Volterra Accentuated Non-Linear Dynamical Admittance (VANYA) to model Deforestation: An Exemplification from the Amazon Rainforest

TL;DR

This paper introduces the VANYA model, which uses Volterra series and prey-predator dynamics to predict deforestation, demonstrated on Amazon Rainforest data and compared with other forecasting methods.

Contribution

The paper presents a novel VANYA model integrating Volterra series with prey-predator dynamics for improved deforestation prediction.

Findings

VANYA outperforms LSTM, N-BEATS, and RCN in forest cover prediction.

The model effectively captures non-linear dynamics of deforestation.

Results demonstrate the model's potential for environmental monitoring.

Abstract

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.