rust disease was therefore proposed based on Error Correcting Output Codes (ECOC) and SVM to compute the binary functions of Plant Density, Shadow Level, Soil Acidity, Last Nighttime Rainfall Intensity and Last Days Relative Humidity.Los productores de café colombianos han sufrido severas consecuencias por la Roya desde que fue reportada por primera vez en el país en el año 1983. Recientemente, investigadores de aprendizaje automático han intentado predecir la roya a través de clasificadores como: arboles de decisión, máquinas de vector de soporte, clasificadores no determinísticos y redes bayesianas, pero se ha demostrado teórica y empíricamente que la combinación de múltiples clasificadores puede mejorar sustancialmente el rendimiento en la clasificación. En este sentido es propuesto un sistema de alerta temprana para la roya en el café, basado en códigos de salida de corrección de error y máquinas de vector de soporte para calcular las funciones binarias de la densidad de planta, el nivel de sombra, la acidez del suelo, la intensidad de lluvia en la última noche, y en últimos días, con humedad relativa
Sistema de alerta temprana para la roya en el café basado en códigos de salida de corrección de error: una propuesta
Colombian coffee producers have had to face the severe consequences of the coffee rust disease since it was first reported in the country in 1983. Recently, machine learning researchers have tried to predict infection through classifiers such as decision trees, regression Support Vector Machines (SVM), non-deterministic classifiers and Bayesian Networks, but it has been theoretically and empirically demonstrated that combining multiple classifiers can substantially improve the classification performance of the constituent members. An Early Warning System (EWS) for coffee rust disease was therefore proposed based on Error Correcting Output Codes (ECOC) and SVM to compute the binary functions of Plant Density, Shadow Level, Soil Acidity, Last Nighttime Rainfall Intensity and Last Days Relative Humidity.Los productores de café colombianos han sufrido severas consecuencias por la Roya desde que fue reportada por primera vez en el país en el año 1983. Recientemente, investigadores de aprendizaje automático han intentado predecir la roya a través de clasificadores como: arboles de decisión, máquinas de vector de soporte, clasificadores no determinísticos y redes bayesianas, pero se ha demostrado teórica y empíricamente que la combinación de múltiples clasificadores puede mejorar sustancialmente el rendimiento en la clasificación. En este sentido es propuesto un sistema de alerta temprana para la roya en el café, basado en códigos de salida de corrección de error y máquinas de vector de soporte para calcular las funciones binarias de la densidad de planta, el nivel de sombra, la acidez del suelo, la intensidad de lluvia en la última noche, y en últimos días, con humedad relativa
Analyzing climate indices to predict the monthly rainfall in an agricultural region of the northern Andes (Caldas, Colombia)
Several authors have established that different indicators which characterize oceanic and atmospheric phenomena overseas, are related to the behavior of weather and climate in the Colombian Andean region. However, the application of this knowledge is almost the uncertainty represented by the scarce meteorological information available to explain event or climate anomalies of low frequency
and the established conception that the El Niño Oceanic Index (ONI) is the only index capable of describing the variation in the values of elements of climate on a monthly scale. This paper aims toreduce the above-mentioned uncertainties by means of a comparative study study to determine the spatio-temporal suitability of different climate indicator and modulators in an agricultural region in the northern Andes. The results show a better correspondence between the region’s precipitation and the figures that characterize El Niño-Southern Oscillation (ENSO), which are up to three month behind schedule. However, ONI is not always the best predictor of monthly rainfall. Therefore, one might think that some other indices could be successfully used to forecast rainfall for most months in the northern Andes agricultural areas
Un nuevo conjunto de datos para la detección de roya en cultivos de café Colombianos basado en clasificadores
Coffee production is the main agricultural activity in Colombia. More than 350.000 Colombian families depend on coffee harvest. Since coffee rust disease was first reported in the country in 1983, these families have had to face severe consequences. Recently, machine learning approaches have built a dataset for monitoring coffee rust incidence that involves weather conditions and physic crop properties. This background encouraged us to build a dataset for coffee rust detection in Colombian crops through data mining process as Cross Industry Standard Process for Data Mining (CRISP-DM). In this paper we define a proper data to generate accurate models; once the dataset is built, this is tested using classifiers as: Support Vector Regression, Backpropagation Neural Networks and Regression Trees.La producción de café es la principal actividad agrícola en Colombia. Más de 350.000 familias colombianas dependen de la cosecha de café. En este sentido, la roya fue reportada por primera vez en el país en 1983, y desde entonces estas familias han tenido que enfrentar graves consecuencias. Recientemente, diversos enfoques basados en aprendizaje automático han construido un conjunto de datos para el monitoreo de la incidencia de la roya del café, teniendo en cuenta las condiciones climáticas y las propiedades físicas de los cultivos. Estas investigaciones motivaron la creación de un conjunto de datos para la detección de la roya en cultivos Colombianos a través del proceso de minería de datos CRISP-DM. En este trabajo se definió un conjunto de datos con el objetivo de generar clasificadores precisos; una vez construido el conjunto de datos, fue probado mediante tres clasificadores: Maquinas de vector de regresión, Redes neuronales con propagación hacia atrás y Árboles de regresión
Trend analysis to determine hazards related to climate change in the andean agricultural areas of cundinamarca and boyacá
Recognizing the threat from climate change that is facing and will face agro ecosystems is the first step in determining adaptation to climate change. One way is through Global Climate Models (GCMs), but their spatial resolution is not best suited for making decisions locally, further reducing scale, seen as a way to resolve the resolution problem, has not yielded the expected results. This study puts forth an exercise in which we study the climatic time series of precipitation and temperature to determine if there are effects of climate change on one of the most important national agricultural areas, using the Mann-Kendall analysis to determine the existence of statistically significant trends, i.e. signs of change in the variables analyzed. It was found that the variable that presents the most significant trends is the average maximum temperature, while precipitation and average minimum temperature do not
Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo
Despite the growing number of confident binary black hole coalescences
observed through gravitational waves so far, the astrophysical origin of these
binaries remains uncertain. Orbital eccentricity is one of the clearest tracers
of binary formation channels. Identifying binary eccentricity, however, remains
challenging due to the limited availability of gravitational waveforms that
include effects of eccentricity. Here, we present observational results for a
waveform-independent search sensitive to eccentric black hole coalescences,
covering the third observing run (O3) of the LIGO and Virgo detectors. We
identified no new high-significance candidates beyond those that were already
identified with searches focusing on quasi-circular binaries. We determine the
sensitivity of our search to high-mass (total mass ) binaries
covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to
compare model predictions to search results. Assuming all detections are indeed
quasi-circular, for our fiducial population model, we place an upper limit for
the merger rate density of high-mass binaries with eccentricities at Gpc yr at 90\% confidence level.Comment: 24 pages, 5 figure
Open data from the third observing run of LIGO, Virgo, KAGRA and GEO
The global network of gravitational-wave observatories now includes five
detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600.
These detectors collected data during their third observing run, O3, composed
of three phases: O3a starting in April of 2019 and lasting six months, O3b
starting in November of 2019 and lasting five months, and O3GK starting in
April of 2020 and lasting 2 weeks. In this paper we describe these data and
various other science products that can be freely accessed through the
Gravitational Wave Open Science Center at https://gwosc.org. The main dataset,
consisting of the gravitational-wave strain time series that contains the
astrophysical signals, is released together with supporting data useful for
their analysis and documentation, tutorials, as well as analysis software
packages.Comment: 27 pages, 3 figure
Model-based cross-correlation search for gravitational waves from the low-mass X-ray binary Scorpius X-1 in LIGO O3 data
We present the results of a model-based search for continuous gravitational waves from the low-mass X-ray binary Scorpius X-1 using LIGO detector data from the third observing run of Advanced LIGO, Advanced Virgo and KAGRA. This is a semicoherent search which uses details of the signal model to coherently combine data separated by less than a specified coherence time, which can be adjusted to balance sensitivity with computing cost. The search covered a range of gravitational-wave frequencies from 25Hz to 1600Hz, as well as ranges in orbital speed, frequency and phase determined from observational constraints. No significant detection candidates were found, and upper limits were set as a function of frequency. The most stringent limits, between 100Hz and 200Hz, correspond to an amplitude h0 of about 1e-25 when marginalized isotropically over the unknown inclination angle of the neutron star's rotation axis, or less than 4e-26 assuming the optimal orientation. The sensitivity of this search is now probing amplitudes predicted by models of torque balance equilibrium. For the usual conservative model assuming accretion at the surface of the neutron star, our isotropically-marginalized upper limits are close to the predicted amplitude from about 70Hz to 100Hz; the limits assuming the neutron star spin is aligned with the most likely orbital angular momentum are below the conservative torque balance predictions from 40Hz to 200Hz. Assuming a broader range of accretion models, our direct limits on gravitational-wave amplitude delve into the relevant parameter space over a wide range of frequencies, to 500Hz or more
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