Clustering approach applied on an artificial neural network model to predict PM10 in mega cities of México

A cluster-based artificial neural network model called CLASO (Classification-Assemblage-Association) has been proposed to predict the maximum of the 24-h moving average of PM10 concentration on the next day in the three largest metropolitan areas of Mexico. The model is a self-organised, real-time learning neural network, which builds its topology via a process of pattern classification by using an historical database. This process is based on a supervised clustering technique, assigning a class to each centroid of the hidden layer, employing the Euclidean distance as a hierarchical criterion. A set of ARIMA models was compared with CLASO model in the forecast performance of the 24-h average PM10 concentration on the next day. In general, CLASO model produced more accurate predictions of the maximum of the 24-h moving average of PM10 concentration than the ARIMA models, although the latter showed a minor tendency to underpredict the results. The CLASO model solely requires to be built a historical database of the air quality parameter, an initial radius of classification and the learning factor. CLASO has demonstrated acceptable predictions of 24-h average PM10 concentration by using exclusively regressive PM10 concentrations. The forecasting capabilities of the model were found to be satisfactory compared to the classical models, demonstrating its potential application to the other major pollutants used in the Mexican air quality index

Diving deeper into the underlying white shark behaviors at Guadalupe Island, Mexico

We thank grants and logistic support from Alianza WWF-Fundación Carlos Slim, Alianza WWF-Telcel, Annenberg Foundation, Pfleger Institute of Environmental Research (PIER), and Fundación Mundo Azul. HV, FGM, and RGA acknowledge support from SNI (CONACYT), and COFAA and EDI programs from Instituto Politécnico Nacional.Fine-scale movement patterns are driven by both biotic (hunting, physiological needs) and abiotic (environmental conditions) factors. The energy balance governs all movement-related strategic decisions. Marine environments can be better understood by considering the vertical component. From 24 acoustic trackings of 10 white sharks in Guadalupe Island, this study linked, for the first time, horizontal and vertical movement data and inferred six different behavioral states along with movement states, through the use of hidden Markov models, which allowed to draw a comprehensive picture of white shark behavior. Traveling was the most frequent state of behavior for white sharks, carried out mainly at night and twilight. In contrast, area-restricted searching was the least used, occurring primarily in daylight hours. Time of day, distance to shore, total shark length, and, to a lesser extent, tide phase affected behavioral states. Chumming activity reversed, in the short term and in a nonpermanent way, the behavioral pattern to a general diel vertical pattern.Publisher PDFPeer reviewe

Changes in the feeding habits of the bat ray Myliobatis californica (Gill 1865) during climatic anomalies off the west coast of the Baja California Peninsula, Mexico

The Mexican Pacific was influenced by “La Mancha” and “El Niño”, from 2014 and until 2016. The increase in sea surface temperature influenced the feeding habits of the bat ray (Myliobatis californica) in the northwest of Baja California Sur, Mexico. To evaluate possible changes in the diet, stomach content analysis and analysis of stable isotopes of carbon (δC) and nitrogen (δN) in muscle was performed during normal (2012 and 2013) and anomalous years (2014, 2015, and 2016). During the normal years, the main prey was the crab Dynomene spp. (% Prey Specific Relative Importance Index (PSIRI) = 29.3) and the stomatopod Hemisquilla californiensis (% PSIRI = 10.6). In contrast, during the anomalous years, these preys were replaced by the pelagic red crab Pleuroncodes planipes (% PSIRI = 28.5) and peanut worms Sipunculus spp. (% PSIRI = 7.9). During normal years the median isotopic values recorded were: δC = −16.2‰ and δN = 15.2‰. During the anomalous years, δC was −16.3‰ and δN was 15.1‰. Between the different periods no trophic (p (probability) > 0.05) or isotopic overlaps (p > 0.3) were found. The change in the diet of M. californica during the anomalous years is an adaptive response to the increase in water temperature caused by “La Mancha” and “El Niño”. The massive presence of P. planipes on the northwest coast of Baja California Sur is associated with the increase in water temperature, which makes P. planipes a food source for M. californica.To Instituto Politécnico Nacional by fellowships (COFAA, EDI). Also to CONACYT by the grant 253700 and IPN-SIP20160084 and 2017056

Red neuronal auto-organizada con aprendizaje en tiempo real para la predicción de la calidad del aire en base a PM10 en Villahermosa Tabasco, México

Se diseño un modelo de red neuronal artificial para la predicción al día siguiente del máximo diario de PM10, (material particulado de menos de 10 micrometros de diámetro), el cual se construye de manera dinámica mediante la formación de clusters para la clasificación de patrones y evoluciona a través de los datos que recibe automáticamente y en tiempo real. Se generó una matriz de distancias a partir de los patrones de entrada para seleccionar el radio óptimo de clasificación. El modelo fue validado mediante la aplicación de datos históricos de variables meteorológicas y de PM10 registrados en Villahermosa, Tabasco, México de 2007 a 2009. Los experimentos realizados permitieron identificar las variables relevantes del modelo y se contemplaron datos normalizados y no-normalizados. Los mejores resultados del modelo se obtuvieron usando promedios móviles y valores máximos y mínimos de PM10 no normalizados como variables de entrada así como radios cercanos al valor mínimo calculado en la matriz de distancias.Sociedad Argentina de Informática e Investigación Operativ