Though the applications of disruptive technologies, i.e., big data, Artificial Intelligence (AI), IoT, cloud computing, and blockchain, were confined to the information technology sector before 21st technology, now these disruptive technologies are part of emerging solutions in almost all of the development sector. For water managers/engineers, policymakers and researchers disruptive technologies are showing promise in many water-related applications such as planning optimum water systems, detecting ecosystem changes through big remote sensing and geographical information system, forecasting/predicting/detecting natural and manmade calamities, scheduling irrigations, mitigating environmental pollution, studying climate change impacts, etc..
This project focuses on developing web-enabled tools and applications to address the data, information, and knowledge gaps in the water sector for strategic water planning and management.
These applications and tools will use open data repositories, AI models, and cloud computing platforms to ensure a free technology transfer to the Global South. The objectives of the Project are to:
- Develop web-based tools, freely available to stakeholders in developing countries that allow historical flooding extent, future flood risks and morphological changes to inland water bodies to be assessed using big data repositories available under open data framework.
- Develop a near-real-time water quality monitoring toolkit utilizing a network of interconnected sensors that can transfer data without human interference (i.e.-internet of things – IoT), and a Wi-Fi-based communication medium.
- Build a skilled workforce in the public and private sectors to use disruptive technologies and fill national water-related data and information needs.
During the current project period (August 2020 to December 2021), the use of disruptive technologies in water management will be illustrated through the development of three tools /toolkits:
1. Historical flood mapping and prediction of future flood risk tool
This tool consists of two modules; a flood mapping module that addresses the data gap of historical flood maps and a flood risk predicting module, which addresses the issue of possible risk in the future. The historical flood mapping module will use a water classification algorithm (Modified Normalized Difference Water Index) applied to ‘stacks’ of historical Landsat and Sentinel 2 satellite imagery to generate temporal and spatial extent of flooding. The module will enable more informed decisions on the exposure of people to floods to support preparedness and contingency planning.
The second module will use AI models to predict the future flood risk for a given area. The AI models will be trained using the historical flood maps from the first module, and open temporal datasets including land use land cover, population, infrastructure, precipitation, temperature, and sex and age disaggregated socio-economic data. This module will help identify the most flood-risk areas for the future.
2. Surface water change detection tool
The surface water change detection tool leverages the extensive archive of Landsat and Sentinel 2 data in the Google Earth Engine archive and Google’s cloud processing power to quickly calculate past patterns of surface water extent from multiple layers of Landsat and Sentinel 2 imagery.
In the first iteration, the tool will produce a high-quality map of erosion and deposition areas, using the Indus River, Pakistan, after the end of every monsoon season (1984 to 2020). Subsequently, the tool will be customized to provide analytics to other river systems at national or regional levels.
3. Water quality monitoring with IoT sensors
This toolkit will be designed to assist water quality monitoring in refugee camps in real or near real-time. The toolkit will aim to replace the current water monitoring system with a network of interconnected sensors that can transfer data without human interference (i.e.-internet of things – IoT), and a Wi-Fi-based communication medium. Information from sensors will be disseminated among refugees and camp managers through dashboards, which will be hosted on micro-servers.
This near real-time water quality data and information would allow the camp managers to take timely decisions, e.g., a septic tank would be emptied before it overflows and causes spread of disease, a contaminated water source would be identified before the water is consumed by the majority of the refugees, water supply to the camp can be monitored in real-time and water release could be adjusted on an hourly basis, etc.
National Statistics and Information Authority, Afghanistan
McGill University, Canada
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, 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)
United Nations High Commissioner for Refugees (UNHCR)
United Nations Children’s Fund (UNICEF)
Example Outputs of Previous Work on Disruptive Technologies Water Management
Strategic Foresight to Applications of Artificial Intelligence to Achieve Water-related Sustainable Development Goals (2020)
Online Historical Flood Mapping Tool for Ontario, Canada (2020)
A Publicly Available GIS-based Web Platform for Reservoir Inundation Mapping in the Lower Mekong Region (2020)