自然基盤ソリューションにおける土壌水理パラメータのハイブリッド感度分析：HYDRUS-1Dシミュレーションとデータ駆動型解釈の統合
Hybrid Sensitivity Analysis of Soil Hydraulic Parameters in Nature-Based Solution Coupling Hydrus-1D Simulations with Data-Driven Interpretation (原題)
Anna Chiara Brusco, Michele Turco, Patrizia Piro
🤖 gxceed AI 要約
日本語
都市の自然基盤ソリューション（NBS）の基盤材の水理応答を予測する枠組みを提案。HYDRUS-1Dによる数値シミュレーションと機械学習・統計解析を組み合わせ、Van Genuchtenパラメータの影響を定量化した。モンテカルロシミュレーションにより、飽和透水係数とαパラメータが排水性能の主要因であることを特定。粗粒質の基盤材が最良の性能を示し、設計最適化への実践的指針を提供する。
English
This study proposes an integrated framework to predict hydrological response of substrates in urban nature-based solutions (NBS), combining HYDRUS-1D simulations with machine learning and statistical analysis. Monte Carlo simulations identified saturated hydraulic conductivity and the α parameter as dominant controls on drainage performance. Coarse-textured substrates showed the best performance, offering practical guidance for NBS design and calibration in urban contexts.
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
日本の都市緑化やグリーンインフラ整備において、NBSの設計最適化は気候変動適応策として重要。本手法は、都市の雨水管理やヒートアイランド対策に資する基盤材選定の定量的根拠を提供し、自治体や設計実務者にとって有用。
In the global GX context
Globally, NBS are recognized for climate adaptation and urban resilience. This study provides a data-driven method to optimize substrate design, which is relevant for urban planners and engineers working on sustainable drainage systems. The integration of ML with hydrological modeling offers a replicable approach for performance prediction and calibration.
👥 読者別の含意
🔬研究者:Methodological integration of HYDRUS-1D with ML for sensitivity analysis in NBS design.
🏢実務担当者:Guidance on selecting substrate types for efficient drainage in green infrastructure projects.
📄 Abstract（原文）
Abstract This study proposes an integrated predictive framework for analysis of substrates’ hydrological response in urban nature-based solutions (NBS), combining numerical modeling with HYDRUS-1D, probabilistic techniques, and advanced tools of statistical analysis and machine learning. The aim is to quantify the influence of Van Genuchten’s hydraulic parameters on flow generation and identify critical factors for optimized green infrastructures’ design. Monte Carlo simulations were carried out on eight parameterized families of NBS substrates, each represented by about 1,000 combinations of hydraulic parameters derived from laboratory analysis. The results show that the most influential parameters are saturated hydraulic conductivity ( Ksat ), parameter α (retention curve position), and, in some cases, parameter n (curve slope). The simulations returned average annual outflow values ranging from − 4,096 mm (less efficient substrate) to − 4,336 mm (more draining substrate), with standard deviations between 1.2 mm (high stability) and 188 mm (high instability). Among the analyzed substrates, through regression analysis, the coarse-textured T5, T6, and T7 (sand- and gravel-rich growing media with high hydraulic conductivity) showed the best overall performance, combining high drainage efficiency, robust model predictability, and a dominant influence of the α parameter. The parameters α and Ksat have emerged as dominant drivers in the most draining substrates, while θ s (saturated water content) and n are critical in substrates with intermediate retention capacity. The methodology also integrates correlation analysis (Pearson, Spearman), partial dependence plot, and nonparametric bootstrap, providing a robust and multiperspective diagnosis of hydraulic sensitivity. The results identify the most stable and predictable substrate configurations and provide practical guidance for NBS design and calibration in complex urban scenarios.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1061/jwrmd5.wreng-7244first seen 2026-09-09 05:01:28
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