Grok-Based Temporal Fusion Transformer Framework for Multi-Horizon Coastal Flood Risk Forecasting and Strategic Adaptation Planning
GrokベースのTemporal Fusion Transformerフレームワークによる多期間沿岸洪水リスク予測と戦略的適応計画 (AI 翻訳)
A. Mikhaylov, S. Barykin, D. Dinets, A. Ochilov, A. Kuznetsova, J. Tukhtabaev, Aslitdin Nizamov, Nodira Murodova, N. Ashurova, Tomonobu Senjyu, V. Abramov, N. Yousif
🤖 gxceed AI 要約
日本語
本論文は、最適化されたGrokアルゴリズムを用いて時系列分析の精度を向上させ、気候変動の動態を理解する手法を提案する。沿岸洪水リスクの多期間予測に適用し、適応計画のための資源配分や避難計画の最適化を図る。AIにより気候変動の監視・評価・管理能力を拡大する。
English
This paper proposes a Grok-based temporal fusion transformer framework for multi-horizon coastal flood risk forecasting. It uses the optimized Grok algorithm to improve time series analysis accuracy and support strategic adaptation planning, including resource allocation and evacuation planning. The framework enhances climate change monitoring, assessment, and management capabilities.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本は台風や津波による沿岸洪水リスクが高く、本フレームワークは自治体の防災計画や気候変動適応策に活用できる可能性がある。ただし、日本の実データでの検証が必要であり、SSBJや有報への直接的な連携は見込まれない。
In the global GX context
Globally, coastal flood risk is a growing concern under climate change, and AI-driven forecasting models can inform adaptation strategies. This paper contributes to the broader field of climate risk modeling, which is relevant for TCFD/ISSB climate scenario analysis and transition planning, though it does not directly address disclosure requirements.
👥 読者別の含意
🔬研究者:AI-based climate risk modeling methodology applicable to coastal flood forecasting and adaptation planning.
🏢実務担当者:Potential tool for developing climate adaptation plans, but requires local calibration and validation.
🏛政策担当者:Highlights AI's role in improving climate risk assessment for proactive policy making and resource allocation.
📄 Abstract（原文）
The optimized Grok algorithm can significantly improve the accuracy of time series analysis and understanding the dynamics of climate change. Fine-tuned Grok architecture can be used to monitor and analyze climate processes. The main aim is to analyze the Fine-tuned Grok architecture for research on climate change, world ecology, carbon dioxide growth, and carbon funds. The global challenges of climate change and ecological degradation demand innovative analytical approaches capable of processing vast, multivariate, and non-linear datasets. Concurrently, the global financial system, deeply intertwined with energy transitions and sustainable development, requires sophisticated tools for risk assessment and investment strategy in a changing world. Fine-tuned Grok architecture model helps to plan strategies for adaptation to climate change by calculating the optimal allocation of resources, taking into account risks and reducing losses. Due to its ability to respond quickly to new conditions, the system will be able to quickly adjust evacuation plans, deploy protective structures, and distribute assistance to affected regions. The use of artificial intelligence significantly expands the capabilities of the scientific community and authorities in monitoring, assessing, and managing climate change. The optimized Fine-tuned Grok architecture opens the way to a new level of informed decision-making about climate change and ensuring the safety of our future generations.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.30564/re.v8i1.12824first seen 2026-06-29 08:42:46
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。