Zarchi, M., Nazari, R. A., & Tee, K. F. (2025). Explainable multi-attribute machine learning via a hierarchical nature-inspired system toward predicting geological hazards. Modeling Earth Systems and Environment, 11(4). https://doi.org/10.1007/s40808-025-02421-z
ISO-690 (author-date, English)ZARCHI, Milad, NAZARI, Reza A. and TEE, Kong Fah, 2025. Explainable multi-attribute machine learning via a hierarchical nature-inspired system toward predicting geological hazards. Modeling Earth Systems and Environment. 1 August 2025. Vol. 11, no. 4, . DOI 10.1007/s40808-025-02421-z.
Modern Language Association 9th editionZarchi, M., R. A. Nazari, and K. F. Tee. “Explainable Multi-Attribute Machine Learning via a Hierarchical Nature-Inspired System Toward Predicting Geological Hazards”. Modeling Earth Systems and Environment, vol. 11, no. 4, Aug. 2025, https://doi.org/10.1007/s40808-025-02421-z.
Mohr Siebeck - Recht (Deutsch - Österreich)Zarchi, Milad/Nazari, Reza A./Tee, Kong Fah: Explainable multi-attribute machine learning via a hierarchical nature-inspired system toward predicting geological hazards, Modeling Earth Systems and Environment 2025,
Emerald - HarvardZarchi, M., Nazari, R.A. and Tee, K.F. (2025), “Explainable multi-attribute machine learning via a hierarchical nature-inspired system toward predicting geological hazards”, Modeling Earth Systems and Environment, Vol. 11 No. 4, available at:https://doi.org/10.1007/s40808-025-02421-z.