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Found 9 papers, 5 papers with code
Causal Temporal Graph Convolutional Neural Networks (CTGCN)
Our CTGCN architecture is based on a causal discovery mechanism, and is capable of discovering the underlying causal processes.
A SWAT-based Reinforcement Learning Framework for Crop Management
Crop management involves a series of critical, interdependent decisions or actions in a complex and highly uncertain environment, which exhibit distinct spatial and temporal variations.
Attention-based Domain Adaptation Forecasting of Streamflow in Data-Sparse Regions
Streamflow forecasts are critical to guide water resource management, mitigate drought and flood effects, and develop climate-smart infrastructure and governance.
Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Management
The trained models are being deployed on small-holding farms in central Texas.
Transfer learning to improve streamflow forecasts in data sparse regions
In this paper, we study the methodology behind Transfer Learning (TL) through fine-tuning and parameter transferring for better generalization performance of streamflow prediction in data-sparse regions.
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales
Enabling learning across the spatial and temporal directions, this paper addresses two fundamental challenges of ML applications to environmental science: 1) data sparsity and the challenges and costs of collecting measurements of environmental conditions such as ocean dynamics, and 2) environmental datasets are inherently connected in the spatial and temporal directions while classical ML approaches only consider one of these directions.
Statistical and machine learning ensemble modelling to forecast sea surface temperature
Training data consisted of satellite-derived SST and atmospheric data from The Weather Company.
Using Deep Learning to Extend the Range of Air-Pollution Monitoring and Forecasting
Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs).
A Machine Learning Framework to Forecast Wave Conditions
These input data along with model outputs of spatially variable wave heights and characteristic period were aggregated into supervised learning training and test data sets, which were supplied to machine learning models.
Atmospheric and Oceanic Physics
