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This page is part of the Climate Change AI Wiki, which aims provide resources at the intersection of climate change and machine learning.
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[edit | edit source]
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.
Background Readings[edit | edit source]
Literature Reviews[edit | edit source]
Conferences, Journals, and Professional Organizations[edit | edit source]
Libraries and Tools[edit | edit source]
Data[edit | edit source]
Future Directions[edit | edit source]
Relevant Groups and Organizations[edit | edit source]
References[edit | edit source]
- Valipour, Mohammad; Banihabib, Mohammad Ebrahim; Behbahani, Seyyed Mahmood Reza (2013-01-07). "Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir". Journal of Hydrology. 476: 433–441. doi:10.1016/j.jhydrol.2012.11.017. ISSN 0022-1694.