Dr Moradkhani Personal Website Of Dr Hamid Moradkhani Abstract: urban drainage systems in metropolitan areas are highly complex, posing significant challenges for effective stormwater management. traditional models like storm water management model (swmm) are widely used but become inefficient at large scales with intricate drainage networks. Drought analysis under climate change using copula.
Dr Moradkhani Personal Website Of Dr Hamid Moradkhani In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. A cluster based temporal attention approach for predicting cyclone induced compound flood dynamics environmental modelling & software 2025 06 | journal article doi: 10.1016 j.envsoft.2025.106499 contributors: samuel daramola; david f. muñoz; hamed moftakhari; hamid moradkhani. Moradkhani is a pioneer in developing methods in data assimilation, machine learning, uncertainty quantification, risk analysis and harnessing data revolution, used in earth system science, engineering and other disciplines. Professor. hamid moradkhani alton n. scott chair of engineering department of civil, construction, and environmental engineering department of computer science.
Dr Moradkhani Personal Website Of Dr Hamid Moradkhani Moradkhani is a pioneer in developing methods in data assimilation, machine learning, uncertainty quantification, risk analysis and harnessing data revolution, used in earth system science, engineering and other disciplines. Professor. hamid moradkhani alton n. scott chair of engineering department of civil, construction, and environmental engineering department of computer science. Daramola, s., d. f. muñoz, h. moftakhari, and h. moradkhani (2025), a cluster based temporal attention approach for predicting cyclone induced compound flood dynamics, environmental modelling & software, doi: 10.1016 j.envsoft.2025.106499. Moradkhani’s research team has aimed to make significant contributions to hydrologic science water resources system analysis and computational modeling with emphasis on harnessing data revolution, predictive science, uncertainty analysis, deep learning and high performance computing. Meet the team of dr. moradkhani at the university of alabama and explore their research interests, expertise, and academic contributions. Jafazadegan, k., v. merwade, and h. moradkhani (2020), combining clustering and classification for the regionalization of environmental model parameters: application to floodplain mapping in data scarce regions, environmental modelling and software. doi: 10.1016 j.envsoft.2019.104613.
Dr Moradkhani Personal Website Of Dr Hamid Moradkhani Daramola, s., d. f. muñoz, h. moftakhari, and h. moradkhani (2025), a cluster based temporal attention approach for predicting cyclone induced compound flood dynamics, environmental modelling & software, doi: 10.1016 j.envsoft.2025.106499. Moradkhani’s research team has aimed to make significant contributions to hydrologic science water resources system analysis and computational modeling with emphasis on harnessing data revolution, predictive science, uncertainty analysis, deep learning and high performance computing. Meet the team of dr. moradkhani at the university of alabama and explore their research interests, expertise, and academic contributions. Jafazadegan, k., v. merwade, and h. moradkhani (2020), combining clustering and classification for the regionalization of environmental model parameters: application to floodplain mapping in data scarce regions, environmental modelling and software. doi: 10.1016 j.envsoft.2019.104613.
Dr Moradkhani Personal Website Of Dr Hamid Moradkhani Meet the team of dr. moradkhani at the university of alabama and explore their research interests, expertise, and academic contributions. Jafazadegan, k., v. merwade, and h. moradkhani (2020), combining clustering and classification for the regionalization of environmental model parameters: application to floodplain mapping in data scarce regions, environmental modelling and software. doi: 10.1016 j.envsoft.2019.104613.
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