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Environmental Science and Pollution Research International|August 19, 2024
Using algorithmic game theory to improve supervised machine learning: A novel applicability approach in flood susceptibility mappingAli Nasiri Khiavi, Mehdi VafakhahScientific Reports|June 27, 2026
Predicting the flood susceptibility under land use and climate change scenarios using deep learning algorithmsRaoof Mostafazadeh, Ali Nasiri Khiavi, Shahnaz MirzaeiEnvironmental Science and Pollution Research International|December 13, 2024
Utilizing InVEST ecosystem services model combined with deep learning and fallback bargaining for effective sediment retention in Northern IranAli Nasiri Khiavi, Hamid Khodamoradi, Fatemeh SarounehEnvironmental Science and Pollution Research International|October 27, 2023
Groundwater quality modeling and determining critical points: a comparison of machine learning to Best-Worst MethodAli Nasiri Khiavi, Raoof Mostafazadeh, Maryam AdhamiEnvironmental Science and Pollution Research International|November 30, 2024
Assessing the performance of machine learning algorithms for analyzing land use changes in the Hyrcanian forests of IranZeinab Aminzadeh, Abazar Esmali Ouri, Raoof Mostafazadeh, et al.Journal of Environmental Management|June 20, 2021
Determination of flood probability and prioritization of sub-watersheds: A comparison of game theory to machine learningMohammadtaghi Avand, Ali Nasiri Khiavi, Majid Khazaei, et al.Pageof 1