ORIGINAL ARTICLE
Real-time geo-decisional system for risk and disaster management in Madagascar
1
School of Management and Technological Innovation, University of Fianarantsoa, Andrainjato, Fianarantsoa, zip code 301, Madagascar
2
Laboratory of Computer Science, Geomatics, Mathematics and Applications, University of Fianarantsoa, Andrainjato, Fianarantsoa, zip code 301, Madagascar
These authors had equal contribution to this work
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
Submission date: 2024-10-24
Final revision date: 2024-12-16
Acceptance date: 2025-01-02
Publication date: 2025-01-24
Corresponding author
Tsiorinantenaina René Rakotoarison
School of Management and Technological Innovation, University of Fianarantsoa, Andrainjato, Fianarantsoa, zip code 301, Madagascar
School of Management and Technological Innovation, University of Fianarantsoa, Andrainjato, Fianarantsoa, zip code 301, Madagascar
Reports on Geodesy and Geoinformatics 2025;119:7-13
KEYWORDS
TOPICS
GIS development & algorithmsGIS applicationsGeospatial data analysisCloud computingBig dataData miningDatabase systems
ABSTRACT
Climate change is intensifying extreme phenomena, and the world is increasingly vulnerable to a variety of disasters whose impacts are considerable and varied over time, from one place to another and from one community to another. Due to its geographical location, Madagascar is the most cyclone-prone country in Africa and the ninth most vulnerable country in the world. Almost every year, Madagascar is hit by cyclones, causing loss of life and property for the population. In terms of prevention, Madagascar already has an early warning system to inform the population, but during a crisis, it still lacks a decision support system for rapid, real-time intervention to minimize damage. In this paper, we propose a real-time geo-decision support system based on real-time data integration, real-time ETL and real-time cube building. In the proposed architecture, continuous data flow is required for real-time data integration. The proposed real-time ETL unit is composed of the capitalization of risk analysis experiments to ensure their reusability, as well as the insertion of processing parallelization to optimize the processing time of voluminous data. The real-time SOLAP unit consists of real-time cube formation using a spatial database that stores spatio-temporal data from a given point in time, with query optimization using materialized query technology. Our prototype uses NASA's weather data streaming service via an API. The ETL is written in a Matlab script and loads the data into a spatial database in Postgresql after processing. A web mapping application queries the constitution of a cube and displays the result for visualization.
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