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| Titel |
Using Data Warehouses to extract knowledge from Agro-Hydrological simulations |
| VerfasserIn |
Tassadit Bouadi, Chantal Gascuel-Odoux, Marie-Odile Cordier, René Quiniou, Pierre Moreau |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250080418
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| Zusammenfassung |
| In recent years, simulation models have been used more and more in hydrology to test the
effect of scenarios and help stakeholders in decision making. Agro-hydrological models have
oriented agricultural water management, by testing the effect of landscape structure and
farming system changes on water and chemical emission in rivers. Such models generate a
large amount of data while few of them, such as daily concentrations at the outlet of the
catchment, or annual budgets regarding soil, water and atmosphere emissions, are stored and
analyzed. Thus, a great amount of information is lost from the simulation process. This is due
to the large volumes of simulated data, but also to the difficulties in analyzing and
transforming the data in an usable information.
In this talk we illustrate a data warehouse which has been built to store and manage
simulation data coming from the agro-hydrological model TNT (Topography-based nitrogen
transfer and transformations, (Beaujouan et al., 2002)). This model simulates the transfer and
transformation of nitrogen in agricultural catchments. TNT was used over 10 years on the Yar
catchment (western France), a 50 km2 square area which present a detailed data
set and have to facing to environmental issue (coastal eutrophication). 44 output
key simulated variables are stored at a daily time step, i.e, 8 GB of storage size,
which allows the users to explore the N emission in space and time, to quantify all
the processes of transfer and transformation regarding the cropping systems, their
location within the catchment, the emission in water and atmosphere, and finally to get
new knowledge and help in making specific and detailed decision in space and
time.
We present the dimensional modeling process of the Nitrogen in catchment data
warehouse (i.e. the snowflake model). After identifying the set of multileveled dimensions
with complex hierarchical structures and relationships among related dimension levels, we
chose the snowflake model to design our agri-environmental data warehouse. The snowflake
schema is required for flexible querying complex dimension relationships. We have designed
the Nitrogen in catchment data warehouse using the open source Business Intelligence
Platform Pentaho Version 3.5.
We use the online analytical processing (OLAP) to access and exploit, intuitively and
quickly, the multidimensional and aggregated data from the Nitrogen in catchment data
warehouse. We illustrate how the data warehouse can be efficiently used to explore
spatio-temporal dimensions and to discover new knowledge and enrich the exploitation
level of simulations. We show how the OLAP tool can be used to provide the user
with the ability to synthesize environmental information and to understand nitrates
emission in surface water by using comparative, personalized views on historical
data.
To perform advanced analyses that aim to find meaningful patterns and relationships
in the data, the Nitrogen in catchment data warehouse should be extended with
data mining or information retrieval methods as Skyline queries (Bouadi et al.,
2012).
(Beaujouan et al., 2002) Beaujouan, V., Durand, P., Ruiz, L., Aurousseau, P., and Cotteret, G.
(2002). A hydrological model dedicated to topography-based simulation of nitrogen transfer
and transformation: rationale and application to the geomorphology denitrification
relationship. Hydrological Processes, pages 493–507.
(Bouadi et al., 2012) Bouadi, T., Cordier, M., and Quiniou, R. (2012). Incremental
computation of skyline queries with dynamic preferences. In DEXA (1), pages 219–233. |
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