10.4231/B783-2C47
Pin-ching Li
,
Sayan Dey
,
Venkatesh Mohan Merwade
01/23/2023
This resource contains codes used in the study "Analyzing the Effect of Data Splitting and Covariate Shift on Machine Leaning Based Streamflow Prediction in Ungauged Basins" published in Water Resources Research (doi: 10.1029/2023WR034464)
Artificial Neural Network (ANN) Machine Learning Random Forest streamflow prediction
10.4231/966Q-6F95
Alexandre Zimmers
,
Dmitri Basov
,
Erica Carlson
,
Forrest Simmons
,
Ivan K. Schuller
,
Lionel Aigouy
,
Lukasz Burzawa
,
Melissa Alzate Banguero
,
Mumtaz Qazilbash
,
Pavel Salev
,
Sayan Basak
04/07/2023
Codes used in "Deep Learning Hamiltonians form Disordered Image Data in Quantum Materials" https://arxiv.org/abs/2211.01490 and the resulting visualizations.
Clustering Machine Learning Physics quantum materials Scientific visualization symmetry Vanadium dioxide VO2
10.4231/50R5-EM83
Karen Hudson
,
Kranthi K Varala
,
Ying Li
01/04/2024
Organ-specific gene expression datasets that include hundreds to thousands of experiments allow reconstruction of gene regulatory networks and discovery of transcriptional regulators various pathways and processes.
Arabidopsis thaliana Gene regulatory networks k-nearest neighbor (kNN) linear support vector machines (SVM) Machine Learning Systems biology
10.4231/SCF2-QJ02
Koushiki Basu
,
Nicholas Huls
,
Shan Lu
,
Tonglei Li
11/06/2024
We implemented the Manifold Embedding of Molecular Surface approach, which retains the quantum mechanical characteristics of molecules, to predict a drug's likelihood of binding to cytochrome P450 enzymes by deep learning.
deep learning Informatics Machine Learning Molecular Pharmacology
10.4231/1TZX-BR43
David R Johnson
,
Ed Delp
,
Fu-Chen Chen
,
Mohammad Jahanshahi
07/28/2020
Structural attributes relevant to flood risk (foundation height/type, square footage, number of stories, building type), produced using machine learning for automated image analysis of GSV images.
flood risk Industrial Engineering Machine Learning structure attributes
10.4231/E80W-7941
Aihua Huang
,
John W. Sutherland
,
Sidi Deng
,
Xiaoyu Zhou
,
Yuehwern Yih
09/03/2020
This repository contains the supporting information for the manuscript regarding Sherwood principle and Machine learning. All critical underlying data files, along with a flow chart that describes the methodologies applied in the paper are enclosed.
Circular Economy Empirical Models Environmental and Ecological Engineering Machine Learning Sherwood Principle
10.4231/0A0F-7A84
10/14/2019
This project works on understanding the different statistical models that are available to analyze and predict mean sea level changes in Guam.
Climate Change Data Education Earth and Atmospheric Sciences FAIR Data Machine Learning Sea level rise Statistical analysis Statistical Methods
10.4231/3YX4-EY30
01/21/2020
Source code of an ANN model, site level data, input data, output data and visualization results, which are presented in the manuscript "Inventorying Global Wetland Methane Emissions Based on In Situ Data and an Artificial Neural Network...
Artificial Neural Network (ANN) Atmospheric Chemistry Modeling Biogeochemistry Earth and Atmospheric Sciences Interactive Data Language (IDL) Machine Learning Matlab Methane Dynamics Model (MDM) Methane Emission
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