The forecasting system we developed is essentially an intelligent computer program that can predict whether it will rain, how intense that rain will be and how long it will last at any location with greater than 90% accuracy at least 96 hours in advance.
The research attempted to provide a fast and preliminary approach for elements-at-risk mapping by developing a semi-automated detection and characterisation method.
This study proposes a framework for a IoT-based local landslide early warning system (Lo-LEWS). Monitoring, modelling, forecasting and warning represent the main phases of the proposed framework.
Continuing drought brings food insecurity and a risk for emerging or ongoing conflict in Kenya, Ethiopia and Somalia, as well as Iraq, Iran and Afghanistan, according to a new quarterly update that analyses finds from a global early warning tool.
United Nations Educational, Scientific and Cultural Office - IHE DELFT Institute for Water Education
