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Using Big Data Analytics for Transboundary Water Management

Using Big Data Analytics for Transboundary Water Management

In Southern Africa, university researchers and government agencies are joining with international development groups and the private sector to explore how big data analytics can improve the management of aquifers that are shared by two or more countries.

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SwissCognitiveSouthern Africa has experienced drought-flood cycles for the past decade that strain the ability of any country to properly manage water resources. This dynamic is exacerbated by human drivers such as the heavy reliance of sectors such as mining and agriculture on groundwater and surface water, as well as subsistence agriculture in rural areas along rivers. These factors have progressively depleted natural freshwater systems and contributed to an accumulation of sediment in river systems. In a region where two or more countries share many of the groundwater and surface resources, water security cuts across the socioeconomic divide and is both a rural and urban issue. For example, the City of Cape Town had to heavily ration all water uses in 2017 and 2018, as its dams were drying up.

New technology, however, brings new opportunities for improved water governance. In Southern Africa, university researchers and government agencies are joining with international development groups and the private sector to explore how big data analytics can improve the management of aquifers that are shared by two or more countries.

Initiated by the USAID Global Development Lab and IBM Research Africa, this effort, known as the Big Data Analytics and Transboundary Water Collaboration for Southern Africa, or the Collaboration, aims to improve water security by promoting big data approaches for regional collaboration. Partners in the Collaboration include the South African Department of Science and Innovation, the Water Research Commission of South Africa, the Southern Africa Development Community-Groundwater Management Institute, the USAID Southern African Regional Mission, the U.S. Geological Survey, and the USAID Center for Water Security, Sanitation, and Hygiene. USAID’s Sustainable Water Partnership (SWP) was tasked with providing technical leadership and coordinating four research teams focusing on the Ramotswa aquifer, which Botswana and South Africa share, and the Shire aquifer, which Malawi and Mozambique share.

Research underway addresses several topics, including data standardization, data availability, and data sharing between countries that share water resources, and the application of big data analytics to the resulting data sets. The research teams have the opportunity to leverage the expertise of IBM Research Africa to explore ways to use the latest technology to collect and analyze data to improve water resource management. Big data analytics will allow basin managers to collect and sift enormous amounts of data to analyze trends and patterns, and leverage techniques such as to improve the management of the Ramotswa and Shire aquifers.

The lessons learned from this Collaboration will contribute to a digitization and data automation process initiated by the South African and Botswana Department of Water and Sanitation for water monitoring and smart decision-making. Ultimately it will help improve management of all shared aquifers in the 16 member states of the Southern African Development Community (SADC).

The Power of Machine Learning

Machine learning is a form of . In the last decade, techniques, a subset of that mimics how the human brain sorts through data to make decisions, have begun to surpass human performance in recognizing images. A growth in computational power has facilitated the use of massive datasets generated by satellites, the increased use of social media and other web-based applications for daily life. Sophisticated algorithms have been created to process substantial amounts of data and return valuable information on consumer behavior, natural processes, trade and economics, and many other sectors. […]

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