Datasets

Subject
Creator
Type
Date

subject: Machine Learning date: 2020

Total is 3 Results
Data of global wetland methane emissions from artificial neural network modeling v1.0

10.4231/3YX4-EY30

Licheng Liu, 0000-0002-9649-1056, Qianlai Zhuang, 0000-0002-4536-9851

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

Structural attributes derived from Google Street View imagery, Louisiana coastal zone

10.4231/1TZX-BR43

David R Johnson, 0000-0002-2364-340X, Ed Delp, 0000-0002-2909-7323, Fu-Chen Chen, 0000-0002-6396-2798, 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

Evaluating Economic Opportunities for Product Recycling via the Sherwood Principle and Machine Learning - Supporting Information

10.4231/E80W-7941

Aihua Huang, John W. Sutherland, 0000-0002-2118-0907, Sidi Deng, 0009-0003-9234-1952, Xiaoyu Zhou, 0000-0002-5083-1713, Yuehwern Yih, 0000-0003-2087-7718

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

Display #

Results 1 - 3 of 3