Data Integration and Analysis System (DIAS) as a platform for data and model integration: Cases in the field of water resources management and disaster risk reduction

Authors

  • Akiyuki Kawasaki Earth Observation Data Integration and Fusion Research Initiative (EDITORIA), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505
  • Petra Koudelova Faculty of Civil Engineering, The Czech Technical University in Prague, Thakurova 7, 166 29, Prague 6
  • Katsunori Tamakawa International Centre for Water Hazard and Risk Management (ICHARM), Public Works Research Institute (PWRI), 1-6, Minamihara, Tsukuba, 305-8516
  • Asanobu Kitamoto National Institute of Informatics (NII), 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430
  • Eiji Ikoma Earth Observation Data Integration and Fusion Research Initiative (EDITORIA), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505
  • Koji Ikeuchi Earth Observation Data Integration and Fusion Research Initiative (EDITORIA), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505
  • Ryosuke Shibasaki Earth Observation Data Integration and Fusion Research Initiative (EDITORIA), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505
  • Masaru Kitsuregawa Earth Observation Data Integration and Fusion Research Initiative (EDITORIA), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505; National Institute of Informatics (NII), 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430
  • Toshio Koike International Centre for Water Hazard and Risk Management (ICHARM), Public Works Research Institute (PWRI), 1-6, Minamihara, Tsukuba, 305-8516

DOI:

https://doi.org/10.5334/dsj-2018-029

Keywords:

data and model integration, platform, dam, hydroelectric power, flood control

Abstract

The development of data and model integration platforms has furthered scientific inquiry and helped to solve pressing social and environmental problems. While several e-infrastructure platforms have been developed, the concept of data and model integration remains obscure, and these platforms have produced few firm results. This article investigates data and model integration on the Data Integration and Analysis System (DIAS) platform, using three case projects from water-related fields. We provide concrete examples of data and model integration by analyzing the data transfer and analysis process, and demonstrate what platform functions are needed to promote the advantages of data and model integration. In addition, we introduce the Digital Object Identifier (DOI), a valuable tool for promoting data and model integration and open science. Our investigation reveals that DIAS advances data and model integration in five main ways: it is a "sophisticated and robust integration platform"; has "rich APIs, including a metadata management system, for high-quality data archive and utilization"; functions as a "core hydrological model"; and promotes a "collaborative R&D community" and "open science and data repositories". This article will appeal especially to researchers interested in new methods of analysis, and information technology experts responsible for developing e-infrastructure systems to support environmental and scientific research.

Author Biography

Akiyuki Kawasaki, Earth Observation Data Integration and Fusion Research Initiative (EDITORIA), The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505

Kawasaki is Professor of civil engineering, and also a core member of development team of the Data Integration and Analysis System (DIAS) that is a leading global environmental Big Data project in Japan. His study focus is the development of problem-solving techniques and methods for disaster risk reduction and poverty eradication. Using DIAS, he integrates various DRR data and information from satellite and in-situ observation, statistics and field survey, and models and analyzes the relationship between natural environment and human society, especially in water-related disaster issues. He has 18 years experience of GIS analysis and database development.

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Published

2018-10-23

Issue

Vol. 17 (2018)

Section

Research Papers

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Copyright (c) 2018 The Author(s)

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