I have a monthly netCDF4 file containing chlorophyll-a values, and I aim to forecast these values using time series analysis.
My approach involves computing monthly spatial averages for this entire region and then forecasting these averages. Is this methodology valid?
Additionally, could you recommend a Python code for time series forecasting based on this approach?
Is it feasible to predict values for individual grid points without considering spatial averaging?
My study area encompasses an oceanic region of approximately 45,000 sq km near the southern coast of Sri Lanka.
Forecasting the quantity of chlorophyll-a based on time series (historical data) is not considered scientifically reliable due to the variable nature of chlorophyll-a, as it serves as the sole reference source for predicting the past behavior of the study area. Instead, it is recommended to estimate the amount of chlorophyll-a based on the variability of SST and SSS. To initiate this process, it is appropriate to utilize the simple mlp artificial network algorithm, which can be tailored to suit your specific requirements. Please refer to this article for further information: https://doi.org/10.1016/j.jenvman.2022.115636
- Badges
- Science topic
- Similar topics
- Phytochemistry
- Extracts