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.

翻译：智能自动化借助最新技术进步，帮助我们应对气旋、干旱和地震事件。算法学习已推动神经科学、遗传学和人机交互等领域的发展。时间序列数据促进了这一进展。然而，在传统领域中采用这些方法仍面临挑战。神经网络存在可解释性和偏差问题。人工智能在科学领域的扩展得益于其适应性描述符和组合论证。本文重点利用 VANYA 模型，结合捕食者-猎物动力学，对森林损失进行建模。VANYA 可预测森林覆盖情况，并在亚马逊雨林数据上进行了验证，与其他预测模型如长短期记忆网络、N-BEATS 和循环卷积网络进行了对比。