Volterra Accentuated Non-Linear Dynamical Admittance (VANYA) to model Deforestation: An Exemplification from the Amazon Rainforest
This addresses deforestation prediction for environmental science, but appears incremental as it builds on existing time-series and ecological modeling approaches.
The paper tackles modeling deforestation by proposing the VANYA model, which incorporates prey-predator dynamics, and demonstrates its prediction of forest cover on Amazon Rainforest data, comparing it against methods like LSTM, N-BEATS, and RCN.
Intelligent automation supports us against cyclones, droughts, and seismic events with recent technology advancements. Algorithmic learning has advanced fields like neuroscience, genetics, and human-computer interaction. Time-series data boosts progress. Challenges persist in adopting these approaches in traditional fields. Neural networks face comprehension and bias issues. AI's expansion across scientific areas is due to adaptable descriptors and combinatorial argumentation. This article focuses on modeling Forest loss using the VANYA Model, incorporating Prey Predator Dynamics. VANYA predicts forest cover, demonstrated on Amazon Rainforest data against other forecasters like Long Short-Term Memory, N-BEATS, RCN.