# Papers we are basing it off of 

We are basing it off of very recent papers in which ML models have been used for higher forecasting power. 

- [Short-term wind speed prediction](https://drive.google.com/file/d/1RdGwLX0m2LwVay2DOmdZomcTeUCL_f80/view?usp=sharing) using Extended Kalman Filter and ML 
	- [Research](https://drive.google.com/file/d/1RdGwLX0m2LwVay2DOmdZomcTeUCL_f80/view?usp=sharing) on short-term wind speed
- Real-time Forecasting [Framework](https://drive.google.com/file/d/1RdGwLX0m2LwVay2DOmdZomcTeUCL_f80/view?usp=sharing) using Deep Learning 
	- [codebase](https://github.com/BruceBinBoxing/Deep_Learning_Weather_Forecasting) for their paper
- [Geophysical Constraints ](https://drive.google.com/file/d/1RdGwLX0m2LwVay2DOmdZomcTeUCL_f80/view?usp=sharing)worldwide 
- Accelerating [Weather Prediction](https://drive.google.com/file/d/1dhRQFjIBVEHJBsnloHD4NX02Y8-9HYQJ/view?usp=sharing) using Near-Memory Reconfigurable Fabric 
- [ Feasibility of soft computing](https://drive.google.com/file/d/1-JaR0f5HSKwnqwMbyGkFXwFFf3tp3cKa/view?usp=sharing) for estimating long-term monthly mean wind speed 
- Visual Wind Speed prediction [ using CNN & RNN](https://arxiv.org/pdf/1905.13290v3.pdf)
	- For Wind Energy res-=ource quantification, air pollution monitoring, and weather forecasting
- Short-term wind speed prediction to [correct numerical weather forecasting ](https://www.sciencedirect.com/science/article/abs/pii/S0306261922002264)
	- Specifically, the values of the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE) are 0.1042 m/s, 4.63% and 0.1309 m/s after correction, decreased by 94.13%, 91.75% and 93.93%, respectively, compared to those without correction.


### Further Reading 
- *Heaven's Breath: A Natural History of the Wind* by Lyall Watson 


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