This project will be inspecting the effects of water stress found from remote sensing data.
- Indentifying points of interest using change point detection
- Use Casual Impact to measure the effect of a point of interest
- Applying the first two point to forcasts to aid preventative measures
input data should be monthly resampled dataframe with NDVI,SPEI and predictive/correlating variables to help the forecasting
first casual impact from dataframe is shown
File descriptions:
filename | description |
---|---|
segmentation1.ipynb | Creating the synthetic dataset |
--- | Benchtesting the algorithms on the synthetic dataset |
segmentation2.ipynb | prototyping on the real NDVI dataset |
--- | check witch changepoints like in the drought-linked pointer years |
all_in_one___periodic_wave.ipynb | an example on how to use the classes made in this repo to benchmark the change point algorithms on the datasets |
benchmark.py | benchmarking class to run the benchmarks |
helper.py | file containing helper functions |
lstm_model.py | file containing the model to facilitate forecasting |
r_files folder | contains all the necessary files to run the R environment to do the checks for BFAST and BEAST on the synthetic dataset |
- cpdetect: https://github.com/jzyee/cpdetect
- ruptures: https://centre-borelli.github.io/ruptures-docs/
- casualimpact: https://github.com/dafiti/causalimpact
- Ensign DL and Pande VS. Bayesian Detection of Intensity Changes in Single Molecule and Molecular Dynamics Trajectories. J. Phys. Chem B 114:280 (2010)
- C. Truong, L. Oudre, and N. Vayatis. Selective review of offline change point detection methods. Signal Processing, 167:107299, 2020.