CSDMS - User contributions [en]

2026 CSDMS meeting-009

2026-04-12T12:40:48Z

FathiSaid:

{{CSDMS meeting personal information template-2026
|CSDMS meeting first name=Mohamed
|CSDMS meeting last name=Fathi Said
|CSDMS meeting institute=Florida Gulf Coast University
|CSDMS meeting city=Fort Myers
|CSDMS meeting country=United States
|CSDMS meeting state=Florida
|CSDMS meeting email address=m.fathi.said0@gmail.com
|CSDMS meeting phone=484-581-9234
}}
{{CSDMS meeting select clinics1 2026
|CSDMS_meeting_select_clinics1_2026=3) Advancing Spatiotemporal Modeling with Deep Learning: CNN-LSTM Integration
}}
{{CSDMS meeting select clinics2 2026
|CSDMS_meeting_select_clinics2_2026=1) Morphodynamical simulations using Delft3D FM
}}
{{CSDMS meeting select clinics3 2026
|CSDMS_meeting_select_clinics3_2026=4) Simulating landslide sediment transport and runout hazard in Landlab using MassWastingRunout
}}
{{CSDMS meeting abstract yes no 2026
|CSDMS meeting abstract submit 2026=Yes
}}
{{CSDMS meeting abstract poster Epub 2026
|CSDMS meeting poster Epub submit 2026=Poster
}}
{{CSDMS meeting abstract title temp2026
|CSDMS meeting abstract title=A Physics-Guided Input Framework for LSTM-Based Streamflow Modeling in Snow-Affected Watersheds
|Working_group_member_WG_FRG=Hydrology Focus Research Group
}}
{{CSDMS meeting abstract template 2026
|CSDMS meeting abstract=Accurate streamflow prediction in snow-dominated catchments remains a persistent challenge, primarily due to the nonlinear and temporally evolving interactions among precipitation phase, temperature variability, snow accumulation, and melt processes. Although machine learning approaches, and Long Short-Term Memory (LSTM) models in particular, have demonstrated strong capability in hydrologic modeling and streamflow prediction, their performance is often limited by the quality and physical relevance of the input forcings. This study investigates whether incorporating physics-informed, hydrologically meaningful inputs derived from standard meteorological variables can further enhance LSTM-based streamflow predictions. To evaluate this, we apply LSTM models across a subset of more than 85 snow-affected catchments from the CAMELS-US dataset. Instead of using raw precipitation time series as input, we partition precipitation into rain and snow components using temperature-dependent probability functions. The partitioned snow time series is also used to derive hydrologically meaningful variables, including snow accumulation and basin-scale snow storage. Finally, these enhanced physics-informed inputs are one-by-one incorporated within the LSTM framework. The results demonstrate a clear improvement in predictive performance, particularly in the historically low-performing catchments. The 25th percentile threshold of Nash-Sutcliffe Efficiency (NSE) increased from 0.51 using standard meteorological inputs to 0.62 with the physics-informed input set. Additionally, the median NSE across all basins improved by 0.07. These findings highlight the potential of integrating simple physical principles into data-driven models to bridge the gap between process understanding and machine learning, offering efficient pathways for improving hydrologic streamflow prediction in snow-affected watersheds.
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2026 CSDMS meeting-009

2026-01-24T00:42:40Z

FathiSaid: Created page with "{{CSDMS meeting personal information template-2026 |CSDMS meeting first name=Mohamed |CSDMS meeting last name=Fathi Said |CSDMS meeting institute=Florida Gulf Coast University |CSDMS meeting city=Fort Myers |CSDMS meeting country=United States |CSDMS meeting state=Florida |CSDMS meeting email address=m.fathi.said0@gmail.com |CSDMS meeting phone=484-581-9234 }} {{CSDMS meeting select clinics1 2026 |CSDMS_meeting_select_clinics1_2026=3) Advancing Spatiotemporal Modeling wi..."

2025 CSDMS meeting-027

2025-03-22T17:09:31Z

FathiSaid:

{{CSDMS meeting personal information template-2025
|CSDMS meeting first name=Mohamed Fathi
|CSDMS meeting last name=Said
|CSDMS meeting institute=Florida Gulf Coast University
|CSDMS meeting city=Fort Myers
|CSDMS meeting country=United States
|CSDMS meeting state=Florida
|CSDMS meeting email address=m.fathi.said0@gmail.com
|CSDMS meeting phone=4845819234
}}
{{CSDMS meeting select clinics1 2025
|CSDMS_meeting_select_clinics1_2025=4) Get lazy with LLMs
}}
{{CSDMS meeting select clinics2 2025
|CSDMS_meeting_select_clinics2_2025=2) From Exploration to Publication: Geospatial Research in the Jupyter Ecosystem
}}
{{CSDMS meeting select clinics3 2025
|CSDMS_meeting_select_clinics3_2025=4) Integrating CNN with LSTM Models for Capturing Spatiotemporal Dynamics of Flood Modeling
}}
{{CSDMS meeting abstract yes no 2025
|CSDMS meeting abstract submit 2025=Yes
}}
{{CSDMS meeting abstract poster Epub 2025
|CSDMS meeting poster Epub submit 2025=Poster
}}
{{CSDMS meeting abstract title temp2025
|CSDMS meeting abstract title=Accelerate Bed-Change Simulations Using Spatiotemporal Deep Learning Framework
|Working_group_member_WG_FRG=Hydrology Focus Research Group, Geodynamics Focus Research Group
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Virginia
|CSDMS meeting coauthor last name abstract=Smith
|CSDMS meeting coauthor institute / Organization=Villanova University
|CSDMS meeting coauthor town-city=Villanova
|CSDMS meeting coauthor country=United States
|State=Pennsylvania
|CSDMS meeting coauthor email address=virginia.smith@villanova.edu
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Zihan
|CSDMS meeting coauthor last name abstract=Liu
|CSDMS meeting coauthor institute / Organization=Villanova University
|CSDMS meeting coauthor town-city=Villanova
|CSDMS meeting coauthor country=United States
|State=Pennsylvania
|CSDMS meeting coauthor email address=zliu5@villanova.edu
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Anjali M.
|CSDMS meeting coauthor last name abstract=Fernandes
|CSDMS meeting coauthor institute / Organization=Denison University
|CSDMS meeting coauthor town-city=Granville
|CSDMS meeting coauthor country=United States
|State=Ohio
|CSDMS meeting coauthor email address=fernandesa@denison.edu
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Michael T.
|CSDMS meeting coauthor last name abstract=Hren
|CSDMS meeting coauthor institute / Organization=University of Connecticut
|CSDMS meeting coauthor town-city=Storrs
|CSDMS meeting coauthor country=United States
|State=Connecticut
|CSDMS meeting coauthor email address=michael.hren@uconn.edu
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Dennis O.
|CSDMS meeting coauthor last name abstract=Terry
|CSDMS meeting coauthor institute / Organization=Temple University
|CSDMS meeting coauthor town-city=Philadelphia
|CSDMS meeting coauthor country=United States
|State=Pennsylvania
|CSDMS meeting coauthor email address=dennis.terry@temple.edu
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=C.
|CSDMS meeting coauthor last name abstract=Nataraj
|CSDMS meeting coauthor institute / Organization=Villanova University
|CSDMS meeting coauthor town-city=Villanova
|CSDMS meeting coauthor country=United States
|State=Pennsylvania
|CSDMS meeting coauthor email address=c.nataraj@villanova.edu
}}
{{CSDMS meeting abstract template 2025
|CSDMS meeting abstract=Traditional sediment transport models are constrained by their spatial and/or temporal resolution, requiring high computational power and consuming extensive processing time. This limitation poses a substantial challenge in modeling two-dimensional landscape evolution over long-term scales and in predicting the impacts of system dynamics, such as climate change. To address this shortcoming, we introduce a novel Deep Learning (DL) framework that integrates Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) to capture the spatiotemporal morphodynamics of rivers in response to flooding events. This framework combines three models to simulate essential hydrodynamic and morphodynamic features: water depth, flow velocity, and bed change, which operate in a continuous loop to ensure dynamic updates to bed topography. The proposed framework was trained and evaluated using a dataset generated by HEC-RAS, a physics-based model, for a 22 km segment of Ninnescah River in Kansas. The hydrodynamic results demonstrate proficiency in capturing the flood dynamics, where the average Root Mean Square Error (RMSE) across the testing dataset is 0.19 m and 0.04 m/s for water depth and flow velocity, respectively. These hydrodynamic features are essential for the bed-change model, which exhibits high accuracy with a normalized RMSE and an R2 of 27% and 0.93, respectively, at the end of the testing dataset. Furthermore, the trained framework can generate predictions 4700 times faster than HEC-RAS. This work signifies a paradigm shift in the long-term simulation of river evolution and sets the stage for exploring new frontiers in fluvial morphodynamic modeling.
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2025 CSDMS meeting-027

2025-02-08T14:56:07Z

FathiSaid: Created page with "{{CSDMS meeting personal information template-2025 |CSDMS meeting first name=Mohamed Fathi |CSDMS meeting last name=Said |CSDMS meeting institute=Florida Gulf Coast University |CSDMS meeting city=Fort Myers |CSDMS meeting country=United States |CSDMS meeting state=Florida |CSDMS meeting email address=m.fathi.said0@gmail.com |CSDMS meeting phone=4845819234 }} {{CSDMS meeting select clinics1 2025 |CSDMS_meeting_select_clinics1_2025=4) Get lazy with LLMs }} {{CSDMS meeting..."

User:FathiSaid

2025-02-08T14:42:23Z

FathiSaid: Created page with "{{Signup information member |First name member=Mohamed Fathi |Last name member=Said |Institute member=Florida Gulf Coast University |City member=Fort Myers |Country member=United States |State member=Florida |Confirm email member=m.fathi.said0@gmail.com |Cell phone member=4845819234 |Working group member=Hydrology Focus Research Group, Artificial Intelligence & Machine Learning Initiative, River Network Modeling Initiative |Emaillist group member=yes |Memberagreement=I h..."

{{Signup information member
|First name member=Mohamed Fathi
|Last name member=Said
|Institute member=Florida Gulf Coast University
|City member=Fort Myers
|Country member=United States
|State member=Florida
|Confirm email member=m.fathi.said0@gmail.com
|Cell phone member=4845819234
|Working group member=Hydrology Focus Research Group, Artificial Intelligence & Machine Learning Initiative, River Network Modeling Initiative
|Emaillist group member=yes
|Memberagreement=I have read and agree to the Privacy Policy
}}