Environmental Modelling & Software最新文献_第10页

AI-assisted voice enabled computing framework for hydrological analysis 用于水文分析的ai辅助语音计算框架

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-12-18 DOI: 10.1016/j.envsoft.2025.106833

Carlos Erazo Ramirez , Ibrahim Demir

{"title":"AI-assisted voice enabled computing framework for hydrological analysis","authors":"Carlos Erazo Ramirez , Ibrahim Demir","doi":"10.1016/j.envsoft.2025.106833","DOIUrl":"10.1016/j.envsoft.2025.106833","url":null,"abstract":"<div><div>This work presents a web-based, voice-enabled, no-code platform for AI-assisted hydrological analysis. The system allows users to interact through natural language—via both text and speech—to retrieve data, utilize hydrological functions, and visualize spatial and analytical outputs. Core components include a conversational AI assistant utilizing Large Language Models, a modular analysis engine based on HydroSuite, and direct integration with hydrological data from federal agencies using HydroShare and other data and web services. Structured intent parsing, persistent session state, and dynamic map-layer control support multi-turn interactions and reproducible workflows. A case study over the Mississippi River Delta demonstrates how the platform enables guided exploration, layered data integration, and low-latency execution with minimal technical overhead. The platform lowers barriers for research, education, and decision-making in hydrology by combining AI reasoning with a transparent, accessible user interface. By enabling natural language interaction, data integration, and reproducible, multi-turn task processing, this system lays the foundation for automated hydrological research and operational workflows.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106833"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hydro3DJS: A modular web-based library for real-time 3D visualization of watershed dynamics and digital twin integration Hydro3DJS：一个基于web的模块化库，用于流域动态和数字孪生集成的实时3D可视化

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-12-25 DOI: 10.1016/j.envsoft.2025.106853

Ramteja Sajja , Omer Mermer , Yusuf Sermet , Ibrahim Demir

{"title":"Hydro3DJS: A modular web-based library for real-time 3D visualization of watershed dynamics and digital twin integration","authors":"Ramteja Sajja , Omer Mermer , Yusuf Sermet , Ibrahim Demir","doi":"10.1016/j.envsoft.2025.106853","DOIUrl":"10.1016/j.envsoft.2025.106853","url":null,"abstract":"<div><div>Effective visualization of hydrological data is essential for interpreting flood risk and supporting environmental decision-making. This study presents Hydro3DJS, a lightweight, browser-native 3D visualization library that integrates real-time environmental data with interactive digital-twin capabilities. Built with JavaScript, WebGL, and the Google Maps API, the system renders rainfall, floodwater dynamics, and insfrastructure exposure within spatially accurate environments while incorporating live data from USGS and NOAA. Its modular design supports terrain-aware water animation, customizable 3D infrastructure models, and scenario-based simulations suitable for research, planning, and communication. Two use cases, a rural multi-hazard simulation and an urban 100-year flood event with stream gauge overlays, demonstrate cross-context applicability. A usability study with 20 domain experts highlights the platform's strengths and identifies opportunities for enhanced interactivity and performance. Hydro3DJS contributes an accessible open-source framework for real-time 3D hydrological visualization, modular environmental data integration, and improved stakeholder communication in flood and watershed analysis.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106853"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blockchain-based carbon informatics for multiple stakeholders using spatial-temporal analytics 使用时空分析为多个利益相关者提供基于区块链的碳信息

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.envsoft.2026.106861

Songyang Ruan , Xinlai Liu , H. Oliver Gao

{"title":"Blockchain-based carbon informatics for multiple stakeholders using spatial-temporal analytics","authors":"Songyang Ruan , Xinlai Liu , H. Oliver Gao","doi":"10.1016/j.envsoft.2026.106861","DOIUrl":"10.1016/j.envsoft.2026.106861","url":null,"abstract":"<div><div>Effective carbon disclosure is critical for climate action but is often constrained by limited transparency, low credibility, and fragmented data. This study develops a blockchain-based carbon informatics platform integrated with spatial-temporal analytics to enhance traceability and accountability in the transportation sector. Following a stakeholder-oriented design architecture, the platform is implemented on a permissioned blockchain to meet the needs of carbon entities, regulators, investors, auditors, and the public. Leveraging transportation emissions data from over 3000 counties in the United States (1990–2020), the proposed blockchain platform presents transparent data recording, secure data exchange, and dynamic visualization through interactive carbon dashboard services. Spatial panel econometric analysis identifies significant spatial-temporal autocorrelation in county-level emissions, wherein clean fuel adoption, higher income, and greater educational attainment correspond to lower emission levels. This study demonstrates the integration of blockchain-based informatics with spatial-temporal analytics can support credible carbon disclosure and facilitate data-driven climate governance.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106861"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PMAR: A Lagrangian approach to the modelling of anthropogenic pressures for marine management 海洋管理中人为压力建模的拉格朗日方法

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-12-05 DOI: 10.1016/j.envsoft.2025.106822

Sofia Bosi , Alessandra Raffaetà , Marta Simeoni , Nikola Bobchev , Dimitar Berov , Andrea Barbanti , Stefano Menegon

{"title":"PMAR: A Lagrangian approach to the modelling of anthropogenic pressures for marine management","authors":"Sofia Bosi , Alessandra Raffaetà , Marta Simeoni , Nikola Bobchev , Dimitar Berov , Andrea Barbanti , Stefano Menegon","doi":"10.1016/j.envsoft.2025.106822","DOIUrl":"10.1016/j.envsoft.2025.106822","url":null,"abstract":"<div><div>PMAR (Pressure models for MARine activities) is a modelling framework and open-source Python-based software designed to assess anthropogenic pressures for marine management. PMAR uses Lagrangian trajectories calculated from ocean models to simulate pressure dispersion. An explicit link between pressures and their sources is established through a weight-based mechanism, which allows to rapidly explore different pressure source scenarios. Here, PMAR is adopted to investigate the distribution of surface macroplastics in the Black Sea under two scenarios, yielding results consistent with previous modelling studies and observations. Over 50% of the macroplastics in each Black Sea country after a yearly run came from elsewhere, stressing the importance of cross-boundary cooperation. Compared to other pressure modelling frameworks, PMAR emerges as a balanced compromise between computational efficiency and predictive accuracy. Future work will focus on making PMAR accessible to decision-makers and aligning it with requirements of Digital Twin of the Ocean applications.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106822"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing significant wave height forecasting through Ensemble Patch-TST and attention-enhanced recurrent models 通过集合Patch-TST和注意增强循环模型优化显著波高预报

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-12-02 DOI: 10.1016/j.envsoft.2025.106803

Zaharaddeeen Karami Lawal , Hayati Yassin , Daphne Teck Ching Lai , Azam Che Idris

{"title":"Optimizing significant wave height forecasting through Ensemble Patch-TST and attention-enhanced recurrent models","authors":"Zaharaddeeen Karami Lawal , Hayati Yassin , Daphne Teck Ching Lai , Azam Che Idris","doi":"10.1016/j.envsoft.2025.106803","DOIUrl":"10.1016/j.envsoft.2025.106803","url":null,"abstract":"<div><div>Accurate significant wave height (SWH) forecasting is pivotal for maritime safety, coastal engineering, and offshore operations. This study explores three advanced deep learning models: Ensemble Patch-TST, LSTM with Attention Mechanism (SWH-AT-LSTM), and GRU with Attention Mechanism (SWH-AT-GRU) for SWH forecasting across multiple forecast horizons. The innovative Ensemble Patch-TST model combines the strengths of ensemble learning and transfer learning to achieve state-of-the-art performance, particularly in medium- and long-term predictions. Using datasets from four distinct offshore stations, the study evaluates each model's ability to capture temporal dependencies and generalize across diverse environmental conditions. Results demonstrate that the Ensemble Patch-TST outperforms baseline Patch-TST and other state-of-the-art models, achieving superior accuracy and robustness, particularly for datasets with limited training samples. This work not only highlights the transformative potential of advanced deep learning techniques in enhancing maritime forecasting systems but also emphasizes their scalability and efficiency, providing a practical and effective approach for real-world applications.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106803"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An enhanced python framework for hydrological modeling in alpine catchments: Snow hysteresis and glacier ice melt 一个用于高山流域水文建模的增强Python框架：雪滞回和冰川冰融化

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-12-19 DOI: 10.1016/j.envsoft.2025.106842

Martin Masten , Simon Seelig , Matevž Vremec , Magdalena Seelig , Gerfried Winkler

{"title":"An enhanced python framework for hydrological modeling in alpine catchments: Snow hysteresis and glacier ice melt","authors":"Martin Masten , Simon Seelig , Matevž Vremec , Magdalena Seelig , Gerfried Winkler","doi":"10.1016/j.envsoft.2025.106842","DOIUrl":"10.1016/j.envsoft.2025.106842","url":null,"abstract":"<div><div>Simulating snow cover and glacier ice melt is essential for understanding hydrological processes in high-alpine catchments. We present a new Python extension to the Rainfall-Runoff Modeling Playground (RRMPG) that incorporates two key alpine-specific processes: snow cover hysteresis and glacier ice melt. Snow hysteresis captures the asymmetric evolution of snow-covered area between accumulation and melt periods, while glacier melt modeling is crucial in glacierized catchments due to its strong influence on water balance. The model is tested in two catchments in the Ötztal Alps and shows high accuracy in simulating runoff and snow cover dynamics. A multi-objective calibration approach using observed runoff and MODIS snow cover data improves model robustness. Designed for modularity and interoperability, the framework integrates easily with tools for calibration, sensitivity analysis, and data visualization. This open-source extension advances hydrological modeling in complex alpine environments by offering enhanced process representation, flexibility, and compatibility with Python-based workflows.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106842"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A flexible, differentiable framework for neural-enhanced hydrological modeling: Design, implementation, and applications with HydroModels.jl 一个灵活的、可微分的神经增强水文建模框架：hydromodels的设计、实现和应用

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-11-28 DOI: 10.1016/j.envsoft.2025.106802

Xin Jing, Xue Yang, JunGang Luo, GangGang Zuo

{"title":"A flexible, differentiable framework for neural-enhanced hydrological modeling: Design, implementation, and applications with HydroModels.jl","authors":"Xin Jing, Xue Yang, JunGang Luo, GangGang Zuo","doi":"10.1016/j.envsoft.2025.106802","DOIUrl":"10.1016/j.envsoft.2025.106802","url":null,"abstract":"<div><div>Integrating deep learning with hydrological models is a popular research direction; however, this field faces significant challenges due to automatic differentiation requirements and interface incompatibilities, leading to many existing hydrological modeling frameworks being unable to perform effective hybrid modeling. To fill this gap, we propose a framework that inherits and enhances the design philosophies of previous modeling frameworks. It utilizes symbolic programming to reduce the difficulty of hydrological modeling, particularly for hybrid models integrating deep learning, supports automatic differentiation for model optimization, and effectively addresses the diverse and evolving needs for both specialized hydrological and hybrid modeling applications. This framework, named HydroModels.jl, is implemented in the Julia programming language, is publicly accessible on GitHub, and is accompanied by detailed documentation. This study describes its architecture and implementation details, and presents two case studies as examples to demonstrate its integration capabilities and applicability.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106802"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145613576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visualization of Urban Digital Twins on the web with attribute-driven adaptive tiling 基于属性驱动自适应平铺的城市数字孪生网络可视化

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2026-01-06 DOI: 10.1016/j.envsoft.2026.106863

Ziya Usta , Alper Tunga Akın , Ken Arroyo Ohori , Jantien Stoter

引用次数: 0

The HYDRUS model for soil and water management: A brief review of capabilities, trends, and future directions 用于水土管理的HYDRUS模型：能力、趋势和未来方向的简要回顾

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-11-26 DOI: 10.1016/j.envsoft.2025.106801

V. Jayasuriya , Prabha Susan Philip , T. Jyolsna

引用次数: 0

Bayesian-factorial analysis for unveiling multi-factor interactive effect on water demand in Central Asia 揭示中亚地区需水量多因素交互效应的贝叶斯析因分析

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2026-02-01 Epub Date: 2025-11-30 DOI: 10.1016/j.envsoft.2025.106806

Yanxiao Zhou , Yongping Li , Guohe Huang , Zhenyao Shen , Yufei Zhang

{"title":"Bayesian-factorial analysis for unveiling multi-factor interactive effect on water demand in Central Asia","authors":"Yanxiao Zhou , Yongping Li , Guohe Huang , Zhenyao Shen , Yufei Zhang","doi":"10.1016/j.envsoft.2025.106806","DOIUrl":"10.1016/j.envsoft.2025.106806","url":null,"abstract":"<div><div>This study advances an integrated Bayesian support vector machine-based two-step factorial analysis (abbreviated as BSVM-TFA) method for revealing the influences of human activities on water demand. The developed method can capture complex nonlinear relationships between human activities and water demand by calibrating SVM hyperparameters through Bayesian optimization, which helps prevent overfitting. BSVM-TFA can also identify the individual and interactive effects of multiple factors on water demand and screen key influencing factors. The BSVM-TFA is then applied to Central Asia, and the results show that by 2050, water demand would range from 75.66 × 10<sup>9</sup> m<sup>3</sup> to 113.23 × 10<sup>9</sup> m<sup>3</sup> under different scenarios, indicating an uncertainty of about 33.18 % driven by human activities. The key factors influencing water demand in Central Asia are GDP and agricultural irrigation efficiency (AIE), with a total contribution of 47.98 %; the water demand would be reduced by 16.42 × 10<sup>9</sup> m<sup>3</sup> with low-growth GDP and increasing AIE.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106806"},"PeriodicalIF":4.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145619718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0