Nasiri Khiavi, A., & Vafakhah, M. (2024). Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping. Environmental Science and Pollution Research, 31(40), 52740-52757. https://doi.org/10.1007/s11356-024-34691-y

NASIRI KHIAVI, Ali und VAFAKHAH, Mehdi, 2024. Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping. Environmental Science and Pollution Research. 1 August 2024. Vol. 31, no. 40, p. 52740-52757. DOI 10.1007/s11356-024-34691-y.

Nasiri Khiavi, A., und M. Vafakhah. „Using Algorithmic Game Theory to Improve Supervised Machine Learning: A Novel Applicability Approach in Flood Susceptibility Mapping“. Environmental Science and Pollution Research, Bd. 31, Nr. 40, August 2024, S. 52740-57, https://doi.org/10.1007/s11356-024-34691-y.

Nasiri Khiavi, Ali/Vafakhah, Mehdi: Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping, Environmental Science and Pollution Research 2024, 52740-52757.

Nasiri Khiavi, A. und Vafakhah, M. (2024), „Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mapping“, Environmental Science and Pollution Research, Vol. 31 No. 40, S. 52740-52757.