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不確実性を考慮した最適化とLLMエージェントを統合した灌漑地区の水-エネルギー-炭素ネクサス管理

Integrating LLM-based agents with uncertainty-aware optimization for water-energy-carbon nexus management in irrigation districts. (原題)

Lei Yu, Zhikai Li, Kai Huang, Fu-Lin Li, Zixiang Lu, Yurui Fan, Ya-Nan Jiang, Chenglong Zhang, Guangtao Fu

Water Research📚 査読済 / ジャーナル2026-08-01#AI×ESGOrigin: CN経営インパクト: コスト削減対象セクター: agriculture
DOI: 10.1016/j.watres.2026.126740
原典: https://doi.org/10.1016/j.watres.2026.126740

🤖 gxceed AI 要約

日本語

灌漑地区の水-エネルギー-炭素（WEC）ネクサス管理のためのLLMエージェント駆動型最適化フレームワークを開発。3つのエージェント（タスク解析、アルゴリズム実行、結果解析）で構成され、自然言語指示を自動処理し、多目的ファジー確信度制約モデル（NSGA-III、AHP-TOPSIS統合）を実行。4種類の指示で100%のタスク完了率を達成。実証では、信頼度レベルを0.5から1.0に上げると、水不足、汚染物質排出、炭素排出が増加し、純経済便益が減少するトレードオフを定量化。

English

This study develops an LLM-agent-driven optimization framework for water-energy-carbon (WEC) nexus management in irrigation districts. It uses three sequential agents (task analysis, algorithm execution, result analysis) to automate parsing, execution, and reporting, achieving 100% task completion across four instruction types. Applied to the Zhaokou Yellow River Irrigation Area, it quantifies trade-offs: increasing credibility from 0.5 to 1.0 raises water shortage, pollutant emissions, and carbon emissions while reducing net economic benefit.

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

日本の農業水利や水管理において、AIを活用した意思決定支援は今後重要になる。特に、気候変動適応やカーボンニュートラル政策（みどりの食料システム戦略）との整合性が求められる中、本フレームワークは水・エネルギー・炭素の統合管理の実践例を提供する。

In the global GX context

Globally, this work contributes to the growing field of AI-driven environmental management, aligning with sustainability disclosure and climate risk assessment. It demonstrates how LLMs can enhance decision-support systems for resource management, relevant to ISSB and CSRD reporting on water and carbon impacts.

👥 読者別の含意

🔬研究者:Provides a novel integration of LLM agents with multi-objective optimization for WEC nexus, offering a template for AI-driven environmental management research.

🏢実務担当者:Offers an interactive decision-support tool for irrigation managers to balance water, energy, and carbon trade-offs under uncertainty.

🏛政策担当者:Highlights the potential of AI to improve resource efficiency and inform policy on water and carbon management in agriculture.

📄 Abstract（原文）

Efficient irrigation water use is vital for food security, economic returns and ecological protection, yet it faces multiple uncertainties within the water-energy-carbon (WEC) nexus. Conventional optimization models are often limited by complexity, lack of interpretability, and poor alignment with routine management. This study develops an LLM-agent driven intelligent optimization framework for WEC-coupled irrigation management. The framework comprises three sequential agents: a task analysis agent that converts natural language instructions into standardized model configurations; an algorithm execution agent that runs a scenario-based multi-objective fuzzy-credibility constrained programming model (integrated with NSGA-III and AHP-TOPSIS); and a result analysis agent that interprets optimization outputs, compares alternative schemes, identifies trade-offs, and generates structured decision reports. A hybrid LLM setup uses DeepSeek-V4-Flash for task parsing and Qwen3.6-Plus for decision analysis. Applied to four instruction types (standard, punctuation-free, swapped word-order, and ambiguous scenarios), the framework achieved 100% task completion without manual intervention, demonstrating efficiency in automated parsing, execution, and reporting. In the Zhaokou Yellow River Diversion Irrigation Area Phase II, the framework quantified critical management trade-offs. Under the 75% hydrological frequency, increasing the credibility level from 0.5 to 1.0 increases water shortage by 9.43×106 m3, pollutant emissions by 0.18×103 tonnes, carbon emissions by 0.05×106 tonnes, and decreases net economic benefit by 2.56×106 CNY. By improving accessibility and interpretability, this framework offers an interactive decision-support pathway for irrigation water management under hydrological and parametric uncertainties.

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