Post‐processing the national water model with long short‐term memory networks for streamflow predictions and model diagnostics
JM Frame, F Kratzert, A Raney… - JAWRA Journal of …, 2021 - Wiley Online Library
We build three long short‐term memory (LSTM) daily streamflow prediction models (deep
learning networks) for 531 basins across the contiguous United States (CONUS), and …
learning networks) for 531 basins across the contiguous United States (CONUS), and …
Metamorphic Testing of Machine Learning and Conceptual Hydrologic Models
Predicting the response of hydrologic systems to modified driving forces, beyond patterns
that have occurred in the past, is of high importance for estimating climate change impacts or …
that have occurred in the past, is of high importance for estimating climate change impacts or …
Metamorphic testing of machine learning and conceptual hydrologic models
Predicting the response of hydrologic systems to modified driving forces beyond patterns
that have occurred in the past is of high importance for estimating climate change impacts or …
that have occurred in the past is of high importance for estimating climate change impacts or …
A systematic review of deep learning applications in streamflow data augmentation and forecasting
The volume and variety of Earth data have increased as a result of growing attention to
climate change and, subsequently, the availability of large-scale sensor networks and …
climate change and, subsequently, the availability of large-scale sensor networks and …
Transfer learning to improve streamflow forecasts in data sparse regions
Effective water resource management requires information on water availability, both in
terms of quality and quantity, spatially and temporally. In this paper, we study the …
terms of quality and quantity, spatially and temporally. In this paper, we study the …
Attention-based Domain Adaptation Forecasting of Streamflow in Data-Sparse Regions
R Oruche, F O'Donncha - arXiv preprint arXiv:2302.05386, 2023 - arxiv.org
Streamflow forecasts are critical to guide water resource management, mitigate drought and
flood effects, and develop climate-smart infrastructure and governance. Many global …
flood effects, and develop climate-smart infrastructure and governance. Many global …
Efficient Extraction of Insights at the Edges of Distributed Systems
The recent advances in Graph Neural Networks (GNN) are poised to improve machine
learning of IoT systems at the edge. Particularly, GNNs allow modeling the topology of …
learning of IoT systems at the edge. Particularly, GNNs allow modeling the topology of …
Forecasting soil moisture using domain inspired temporal graph convolution neural networks to guide sustainable crop management
Climate change, population growth, and water scarcity present unprecedented challenges
for agriculture. This project aims to forecast soil moisture using domain knowledge and …
for agriculture. This project aims to forecast soil moisture using domain knowledge and …
EvaNet: Elevation-Guided Flood Extent Mapping on Earth Imagery
Accurate and timely mapping of flood extent from high-resolution satellite imagery plays a
crucial role in disaster management such as damage assessment and relief activities …
crucial role in disaster management such as damage assessment and relief activities …
Robustness of the long short-term memory network in rainfall-runoff prediction improved by the water balance constraint
While the water balance constraint is fundamental to catchment hydrological models, there
is yet no consensus on its role in the long short-term memory (LSTM) network. This paper is …
is yet no consensus on its role in the long short-term memory (LSTM) network. This paper is …