This repository contains the codes associated with the following publication: Pilario, Escober, de los Reyes V, and Espino (2024). "Robust Prediction of Chlorophyll-a from Nitrogen and Phosphorus Content in Philippine and Global Lakes Using Fine-Tuned, Explainable Machine Learning," Environmental Challenges. DOI: 10.1016/j.envc.2024.101056
To run the codes, the following libraries are needed: NumPy, Pandas, Seaborn, Matplotlib, Scikit-learn, Optuna.
There are two main files corresponding to two case studies in the paper:
This repository also contains the following data sets:
Please refer to the original source of the global lakes data from Naderian et al. (2024).
If you find any issues or have any suggestions for improvement, feel free to contact me via kspilario@up.edu.ph. If any codes are not working on your terminal, let me know. :)