Flood forecasting

From Wikipedia, the free encyclopedia

Flood forecasting is the use of forecasted precipitation and streamflow data in rainfall-runoff and streamflow routing models to forecast flow rates and water levels for periods ranging from a few hours to days ahead, depending on the size of the watershed or river basin.[1] Flood forecasting can also make use of forecasts of precipitation in an attempt to extend the lead-time available.

Flood forecasting is an important component of flood warning, where the distinction between the two is that the outcome of flood forecasting is a set of forecast time-profiles of channel flows or river levels at various locations, while "flood warning" is the task of making use of these forecasts to tell decisions on warnings of floods.

Real-time flood forecasting at regional area can be done within seconds by using the technology of artificial neural network.[2] Effective real-time flood forecasting models could be useful for early warning and disaster prevention.

AI can assist us in predicting floods. A case study of the Red River of the north applied Deep Learning algorithms to predict flood-water-level using previous data from Pembina, Drayton, and Grand Forks.[3]

See also[edit]


  1. ^ "AMS Glossary". allenpress.com. Archived from the original on 16 July 2012. Retrieved 9 July 2015.
  2. ^ Chang, Li-Chiu; Shen, Hung-Yu; Chang, Fi-John (2014-11-27). "Regional flood inundation nowcast using hybrid SOM and dynamic neural networks". Journal of Hydrology. 519 (Part A): 476–489. Bibcode:2014JHyd..519..476C. doi:10.1016/j.jhydrol.2014.07.036.
  3. ^ Atashi, Vida (23 April 2022). "Water Level Forecasting Using Deep Learning Time-Series Analysis: A Case Study of Red River of the North". Water. 14 (12): 1971. doi:10.3390/w14121971.

External links[edit]