Machine learning for water-energy-food-ecosystems nexus policy

Abstract

Machine learning for water-energy-food-ecosystems nexus policy Dr Janez Sušnik, from the IHE Delft Institute for Water Education and NEXOGENESIS Coordinator, guides us through the use of machine learning for improving policy advice in the water-energy-food-ecosystems nexus. Water, energy, and food (WEF) form a coherent interconnected system often referred to as the WEF nexus (Hoff, 2011). The WEF nexus interacts strongly with ecosystems, forming the wider WEFE nexus. Ecosystems provide the ‘base’ of the WEFE nexus, helping ensure the quantity, quality, timing, and accessibility of WEF resources, for example, by providing services including water purification, contributing freshwater provisioning, pollution reduction and control; maintaining healthy landscapes, contributing towards crop growth for food and energy crops; biodiversity providing pollinating insects for crop production and; forest and floodplain ecosystems provide biomass that as act as a global carbon sink and oxygen supply (Bell et al. 2016; Martinez- Hernandez et al. 2017).