Automatic Detection of Expanding HI Shells Using Artificial Neural Networks

The identification of expanding HI shells is difficult because of their variable morphological characteristics. The detection of HI bubbles on a global scale therefore never has been attempted. In this paper, an automatic detector for expanding HI shells is presented. The detection is based on the more stable dynamical characteristics of expanding shells and is performed in two stages. The first one is the recognition of the dynamical signature of an expanding bubble in the velocity spectra, based on the classification of an artificial neural network. The pixels associated with these recognized spectra are identified on each velocity channel. The second stage consists in looking for concentrations of those pixels that were firstly pointed out, and to decide if they are potential detections by morphological and 21-cm emission variation considerations. Two test bubbles are correctly detected and a potentially new case of shell that is visually very convincing is discovered. About 0.6% of the surveyed pixels are identified as part of a bubble. These may be false detections, but still constitute regions of space with high probability of finding an expanding shell. The subsequent search field is thus significantly reduced. We intend to conduct in the near future a large scale HI shells detection over the Perseus Arm using our detector.Comment: 39 pages, 11 figures, accepted by PAS

Optimization methodology for a river temperature monitoring network for the characterization of fish thermal habitat

A methodology for planning an optimized river water temperature monitoring network is presented. The methodology is based on sampling of the physio-climatic variability of the region to be monitored. Physio-climatic metrics are selected to describe the study region, based on principal component analysis. The sites to be monitored are then identified based on a k-means clustering in the multidimensional space defined by the selected metrics. The methodology is validated on an existing dense water temperature network in Haute-Savoie, France. Different configurations of more or less dense network scenarios are evaluated by assessing their ability to estimate water temperature indices at ungauged locations. An optimized network containing 83 sites is found to provide satisfactory estimations for seven ecologically and biologically meaningful thermal indices defined to characterize brown trout thermal habitat

Modeling of the thermal regime of rivers subject to seasonal ice cover using data from different sources and temporal resolutions

A comprehensive picture of the spatial and temporal patterns of river thermal regimes requires temperature recorded over continuous long time series and across various environments. Unfortunately, these data are generally scarce in extended areas. In Canada, the first attempt to a general large-scale characterization of river thermal regimes was done using a standardized three-parameter Gaussian function and continuous temperature records collected in 158 Québec rivers. This model provided estimates of the river temperature annual maximum, the date of the annual maximum occurrence and the duration of the warm season, with confidence intervals linked to the duration of the available time series. This resulting thermal map was however limited spatially by the geographical location of the monitoring stations, restricted to the eastern portion of the province. It was also based on relatively short and recent temperature series, with most records shorter than five years and starting after 2010. In this work, we expanded both the space and time spans of the Québec rivers thermal map by adding new temperature data sources, namely satellite thermal data and spot measurements. Satellite data provided thermal information in remote northern regions where in situ data acquisition is difficult, from early 1980s until today. Spot measurements from the Banque de données sur la qualité des milieux aquatiques allowed to add nearly 250 rivers to the Québec thermal regimes characterization, with several stations operating since 1979. These three data sources were combined to characterize Québec rivers thermal regimes in more than 400 rivers and streams, over an extended geographical distribution. Uncertainty brought by the coarser temporal resolution of the spot and combined time series was assessed and found to result in substantially larger confidence intervals on the estimated model parameters, as compared with the confidence intervals obtained when using continuous time series of similar length.</p

Comparison of parametric and non-parametric estimations of the annual date of positive water temperature onset.

The onset date of positive water temperature in the annual thermal cycle of North-American streams is modeled using parametric (regression) and non-parametric (artificial neural networks) approaches. Physiographic, land cover and weather-related variables are used to predict the date of positive temperature onset for 191 station-years at 48 locations in Canada and in Northern US. Preliminary correlation analysis is performed in order to test the relationships between the physiographic/land cover/weather variables and the date of positive temperature onset. Moreover, several different subsets of variables are tested as inputs to each model type. Artificial neural networks can predict the date of positive temperature onset for a given station-year, given its longitude, lake coverage of its drainage basin, and two January–February daily temperature indices, with a split-sample validation root mean square error (RMSE) ∼8.8 days. Ordinary least square (OLS) regression models allow to predict the onset date with RMSE ∼9.5 days, given the station’s latitude, longitude, lake coverage and one January–February daily temperature index. OLS regression models adjusted on canonical variates combining 13 physiographic/land cover and weather variables achieve prediction performance ∼9.1 days. The precipitation does not impact much on the onset date prediction for all tested models

Statistical downscaling of precipitation and temperature using sparse Bayesian learning, multiple linear regression and genetic programming frameworks

<div><p>This study attempted to investigate two approaches to downscale temperature and four approaches to downscale precipitation. The first approach was an implementation of multiple linear regression (MLR) in the form of backward stepwise regression. The second approach applied canonical correlation analysis (CCA) with a sparse Bayesian learning (SBL) approach called relevance vector machine (RVM). For precipitation downscaling, two additional approaches which combined genetic programming (GP) as the predictor processing method with sparse Bayesian learning (SBLGP) and multiple linear regression (MLRGP) were also presented. The results showed that SBL outperformed MLR in downscaling temperature. For all stations, temperature was better downscaled than precipitation. For precipitation downscaling, the SBLGP approach outperformed all other approaches. MLRGP, on the other hand, did not bring about much improvement in the results and was in many cases outperformed by MLR.</p></div

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The identification of expanding HI shells is difficult because of their variable morphology. In this paper, we present an automatic detector for HI shells, based on the more stable dynamical characteristics of expanding bubbles with radii &lt; 40 pc. The detection is performed in two stages. First, artificial neural networks are trained to recognize the dynamical signature of an expanding bubble in the velocity spectra of 21-cm data. The second stage consists in subsequent validations based on the potential bubble’s morphology. The technique is tested on 11 known bubbles, and 10 of them are successfully detected. Conducting a systematic detection on a 48 ◦ × 9 ◦ region in the Perseus Arm, we obtain 7100 detections with spatial distribution following the stellar distribution of the Galactic disk. The estimated radius and expansion velocity distributions for objects with R ≤ 10 pc agree with the distributions predicted by models of adiabatically expanding bubble populations. The fraction of the Perseus Arm volume occupied by the detected objects, which can be interpreted as the small bubbles ’ contri-bution to the Galactic porosity Q, is calculated to QR&lt;40pc = 0.007 +0.025 −0.003. Four new bubble cases and eight serious candidates, related to known progenitors, are proposed