Page 46 - Frontier Technologies to Protect the Environment and Tackle Climate Change

Frontier Technologies to Protect the Environment and Tackle Climate Change

Box 6: UNESCO: Using AI to reduce hydrological risk
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The UNESCO G-WADI Geoserver application (Water and Development Information for Arid
Lands – a Global Network) is using an artificial neural network (ANN) to estimate real-time
precipitation worldwide. This product is called the Precipitation Estimation from Remotely
Sensed Information using Artificial Neural Networks – Cloud Classification System (G-WADI
PERSIANN-CCS). The G-WADI PERSIANN-CCS GeoServer has been under development since
2005, through a close working relationship between the Center for Hydrometeorology and
Remote Sensing (CHRS) at the University of California, Irvine, and UNESCO’s International
Hydrological Programme. The core algorithm of this system, supported by NASA and NOAA,
extracts local and regional cloud features (coldness, geometric structure and texture) from
the international constellation of GEO satellites capturing infrared imagery and estimates
of rainfall every 30 minutes. Information from LEO satellites is then used to adjust the initial
precipitation estimation from the ANN algorithm.

The G-WADI PERSIANN-CCS geoserver is also being used to inform emergency planning and
management of hydrological risks, such as floods, droughts and other extreme weather
events. For example, the Namibian Drought Hydrological Services uses it to prepare daily
bulletins with up-to-date information on flood and drought conditions for local communities.
The geoserver is also being widely used to track storms globally, as in the case of the Haiyan
Super Typhoon.
The G-WADI PERSIANN-CCS system is now available through the iRain mobile application,
devoted to facilitating people’s involvement in collecting local data for global precipitation
monitoring. iRain allows users to visualize real-time global satellite precipitation observations,
track extreme precipitation events worldwide and report local rainfall information using a
crowd-sourcing functionality to supplement the data. This provides an opportunity to
improve remotely sensed estimations of precipitation. Moreover, the use of a crowdsourcing
functionality in iRain to supplement the data opens opportunities for engaging citizen scientists.

Waste optimization using AI

Waste prevention, recycling and resource recovery are also areas in which the application of AI within
the waste sector can significantly contribute to climate change mitigation. Reuse and repair are the
first waste management options that extend the life of products, improving material efficiency and
reducing GHG emissions. This can be illustrated via the example of one of the fastest growing
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hazardous waste streams: Waste Electrical and Electronic Equipment (WEEE), as detailed in Box 7.

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