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vegesimulation's Introduction

Data Science Project - Group 25

Project name

Emulation of dynamic simulation models of vegetation growth

Background Information

  • Most of the plant growth simulation has ultra-high time and space consumption.
  • A surrogate model is a good replacement for those high consumption simulators.
  • It is a challenge to design a surrogate model so that it performs as well as the original scientific model while remaining computationally efficient remains.

Expected Outcomes

  • Short time-stamp prediction of NPP.
  • Simulated data (NPP)’s whole time-series emulation process with multiple period prediction.

Data

The data is from the JeDi-DGVM which is provided by the project manager and supervisor. Because of the security issue all data from JeDi-DGVM will not be include in the repository, only the result will be displayed

Folder Description

  • ARIMA_models: ARIMA models which are applies by the cluster.
  • ARIMA_summary: Summaries of ARIMA models which includes the key parameters, estimator, standard error and p-value.
  • cluster_models: main code which includes the process of data preprocessing, uilts, and models of cluster, ARIMA and LSTM.
  • LSTM_models: LSTM models which are applies by the cluster.
  • LSTM_summary:Summaries of LSTM models which includes the information of each layer.
  • Random_Forest: Whole process for Random_forest with data preprocessing and modeling.
  • numpy: Arrays or label which is for modeling or plotting.
  • plot: Plots for summarizing and evaluation

Client information:

Prod Peter Rayner -- Project Manager

Professor In Climate Science
School of Geography, Earth and Atmospheric Sciences of University of Melbourne

Dr Jeremy Silver -- Program Supervisor

Research Fellow In Data Science
Mathematics and Statistics of University of Melbourne

Group information

MAST90106 - Project Part-1

Presentation

Report

MAST90107 - Project Part-2

Presentation

Report

Keywords

JeDi-DGVM, Surrogate Model, Random Forest, Clustering, ARIMA, Long Short-Term Memory

Clusters display

77 Clusters Cluster on global map

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