Feuer- und Quellenvorhersage mit Dynamischer Gewichtungsbasierter Ensemblemodellierung und Probabilistischer Ursachesbestimmung

Details

Veröffentlichungs-Nr.

EP4776193

Anmeldetag

5. Dezember 2025

Veröffentlichung

15. Juli 2026

Rechtsraum

EP

IPC

G06N20/20, G06N7/01

Abstract

In traditional ensemble modeling multiple individual models are trained on the same set of input variables without considering variations in spatial and temporal resolutions. A method and system for dynamic forest fire prediction and source prediction is proposed using dynamic weighting-based ensemble modeling and probabilistic cause determination. The adaptive ensemble model dynamically re-weights multiple base models, each optimized for different data scales, to provide accurate predictions based on the spatial and temporal granularity of incoming data. The method enables adjusting fire predictions probabilities due to shifts in the feature space caused by phenological and environmental changes. Key environmental variables are continuously monitored for recalibrating model predictions using shift detection algorithms, ensuring long-term accuracy without frequent retraining. Probabilistic determination is applied for likely causes of forest fires, which integrates data from various sources to provide a nuanced, data-driven understanding of the factors contributing to fire outbreaks.

Anmelder

Firma

Tata Consultancy Services Limited

Land

🇮🇳 Indien

Fachgebiete