River Proximity Data as A Predictor for Ophiolite Classification: A Machine Learning Approach with OSM Data

References

1. Arnaut, F., Đurić, D., Đurić, U., Samardžić-Petrović, M., Peshevski, I. (2024). Application of geophysical and multispectral imagery data for predictive mapping of a complex geo-tectonic unit: a case study of the East Vardar Ophiolite Zone, North-Macedonia. Earth Science Informatics, pp. 1–20.
   [Google Scholar]
2. CarranzaEJM, LaborteAG (2015). Random Forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines). Comput Geosci74:60–70.
   [Google Scholar]
3. CarranzaEJM, LaborteAG (2015). Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm. Ore Geol Rev71:777–787.
   [Google Scholar]
4. Chen, T., Guestrin, C. (2016). August. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785–794).
   [Google Scholar]
5. Ham, J., Chen, Y., Crawford, M. M., Ghosh, J. (2005). Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans Geosci43:492–501.
   [Google Scholar]
6. Huang, C., Davis, L.S., Townshend, J. R.G (2002). An assessment of support vector machines for land cover classification. Int J Remote Sens23:725–749.
   [Google Scholar]