Sustainable management of water resources in any country or region requires relevant data and information. The disruptive digital technologies, i.e., big data, Artificial Intelligence (AI), cloud computing, and blockchain show promise in water-related applications such as planning optimum water systems, detecting ecosystem changes, forecasting/predicting/detecting natural and human made calamities, scheduling irrigation, mitigating environmental pollution, studying climate change impacts, and many others.

The project focuses on application of remote sensing, GIS, big data, AI, and machine learning to assess risks of hydroclimatic disasters, detect changes in water resources and help quantify of water security indicators and water cycle components – all in the form of web-enabled applications and tools. These applications and tools use open data repositories, open-source computing, AI models and cloud computing platforms to ensure the technology transfer to the Global South without big cost impact.

In 2020-2021, this project developed a historical flood mapping tool using historical Landsat data. The tool allows mapping of surface inundation for any region of the world over the period 1984-2020 to be carried out. The tool was developed leveraging the power of Google Earth Engine (cloud computing) resources. It enables users to analyse spatial and temporal extent of surface water inundation and download the result for further analysis.

The objectives of the project over the period 2022-2023 are to:

  • Enhance this historical flood mapping tool (FMT) by incorporating other data sources especially freely available RADAR satellite data and improving the overall spatial resolution of the mapped products (using both free and commercial data) for local, urban and property level applications.
  • Develop a flood risk analysis and mapping (FRAM) tool that will provide flood risk map for a given area based on historical flood map generated by the FMT and trained AI model. FRAM will help identify the most flood-risky areas for future planning and risk mitigation.
  • Develop a Surface Water Change Detection (SWCD) to quickly calculate past patterns of surface water extent. This tool will be useful for analysing storage in waterbodies and reservoirs.
  • Support UNU-INWEH water security assessment project by developing methodologies for quantification of specific water security indicators – water quality ones in the first place
Toshka Lake in Egypt
Bangladesh Flood 2017
Flood Mapping Tool


The project works with a range of partners to co-develop and roll out the above tools and is building new partnership across the world as the work develops. Examples of such partnerships – in the previous years and at present – include, but not limited to:

  • The Pacific Community, Fiji
  • International Centre for Water Hazard and Risk Management, Japan
  • Global Partnership for Sustainable Development Data, Kenya
  • The International Centre for Integrated Mountain Development (ICIMOD), Nepal
  • SDG Accelerator Lab, University of Baluchistan, Pakistan
  • Qatar Computing Research Institute, Hamad Bin Khalifa University, Qatar
  • Ministry of Agriculture, Sri Lanka
  • Asian Disaster Preparedness Center, Stockholm Environment Institute, Thailand
  • United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP)
  • Flood Forecasting and warning Center (FFWC), Bangladesh

Example Outputs of Previous Work


Dr. Mir Matin


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