Deep learning hybrid predictive modeling for ecosystem fluxes

Knowledge graph centered on Deep learning hybrid predictive modeling for ecosystem fluxes with 31 nodes and 81 connections. Top connected: Engelmann Spruce, Pekania pennanti, Automated Cloud Based Long Short-Term Memory Neura, Pinus ponderosa, Ponderosa pine.

Method synopsis

A novel approach combining LSTM neural networks with process-based ecological understanding to predict evapotranspiration and ecosystem respiration. The method integrates temporal pattern recognition with physical constraints from ecosystem processes.

Synthesized from method descriptions across 2 papers using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration (2021), Hydrology and Earth Systems Science. The protocol was originally introduced by Automated Cloud Based Long Short-Term Memory Neural Network Based SWE Prediction (2020), Frontiers in Water. Implementations in other papers (listed below) may differ.

1. LSTM neural network training
   Configured LSTM architecture with first layer of 50 units, second layer of 25 units, dense layer with 8 units using L2 regularization, and final dense output layer. Applied dropout layers between LSTM layers to prevent overfitting. Used 70% of data for training, 30% for validation with predefined stopping criteria based on root mean squared error improvement.
   Quantities: 50 units first LSTM layer, 25 units second layer, 8 units dense layer, 70% training data, 30% validation data, 11600 and 7600 parameters for first and second LSTM layers respectively, 208 and 9 parameters for dense layersDuration: Training continued until RMSE improvement ceased indicating convergenceConditions: Sequential time series input with memory retention across temporal steps
   Equipment: Deep learning framework, LSTM neural network architecture

Typical Equipment

• deep learning software frameworks
• machine learning software
• GPU computing resources

Output Measurements

• evapotranspiration predictions
• ecosystem respiration predictions
• net ecosystem exchange estimates

Papers Using This Protocol (2)

Species Studied With This Protocol

Places Where Used