Ehsan Foroumandi
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View article: Harnessing Generative Deep Learning for Enhanced Ensemble Data Assimilation
Harnessing Generative Deep Learning for Enhanced Ensemble Data Assimilation Open
Hydrologic modeling faces challenges due to various sources of uncertainty, the inherent nonlinearity, and high dimensionality of Earth systems. Data assimilation (DA) methods are known to improve the accuracy and account for uncertainties…
View article: Global Predictability of Marine Heatwave Induced Rapid Intensification of Tropical Cyclones
Global Predictability of Marine Heatwave Induced Rapid Intensification of Tropical Cyclones Open
Prediction of the rapid intensification (RI) of tropical cyclones (TCs) is crucial for improving disaster preparedness against storm hazards. These events can cause extensive damage to coastal areas if occurring close to landfall. Availabl…
View article: Enhancing Streamflow Prediction in Ungauged Basins Using a Nonlinear Knowledge‐Based Framework and Deep Learning
Enhancing Streamflow Prediction in Ungauged Basins Using a Nonlinear Knowledge‐Based Framework and Deep Learning Open
In hydrology, a fundamental task involves enhancing the predictive power of a model in ungagged basins by transferring information on physical attributes and hydroclimate dynamics from gauged basins. Introducing an integrated nonlinear clu…
View article: Generative Adversarial Network for Real‐Time Flash Drought Monitoring: A Deep Learning Study
Generative Adversarial Network for Real‐Time Flash Drought Monitoring: A Deep Learning Study Open
Droughts are among the most devastating natural hazards, occurring in all regions with different climate conditions. The impacts of droughts result in significant damages annually around the world. While drought is generally described as a…
View article: ChatGPT in Hydrology and Earth Sciences: Opportunities, Prospects, and Concerns
ChatGPT in Hydrology and Earth Sciences: Opportunities, Prospects, and Concerns Open
The emergence of large language models (LLMs), such as ChatGPT, has garnered significant attention, particularly in academic and scientific circles. Researchers, scientists, and instructors hold varying perspectives on the advantages and d…
View article: Linking Spatial–Temporal Changes of Vegetation Cover with Hydroclimatological Variables in Terrestrial Environments with a Focus on the Lake Urmia Basin
Linking Spatial–Temporal Changes of Vegetation Cover with Hydroclimatological Variables in Terrestrial Environments with a Focus on the Lake Urmia Basin Open
Investigation of vegetation cover is crucial to the study of terrestrial ecological environments as it has a close relationship with hydroclimatological variables and plays a dominant role in preserving the characteristics of a region. In …
View article: Climate change or regional human impacts? Remote sensing tools, artificial neural networks, and wavelet approaches aim to solve the problem
Climate change or regional human impacts? Remote sensing tools, artificial neural networks, and wavelet approaches aim to solve the problem Open
Lake Urmia, as the largest lake in Iran, has suffered from water-level decline and this problem needs to be investigated accurately. The major reason for the decline is controversial. The current paper aimed to study the hydro-environmenta…
View article: Ecological-environmental quality estimation using remote sensing and combined artificial intelligence techniques
Ecological-environmental quality estimation using remote sensing and combined artificial intelligence techniques Open
Ecological-environmental quality was evaluated for Tabriz and Rasht cities (in Iran) with different climate conditions using artificial intelligence (AI) and remote sensing (RS) techniques. Sampling sites were surveyed and ecological exper…