Parameter identification based on statistical and neural network approaches for the vegetation-water model.

Area of Science:

• Ecological modeling
• Computational biology
• Climate science

Background:

• Turing patterns in vegetation-water models present complex spatial structures.
• Parameter identification for these patterns is a challenging inverse problem.
• Climate data (precipitation, temperature, CO2) influences vegetation dynamics.

Purpose of the Study:

• To present and compare statistical and deep learning methods for parameter identification in a vegetation-water model.
• To evaluate the accuracy and robustness of different parameter identification approaches.
• To enhance the predictive capacity of vegetation-water models under climate change.

Main Methods:

• Statistical parameter identification using handcrafted image features and distance metrics.
• Deep learning approaches: modified ResNet50 with regression and regularization.
• Improved VGG19 utilizing Gaussian Error Linear Unit (GELU) and mixed-precision training.

Main Results:

• Deep learning methods demonstrated superior accuracy and robustness over the statistical approach.
• ResNet50 achieved the best overall performance in parameter identification.
• Normalized Difference Vegetation Index (NDVI) data validated the simulation results.

Conclusions:

• Deep learning significantly enhances parameter identification for vegetation-water models.
• Improved parameterization leads to better predictive capacity for vegetation patterns.
• This research provides valuable tools for understanding ecosystem responses to climate change.