A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

Relativist ic Corrections to the Thermal Sunyaev-Zel''''dovich Power Spectrum

We present a quantitative estimate of the relativistic corrections to the thermal SZ power spectrum produced by the energetic electrons in massive clusters. The corrections are well within 10% for current experiments with working frequencies below v < 100 GHz, but become non-negligible at high frequencies v > 350 GHz. Moreover, the corrections appear to be slightly smaller at higher l or smaller angular scales. We conclude that there is no need to include the relativistic corrections in the theoretical study of the SZ power spectrum especially at low frequencies unless the SZ power spectrum is used for precision cosmology.     

Towards a free–free template for CMB foregrounds

A full-sky template map of the Galactic free–free foreground emission component is increasingly important for high-sensitivity cosmic microwave background (CMB) experiments. We use the recently published Hα data of both the northern and southern skies as the basis for such a template.
The first step is to correct the Hα maps for dust absorption using the 100-μm dust maps of Schlegel, Finkbeiner & Davis. We show that for a range of longitudes, the Galactic latitude distribution of absorption suggests that it is 33 per cent of the full extragalactic absorption. A reliable absorption-corrected Hα map can be produced for ∼95 per cent of the sky; the area for which a template cannot be recovered is the Galactic plane area  | b | < 5°, l = 260°–0°–160°  and some isolated dense dust clouds at intermediate latitudes.
The second step is to convert the dust-corrected Hα data into a predicted radio surface brightness. The free–free emission formula is revised to give an accurate expression (1 per cent) for the radio emission covering the frequency range 100 MHz–100 GHz and the electron temperature range 3000–20 000 K. The main uncertainty when applying this expression is the variation of electron temperature across the sky. The emission formula is verified in several extended H  ii regions using data in the range 408–2326 MHz.
A full-sky free–free template map is presented at 30 GHz; the scaling to other frequencies is given. The Haslam et al. all-sky 408-MHz map of the sky can be corrected for this free–free component, which amounts to a  ≈6  per cent correction at intermediate and high latitudes, to provide a pure synchrotron all-sky template. The implications for CMB experiments are discussed.     

Using bright ellipticals as dark energy cosmic clocks

The age of the Universe has been increasingly constrained by different techniques, such as the observations of type Ia supernovae (SNIa) at high redshift or dating the stellar populations of globular clusters. In this paper, we present a complementary approach using the colours of the brightest elliptical galaxies in clusters over a wide redshift range  ( z ≲ 1)  . We put new and independent bounds on the dark energy equation of state parametrized by a constant pressure-to-density ratio   w _Q  and by a parameter (ξ) which determines the scaling between the matter and dark energy densities. We find that accurate estimates of the metallicities of the stellar populations in moderate and high-redshift cluster galaxies can pose stringent constraints on the parameters that describe dark energy. Our results are in good agreement with the analysis of dark energy models using SNIa data as a constraint. Accurate estimates of the metallicities of stellar populations in cluster galaxies at   z ≲ 2  will make this approach a powerful complement to studies of cosmological parameters using high-redshift SNIa.     

Estimation of snow water equivalent using microwave radiometry over Arctic first‐year sea ice

The magnitude and spatial distribution of snow on sea ice are both integral components of the ocean–sea‐ice–atmosphere system. Although there exists a number of algorithms to estimate the snow water equivalent (SWE) on terrestrial surfaces, to date there is no precise method to estimate SWE on sea ice. Physical snow properties and in situ microwave radiometry at 19, 37 and 85 GHz, V and H polarization were collected for a 10‐day period over 20 first‐year sea ice sites. We present and compare the in situ physical, electrical and microwave emission properties of snow over smooth Arctic first‐year sea ice for 19 of the 20 sites sampled. Physical processes creating the observed vertical patterns in the physical and electrical properties are discussed. An algorithm is then developed from the relationship between the SWE and the brightness temperature measured at 37 GHz (55°) H polarization and the air temperature. The multiple regression between these variables is able to account for over 90% of the variability in the measured SWE. This algorithm is validated with a small in situ data set collected during the 1999 field experiment. We then compare our data against the NASA snow thickness algorithm, designed as part of the NASA Earth Enterprise Program. The results indicated a lack of agreement between the NASA algorithm and the algorithm developed here. This lack of agreement is attributed to differences in scale between the Special Sensor Microwave/Imager and surface radiometers and to differences in the Antarctic versus Arctic snow physical and electrical properties. Copyright © 2003 John Wiley & Sons, Ltd.     

用遗传算法解算病态方程

对应用遗传算法解决病态方程问题进行了探讨。利用拟合法而不是通过法方程求解参数，从而避免了法方程系数求逆，使病态方程的解答有了较好的结果。通过模拟计算并和其他方法进行比较，证明该方法是可行的和有效的。