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Computational Approaches to Big Data...

Barnes, Richard.

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Computational Approaches to Big Data and Deep Time in Ecology, Hydrology, and Geomorphology.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational Approaches to Big Data and Deep Time in Ecology, Hydrology, and Geomorphology./
Author:	Barnes, Richard.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	165 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
Contained By:	Dissertations Abstracts International82-05B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28090506
ISBN:	9798691237423

Computational Approaches to Big Data and Deep Time in Ecology, Hydrology, and Geomorphology.
Barnes, Richard.

Computational Approaches to Big Data and Deep Time in Ecology, Hydrology, and Geomorphology. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 165 p.

Source: Dissertations Abstracts International, Volume: 82-05, Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2020.

This item must not be sold to any third party vendors.

Some of the most challenging problems in ecology, hydrology, and geomorphology arise from processes which play out over large spatial scales and long time intervals. High-resolution global digital elevation models are becoming available. They will allow problems of broad spatial extent to be addressed, but only if we can handle the enormous volume of data. Similarly, performance gains in computers now make it feasible to test more complex theory using computational models, but only if efficient techniques are used. In this dissertation I develop and demonstrate techniques for handling large geospatial raster datasets and rapidly modeling ecological and hydrological processes, producing results that are orders of magnitude more efficient than previous work. Notably, these techniques work on both high-performance machines and laptops. Finally, I apply the techniques to a challenging problem at the interface of ecology, evolution, climate, and geology.

ISBN: 9798691237423Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Computational science

Computational Approaches to Big Data and Deep Time in Ecology, Hydrology, and Geomorphology.
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245 1 0 $a Computational Approaches to Big Data and Deep Time in Ecology, Hydrology, and Geomorphology.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 165 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-05, Section: B.
500 $a Advisor: Harte, John.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Some of the most challenging problems in ecology, hydrology, and geomorphology arise from processes which play out over large spatial scales and long time intervals. High-resolution global digital elevation models are becoming available. They will allow problems of broad spatial extent to be addressed, but only if we can handle the enormous volume of data. Similarly, performance gains in computers now make it feasible to test more complex theory using computational models, but only if efficient techniques are used. In this dissertation I develop and demonstrate techniques for handling large geospatial raster datasets and rapidly modeling ecological and hydrological processes, producing results that are orders of magnitude more efficient than previous work. Notably, these techniques work on both high-performance machines and laptops. Finally, I apply the techniques to a challenging problem at the interface of ecology, evolution, climate, and geology.
590 $a School code: 0028.
650 4 $a Computer science. $3 523869
650 4 $a Geomorphology. $3 542703
650 4 $a Ecology. $3 516476
650 4 $a Hydrologic sciences. $3 3168407
650 4 $a Information technology. $3 532993
653 $a Computational science
653 $a Evolutionary biology
653 $a Graph algorithms
653 $a Hydrological modeling
653 $a Landscape evolution
653 $a Parallel algorithms
653 $a High-resolution global digital elevation
653 $a Bid data
690 $a 0984
690 $a 0484
690 $a 0329
690 $a 0489
690 $a 0388
710 2 $a University of California, Berkeley. $b Energy & Resources. $3 1673767
773 0 $t Dissertations Abstracts International $g 82-05B.
790 $a 0028
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28090506