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Davenport, United States — photo by @kellysikkema

Predicting Floods & Drought with AI

Using LSTMs for Climate Change Assessment Studies

One thing is certain: the Coronavirus is not the only crisis we are facing. Although of course it is an important one we have to deal with water to an increasing degree. Too much water in the wrong place (floods, rain) or too little water (drought, water scarcity). These changes in our environment brought on in part by the drastic increase in carbon emissions is affecting occurrences off floods and droughts around the globe.

  1. Predicting climate impacts over individual watersheds is difficult.
  2. Floods and droughts affect more people than any other type of weather-related natural hazard.

“…there exists a proof-of-concept that deep learning can transfer information about hydrologic processes and behaviors between basins, time and unobserved locations.”

Data Used

  • Models were trained on the data from 531 basins of the freely available CAMELS data set.
  1. Averaged the absolute gradients separately for each static input feature (catchment characteristics and climate indexes) over the low- and high-flow periods.
  2. Averaged values were normalized to [0,1] separately in each basin [17], so that the features could be ranked according to their relative influence.

Opening up the possibilities for large-scale impact assessment

They argue that:

Written by

AI Policy and Ethics at Student at University of Copenhagen MSc in Social Data Science. All views are my own.

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