Παρακολούθηση
Farshid Rahmani
Farshid Rahmani
PhD Candidate, Pennsylvania State University
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα psu.edu
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Παρατίθεται από
Παρατίθεται από
Έτος
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
F Rahmani, K Lawson, W Ouyang, A Appling, S Oliver, C Shen
Environmental Research Letters, 2021
892021
Differentiable modelling to unify machine learning and physical models for geosciences
C Shen, AP Appling, P Gentine, T Bandai, H Gupta, A Tartakovsky, ...
Nature Reviews Earth & Environment 4 (8), 552-567, 2023
432023
Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
F Rahmani, C Shen, S Oliver, K Lawson, A Appling
Hydrological processes, 2021
432021
A multiscale deep learning model for soil moisture integrating satellite and in situ data
J Liu, F Rahmani, K Lawson, C Shen
Geophysical Research Letters 49 (7), e2021GL096847, 2022
352022
Optimal operation of water distribution systems using a graph theory–based configuration of district metered areas
F Rahmani, K Muhammed, K Behzadian, R Farmani
Journal of Water Resources Planning and Management 144 (8), 04018042, 2018
232018
Rehabilitation of a water distribution system using sequential multiobjective optimization models
F Rahmani, K Behzadian, A Ardeshir
Journal of water resources planning and management 142 (5), C4015003, 2016
232016
Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning
T Bindas, WP Tsai, J Liu, F Rahmani, D Feng, Y Bian, K Lawson, C Shen
Water Resources Research 60 (1), e2023WR035337, 2024
16*2024
A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds
GK Saha, F Rahmani, C Shen, L Li, R Cibin
Science of the Total Environment 878, 162930, 2023
132023
Differentiable modeling to unify machine learning and physical models and advance Geosciences
C Shen, AP Appling, P Gentine, T Bandai, H Gupta, A Tartakovsky, ...
arXiv preprint arXiv:2301.04027, 2023
112023
Sequential multi-objective evolutionary algorithm for a real-world water distribution system design
F Rahmani, K Behzadian
Procedia Engineering 89, 95-102, 2014
102014
Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration
Y Khoshkalam, AN Rousseau, F Rahmani, C Shen, K Abbasnezhadi
Journal of Hydrology 622, 129682, 2023
82023
Differentiable modeling to unify machine learning and physical models and advance Geosciences, arXiv
C Shen, AP Appling, P Gentine, T Bandai, H Gupta, A Tartakovsky, ...
arXiv preprint arXiv:2301.04027, 2023
82023
Optimal rehabilitation strategy in water distribution systems considering reduction in greenhouse gas emissions
F Rahmani, A Ardeshir, K Behzadian, F Jalilsani
11th International Conference on Hydroinformatics, 2014
52014
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1. 0) with potential applications for crop threats
J Liu, D Hughes, F Rahmani, K Lawson, C Shen
Geoscientific Model Development 16 (5), 2023
32023
Identifying structural priors in a hybrid differentiable model for stream water temperature modeling
F Rahmani, A Appling, D Feng, K Lawson, C Shen
Water Resources Research 59 (12), e2023WR034420, 2023
12023
Evaluating a Global Soil Moisture dataset from a Multitask Model (GSM3 v1. 0) for current and emerging threats to crops
J Liu, D Hughes, F Rahmani, K Lawson, C Shen
Geoscientific Model Development Discussions 2022, 1-23, 2022
12022
Process learning of stream temperature modelling using deep learning and big data
F Rahmani, K Lawson, A Appling, S Oliver, C Shen
AGU Fall Meeting 2021, 2021
12021
Differentiable modeling for global water resources under global change
C Shen, Y Song, F Rahmani, T Bindas, D Aboelyazeed, K Sawadekar, ...
EGU24, 2024
2024
Leveraging the two-way relationship between stream water temperature and streamflow in a hybrid differentiable modeling framework
F Rahmani, A Appling, K Lawson, C Shen
AGU23, 2023
2023
Improving Streamflow Predictions in Ungauged Basins Using LSTM-based Physics-Informed Transfer Learning Models
Y Khoshkalam, AN Rousseau, F Rahmani, C Shen, K Abbasnezhadi
AGU23, 2023
2023
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