Prediction of Gross Primary Productivity using Hybrid LSTM-GRU Neural Networks and ERA5-Reanalysis Data-Reference-Cited by-同舟云学术

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Prediction of Gross Primary Productivity using Hybrid LSTM-GRU Neural Networks and ERA5-Reanalysis Data

Published:2023-11-03 Issue: Volume: Page:94-99
ISSN:
Container-title:2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)
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Author:

Agarwal Geetika¹,Deb Burman Pramit Kumar²,Kulkarni Vrushali Y.¹,Kosamkar Pranali K.¹

Affiliation:

1. School of Engineering and Technology Maharashtra Institute of Technology,WPU,Pune,India

2. Centre of Climate Change Research, Indian Institute of Tropical Meteorology, Savitribai Phule Pune University,Department of Atmospheric and Space Sciences,Pune,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10424970/10424704/10425755.pdf?arnumber=10425755

Reference25 articles.

1. Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product

2. An Artificial Intelligence Approach to Predict Gross Primary Productivity in the Forests of South Korea Using Satellite Remote Sensing Data

3. Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation

4. An Online Deep Convolutional Model of Gross Primary Productivity and Net Ecosystem Exchange Estimation for Global Forests

5. Overview on estimation accuracy of gross primary productivity with remote sensing methods

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