Localized Extreme Event Forecasting: Difference between revisions
Adding flood prediction section
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Storms, droughts, fires, floods, and other extreme events are expected to become stronger and more frequent as climate change progresses. Machine learning can be used to refine what are otherwise coarse-grained forecasts (e.g., generated from climate or weather prediction models) of these extreme weather events. These high-resolution forecasts can guide improvements in system robustness and resilience.
== ML Application Areas ==
'''Flood Prediction:''' Machine Learning algorithms have shown huge potential to predict floods in both the short and long runs. In fact, Autoregressive Artificial Neural Networks have been demonstrated to outperform conventional statistical methods, such as Autoregressive Moving Average (ARMA), for long-term flood prediction.<ref>{{Cite journal|last=Valipour|first=Mohammad|last2=Banihabib|first2=Mohammad Ebrahim|last3=Behbahani|first3=Seyyed Mahmood Reza|date=2013-01-07|title=Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir|url=https://www.sciencedirect.com/science/article/pii/S002216941200981X|journal=Journal of Hydrology|language=en|volume=476|pages=433–441|doi=10.1016/j.jhydrol.2012.11.017|issn=0022-1694}}</ref>
==Background Readings==
=== Literature Reviews ===
* [https://doi.org/10.3390/w10111536 Machine Learning for Flood Prediction]
==Conferences, Journals, and Professional Organizations==
==Libraries and Tools==
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