Environmental Modelling & Software最新文献_第3页

AI-driven forecasting of harmful algal blooms in Persian Gulf and Gulf of Oman using remote sensing 人工智能驱动的波斯湾和阿曼湾有害藻华遥感预测

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106311

Amirreza Shahmiri, Mohamad Hosein Seyed-Djawadi, Seyed Mostafa Siadatmousavi

{"title":"AI-driven forecasting of harmful algal blooms in Persian Gulf and Gulf of Oman using remote sensing","authors":"Amirreza Shahmiri, Mohamad Hosein Seyed-Djawadi, Seyed Mostafa Siadatmousavi","doi":"10.1016/j.envsoft.2024.106311","DOIUrl":"10.1016/j.envsoft.2024.106311","url":null,"abstract":"<div><div>This study develops an artificial intelligence (AI) model to forecast harmful algal blooms (HABs) in the Persian Gulf and Gulf of Oman using freely available remote sensing data, including chlorophyll-a (Chl-a), sea surface temperature (SST), salinity, and wind. The model introduces novel features such as spatial and temporal standard deviations of Chl-a concentration and a derived gradient feature. Correlation analysis indicated that these features enhance predictive capability. A multi-layer artificial neural network (ANN) was trained using a 66%/34% data split for training and testing, achieving 88.7% accuracy in binary classification (bloom/non-bloom) with an area under the ROC curve (AUC) of 90.1%. Overfitting was mitigated by monitoring training and validation loss, both of which consistently decreased over epochs, confirming robust model generalization. The use of standard deviation in SST and salinity highlights their influence on bloom dynamics, providing key insights into algal bloom drivers. The focus on freely available data enables stakeholders to better manage the environmental challenges posed by HABs.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106311"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905668","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

Machine learning-based prediction of belowground biomass from aboveground biomass and soil properties 基于机器学习的地下生物量从地上生物量和土壤性质预测

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106313

Yuquan Zhao , Lu Zhang , Shilong Lei , Lirong Liao , Chao Zhang

{"title":"Machine learning-based prediction of belowground biomass from aboveground biomass and soil properties","authors":"Yuquan Zhao , Lu Zhang , Shilong Lei , Lirong Liao , Chao Zhang","doi":"10.1016/j.envsoft.2024.106313","DOIUrl":"10.1016/j.envsoft.2024.106313","url":null,"abstract":"<div><div>Precise and accurate quantification of belowground biomass (BGB) is essential for understanding terrestrial carbon dynamics. Traditional methods for estimating BGB suffer from a number of disadvantages, including inability to resolve differences among plant species, high dependence on Diameter at Breast Height, and destructive sampling. To address these issues, we developed a novel machine learning framework to estimate grassland BGB by integrating vegetation and soil data from 294 plots on China's Loess Plateau. An ensemble model combining XGBoost regression, Gradient boosting regression, Ridge regression, and ElasticNet regression outperformed the individual models, achieving a training R<sup>2</sup> of 0.623 and a testing R<sup>2</sup> of 0.502, highlighting its superior ability to identify the complex dependencies of BGB. Integration of key features, including soil organic carbon, plant height, and aboveground biomass, significantly improved the predictive accuracy. Nonlinear BGB–environment interactions are commonly underrecognized in traditional models. The model presented herein advances our ability to assess underground carbon stocks and offers insights into the ecological strategies of grassland species under competitive light conditions. By revealing the multifaceted influences of soil and vegetation on BGB, our research refines the understanding of grassland carbon dynamics. This study marks a precedent for harnessing advanced machine learning in ecological modeling to facilitate more accurate predictions of global change.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106313"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935470","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

Synthetic random environmental time series generation with similarity control, preserving original signal’s statistical characteristics 采用相似度控制合成随机环境时间序列，保持原始信号的统计特征

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106283

Ofek Aloni , Gal Perelman , Barak Fishbain

引用次数: 0

Community-enabled life-cycle assessment Stormwater Infrastructure Costs (CLASIC) tool 社区支持的生命周期评估雨水基础设施成本（classic）工具

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106279

Mazdak Arabi , Tyler Dell , Mahshid Mohammad Zadeh , Christine A. Pomeroy , Jennifer M. Egan , Tyler Wible , Sybil Sharvelle

{"title":"Community-enabled life-cycle assessment Stormwater Infrastructure Costs (CLASIC) tool","authors":"Mazdak Arabi , Tyler Dell , Mahshid Mohammad Zadeh , Christine A. Pomeroy , Jennifer M. Egan , Tyler Wible , Sybil Sharvelle","doi":"10.1016/j.envsoft.2024.106279","DOIUrl":"10.1016/j.envsoft.2024.106279","url":null,"abstract":"<div><div>Urbanization, land use change, and climate change have profound effects on urban stormwater. This study develops the Community-enabled Life-cycle Analysis of Stormwater <span>Infrastructure</span> Costs (CLASIC) software to support decisions about stormwater control infrastructure over a range of alternative scenarios at the neighborhood to municipal scales. The tool quantifies hydrologic and stormwater quality performance, life-cycle costs, and triple-bottom-line social, economic, and environmental co-benefits of green, gray, and hybrid green-gray stormwater practices. CLASIC is deployed as a cloud-based web-tool, with a geographical information system (GIS) enabled interface, and built-in computing services to characterize terrain, soil, land use, and climatic conditions using publicly available datasets, and to parameterize and execute the modeling modules. Three community level case studies in the United States illustrate the utility of CLASIC for climate change assessments, green infrastructure implementation for community redevelopment, and assessment of the effects of changes in rainfall characteristics on the performance of stormwater practices.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106279"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793157","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

Predicting massive floating macroalgal blooms in a regional sea 预测区域海洋中大量漂浮的大型藻华

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106310

Fucang Zhou , Zhi Chen , Zaiyang Zhou , Bing Cao , Lili Xu , Dongyan Liu , Ruishan Chen , Karline Soetaert , Jianzhong Ge

{"title":"Predicting massive floating macroalgal blooms in a regional sea","authors":"Fucang Zhou , Zhi Chen , Zaiyang Zhou , Bing Cao , Lili Xu , Dongyan Liu , Ruishan Chen , Karline Soetaert , Jianzhong Ge","doi":"10.1016/j.envsoft.2024.106310","DOIUrl":"10.1016/j.envsoft.2024.106310","url":null,"abstract":"<div><div>Increasingly frequent and severe floating macroalgal blooms present significant challenges to coastal and ocean environments. Here a short-term forecast system of floating macroalgal blooms was developed to predict the physical-biogeochemical environment and macroalgal ecodynamic processes in a regional ocean. Predictions of macroalgal ecodynamic processes are influenced by oceanic conditions (hydrodynamics, temperature, and nutrients), as well as atmospheric conditions (wind). The system's effectiveness is demonstrated by successfully hindcasting the June 2021 green tide bloom event in the Yellow Sea and using real-time satellite data to make reliable and robust continuous short-term predictions for 2022 and 2023. The prediction accuracy of coverage reaches 87.5%, and the minimum transport error of the green tide center of mass is 6.09 nautical miles over an 7-day prediction duration. Supported by regional marine physics and biogeochemistry and macroalgal physiological characteristic datasets, this system may serve as a crucial cornerstone for similar floating macroalgal disaster prevention.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106310"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901913","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

Solving the Master Equation on river networks: A computer algebra approach 求解河网主方程：一种计算机代数方法

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106288

Samuele De Bartolo , Gaetano Napoli , Stefano Rizzello , Raffaele Vitolo

引用次数: 0

A simple method for the enhancement of river bathymetry in LiDAR DEM

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106354

Gabriele Farina , Marco Pilotti , Luca Milanesi , Giulia Valerio

引用次数: 0

SIMRA: An interactive web-based tool for single integrated microbial risk assessment and management within the national water security framework (NWSF) SIMRA：在国家水安全框架（NWSF）内进行单一综合微生物风险评估和管理的交互式网络工具

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106304

A. Murei, M.N.B. Momba

{"title":"SIMRA: An interactive web-based tool for single integrated microbial risk assessment and management within the national water security framework (NWSF)","authors":"A. Murei, M.N.B. Momba","doi":"10.1016/j.envsoft.2024.106304","DOIUrl":"10.1016/j.envsoft.2024.106304","url":null,"abstract":"<div><div>In this study, we present a single integrated microbial risk assessment (SIMRA) tool, which is a web-based application that offers a multi-tiered approach to microbial risk assessment of water resources, and accommodates three different user proficiency levels for microbial risk assessment based on resource availability. The main objective was to integrate the components of water safety plans and sanitation safety plans and develop a single web-based application platform that enables the assessment, management, and monitoring of possible risks associated with various types of water. Key parameters in the risk assessment process in the tool are concentration of indicator microorganisms and pathogens. The SIMRA web-based tool enables comprehensive water and sanitation analysis, risk assessment, geospatial mapping of water resources, and generates feedback on the level of safety of the water source. The web server is publicly accessible at <span><span>https://simraweb.web.app/</span><svg><path></path></svg></span>. Here, we outline the development of SIMRA and its main features and demonstrate its applicability.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106304"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An open framework for analysing future flood risk in urban areas 分析市区未来洪水风险的开放架构

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106302

Olivia Butters , Craig Robson , Fergus McClean , Vassilis Glenis , James Virgo , Alistair Ford , Christos Iliadis , Richard Dawson

{"title":"An open framework for analysing future flood risk in urban areas","authors":"Olivia Butters , Craig Robson , Fergus McClean , Vassilis Glenis , James Virgo , Alistair Ford , Christos Iliadis , Richard Dawson","doi":"10.1016/j.envsoft.2024.106302","DOIUrl":"10.1016/j.envsoft.2024.106302","url":null,"abstract":"<div><div>A combination of climate change and urban development are increasing flood risk in cities worldwide, however analysing both drivers of risk is especially complex as new buildings alter surface water flows changing flood events. This paper provides an overview of the approaches, algorithms, design, and capabilities of the OpenCLIM urban flooding workflow which attempts to address this, coupling building-scale models of urban development with high-resolution simulations of urban flooding. The workflow retrieves and processes national-scale datasets, automatically configuring data and models, thereby significantly reducing user effort in commissioning simulations of risk analysis. A demonstration for Newcastle-upon-Tyne (UK) reveals hotspots of changes in risk and exposure are altered by both urban development and changes in rainfall, with climate change the most significant driver. The workflow is made accessible and transparent via DAFNI (<span><span>www.dafni.ac.uk</span><svg><path></path></svg></span>), a national computing facility, providing a vital tool for routine and repeatable urban flood risk analysis.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106302"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modelling evapotranspiration in urban green stormwater infrastructures: Importance of sensitivity analysis and calibration strategies with a hydrological model 城市绿色雨水基础设施的蒸散模拟：水文模型敏感性分析和校准策略的重要性

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106319

Ahmeda Assann Ouédraogo , Emmanuel Berthier , Jérémie Sage , Marie-Christine Gromaire

{"title":"Modelling evapotranspiration in urban green stormwater infrastructures: Importance of sensitivity analysis and calibration strategies with a hydrological model","authors":"Ahmeda Assann Ouédraogo , Emmanuel Berthier , Jérémie Sage , Marie-Christine Gromaire","doi":"10.1016/j.envsoft.2025.106319","DOIUrl":"10.1016/j.envsoft.2025.106319","url":null,"abstract":"<div><div>Evapotranspiration (ET) is crucial for urban runoff management, the cooling efficiency of green stormwater infrastructure (GSI), and vegetation resilience. This research investigates the ability of a commonly used hydrological ET scheme, implemented in HYDRUS-1D, to accurately replicate ET fluxes within GSI, including green roofs (GRs) and rain gardens (RGs), in the Paris region, France. Application of the Sobol sensitivity analysis method indicates that, vegetation height and stomatal resistance are key elements in Penman-Monteith potential ET calculations, while substrate water retention parameters are essential for actual ET simulations. Soil cover fraction, substrate pressure head during the anaerobic phase, and interception parameter also influence ET. Calibration using extensive datasets (water content, ET, drainage) demonstrates improved model accuracy for GRs with thicker substrates compared to those with thinner substrates and for RG setups. Drainage calibration ensures long-term ET simulation accuracy, while calibration with water content or ET observations is recommended during prolonged dry periods.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106319"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975686","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