Practical application of machine learning for organic matter and harmful algal blooms in freshwater systems: A review
The application of machine learning (ML) techniques for understanding and predicting
organic matter (OM) and harmful algal blooms (HABs) in freshwater systems has increased …
organic matter (OM) and harmful algal blooms (HABs) in freshwater systems has increased …
[HTML][HTML] Integrating Generative AI in Hackathons: Opportunities, Challenges, and Educational Implications
Hackathons have become essential in the software industry, fostering innovation and skill
development for both organizations and students. These events facilitate rapid prototy** …
development for both organizations and students. These events facilitate rapid prototy** …
Incorporating spatial autocorrelation into deformable ConvLSTM for hourly precipitation forecasting
Hourly precipitation forecasting is considered a spatiotemporal sequence forecasting
problem that plays an increasingly important role in early warning of rainfall-induced floods …
problem that plays an increasingly important role in early warning of rainfall-induced floods …
Using a physics-based hydrological model and storm transposition to investigate machine-learning algorithms for streamflow prediction
Abstract Machine learning (ML) algorithms have produced remarkable advances in
streamflow prediction, exceeding the performance of calibrated conceptual and physics …
streamflow prediction, exceeding the performance of calibrated conceptual and physics …
The future of coastal monitoring through satellite remote sensing
Satellite remote sensing is transforming coastal science from a “data-poor” field into a “data-
rich” field. Sandy beaches are dynamic landscapes that change in response to long-term …
rich” field. Sandy beaches are dynamic landscapes that change in response to long-term …
Automated hydrologic forecasting using open-source sensors: Predicting stream depths across 200,000 km2
Wireless sensor networks support decision-making in diverse environmental contexts.
Adoption of these networks has increased dramatically due to technological advances that …
Adoption of these networks has increased dramatically due to technological advances that …
[HTML][HTML] The Implementation of Multimodal Large Language Models for Hydrological Applications: A Comparative Study of GPT-4 Vision, Gemini, LLaVa, and …
Large Language Models (LLMs) combined with visual foundation models have
demonstrated significant advancements, achieving intelligence levels comparable to human …
demonstrated significant advancements, achieving intelligence levels comparable to human …
Streamflow estimation in a mediterranean watershed using neural network models: A detailed description of the implementation and optimization
This study compares the performance of three different neural network models to estimate
daily streamflow in a watershed under a natural flow regime. Based on existing and public …
daily streamflow in a watershed under a natural flow regime. Based on existing and public …
A contemporary systematic review of Cyberinfrastructure Systems and Applications for Flood and Drought Data Analytics and Communication
Hydrometeorological disasters, including floods and droughts, have intensified in both
frequency and severity in recent years. This trend underscores the critical role of timely …
frequency and severity in recent years. This trend underscores the critical role of timely …
[HTML][HTML] Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model
A Tursun, X **e, Y Wang, Y Liu, D Peng… - Journal of Hydrology …, 2024 - Elsevier
Abstract Study region Yellow River Basin in China, where streamflow dynamics were
significantly impacted by human activities. Study focus We introduced a deep learning …
significantly impacted by human activities. Study focus We introduced a deep learning …