Environmental Modelling & Software最新文献_第10页

Exploring a hybrid ensemble–variational data assimilation technique (4DEnVar) with a simple ecosystem carbon model 基于简单生态系统碳模型的组合-变分数据同化技术（4DEnVar）研究

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-10 DOI: 10.1016/j.envsoft.2025.106361

Natalie Douglas, Tristan Quaife, Ross Bannister

引用次数: 0

Hybrid cellular automata-based air pollution model for traffic scenario microsimulations 基于混合元胞自动机的交通场景微模拟空气污染模型

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-07 DOI: 10.1016/j.envsoft.2025.106356

Tabea S. Sonnenschein , Zhendong Yuan , Jibran Khan , Jules Kerckhoffs , Roel C.H. Vermeulen , Simon Scheider

{"title":"Hybrid cellular automata-based air pollution model for traffic scenario microsimulations","authors":"Tabea S. Sonnenschein , Zhendong Yuan , Jibran Khan , Jules Kerckhoffs , Roel C.H. Vermeulen , Simon Scheider","doi":"10.1016/j.envsoft.2025.106356","DOIUrl":"10.1016/j.envsoft.2025.106356","url":null,"abstract":"<div><div>Scenario microsimulations like agent-based models can account for feedbacks and spatio-temporal and social heterogeneity when projecting future intervention impacts. Addressing air pollution exposure requires traffic scenario models (<em>i.e</em>. of car-free zones). Traditional air pollution models do not meet all requirements for traffic scenario microsimulation: isolating traffic emission, integrating relevant dispersion moderators, while computationally efficient, interoperable and valid. We propose a hybrid model of land use regression-based baseline concentrations and on-road emissions in conjunction with cellular automata-based off-road dispersion. The model efficiently assesses air pollution, while accounting for meteorological and morphological dispersion processes. We calibrate using genetic algorithms and externally validate the model based on mobile measurements and fixed-site routine monitoring data of NO2 concentrations across Amsterdam. Our model achieves an external validation R2 of 0.60 and 0.48 s computation time in a 50 m × 50 m raster. Further, we successfully projected the NO2 reduction of the first Covid-19 lockdown traffic scenario (R2 0.57).</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106356"},"PeriodicalIF":4.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377303","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

PSLSA v2.0: An automatic Python package integrating machine learning models for regional landslide susceptibility assessment PSLSA v2.0：一个集成区域滑坡易感性评估机器学习模型的自动Python包

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-07 DOI: 10.1016/j.envsoft.2025.106367

Zizheng Guo , Haojie Wang , Jun He , Da Huang , Yixiang Song , Tengfei Wang , Yuanbo Liu , Joaquin V. Ferrer

{"title":"PSLSA v2.0: An automatic Python package integrating machine learning models for regional landslide susceptibility assessment","authors":"Zizheng Guo , Haojie Wang , Jun He , Da Huang , Yixiang Song , Tengfei Wang , Yuanbo Liu , Joaquin V. Ferrer","doi":"10.1016/j.envsoft.2025.106367","DOIUrl":"10.1016/j.envsoft.2025.106367","url":null,"abstract":"<div><div>Accurate landslide susceptibility assessments (LSA) are crucial for civil protection and land use planning. This study introduces PSLSA v2.0 as an open-source Python package that can conduct LSA automatically. It integrates six sophisticated machine learning algorithms (C5.0, SVM, LR, RF, MLP, XGBoost), and allows arbitrary combinations of influencing factors to generate landslide susceptibility index (LSI). We demonstrate how factor contribution and hyperparameter optimization as additional outputs can enhance the model interpretability. We apply PSLSA to a case study focused from Linzhi City in the Tibetan Plateau of China, that has undergone significant engineering modifications on its slopes. The results reveal that slope and aspect are the dominant factors in determining landslide susceptibility. All the six algorithms have an accuracy of over 80%. Although the distribution patterns of LSI vary, the C5.0 model is set apart with the best performance. PSLSA provides a powerful tool for stakeholders especially the non-geohazard professionals.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106367"},"PeriodicalIF":4.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379430","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

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-06 DOI: 10.1016/j.envsoft.2025.106357

Fransiskus Serfian Jogo , Hanum Khairana Fatmah , Aufaclav Zatu Kusuma Frisky

引用次数: 0

Development of an advanced numerical simulation program considering debris flow and driftwood behavior 考虑泥石流和浮木行为的先进数值模拟程序的开发

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-06 DOI: 10.1016/j.envsoft.2025.106366

T. Kang , S. Lee , H. An , M. Kim , I. Kimura

引用次数: 0

Spatiotemporal PM2.5 forecasting via dynamic geographical Graph Neural Network 基于动态地理图神经网络的PM2.5时空预测

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-06 DOI: 10.1016/j.envsoft.2025.106351

Qin Zhao , Jiajun Liu , Xinwen Yang , Hongda Qi , Jie Lian

{"title":"Spatiotemporal PM2.5 forecasting via dynamic geographical Graph Neural Network","authors":"Qin Zhao , Jiajun Liu , Xinwen Yang , Hongda Qi , Jie Lian","doi":"10.1016/j.envsoft.2025.106351","DOIUrl":"10.1016/j.envsoft.2025.106351","url":null,"abstract":"<div><div>With the growing interest in data-driven methods, Graph Neural Networks (GNNs) have demonstrated strong performance in <span><math><msub><mrow><mi>PM</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></math></span> forecasting as a deep learning architecture. However, GNN-based methods typically construct the graph based solely on the distance between stations, and few methods introduce geographical factors that significantly affect the spatial dispersion of <span><math><msub><mrow><mi>PM</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></math></span>, leading to performance bottlenecks. Additionally, these methods often fail to process the dynamic wind–field data comprehensively, resulting in inaccurate <span><math><msub><mrow><mi>PM</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></math></span> dispersion graph construction. These shortcomings greatly limit the interpretability of GNN models in forecasting air pollution. To address these issues, we propose a deep learning method that combines Graph Convolution Network (GCN) with Long Short-Term Memory (LSTM), leveraging geographical information within a dynamic graph. The model captures spatial dependencies between <span><math><msub><mrow><mi>PM</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></math></span> monitoring stations using a dynamic directional graph derived from the wind–field data and a static graph to represent inherent geographical relationships. The combination of GCN and LSTM enables the extraction of both spatial and temporal correlations. The results of experiments suggest that our proposed model, which offers great interpretability, outperforms state-of-the-art methods, especially in 24, 30, and 36 hours forecasts.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106351"},"PeriodicalIF":4.8,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143336705","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

ExMAD (Expert-based Multitemporal AI Detector): An open-source methodological framework for remote and field landslide inventory ExMAD（基于专家的多时态人工智能检测器）：用于远程和现场滑坡调查的开源方法框架

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-04 DOI: 10.1016/j.envsoft.2025.106363

Michele Licata, Stefano Faga , Giandomenico Fubelli

{"title":"ExMAD (Expert-based Multitemporal AI Detector): An open-source methodological framework for remote and field landslide inventory","authors":"Michele Licata, Stefano Faga , Giandomenico Fubelli","doi":"10.1016/j.envsoft.2025.106363","DOIUrl":"10.1016/j.envsoft.2025.106363","url":null,"abstract":"<div><div>Landslides threaten lives and infrastructure, making accurate inventories crucial for risk management. This study combines expert methods with machine learning to automate and validate landslide detection and timing using Sentinel-2 satellite imagery. We developed ExMAD (Expert-based Multi-temporal AI Detector), an open-source methodological framework (<span><span>https://github.com/NewGeoProjects/ExMAD</span><svg><path></path></svg></span>) to integrate artificial intelligence with human expertise to detect occurrence timing of a targeted landslide. A U-Net neural network was chosen to effectively test ExMAD in landslide detection over Sentinel-2 worldwide multitemporal satellite imagery sequences, and the model was tested through five evaluations. ExMAD was able to effectively extract timing of target landslides on Sentinel-2 images and was able to correctly detect the presence/absence of landslide, proving the suitability of AI systems in landslide temporal mapping task.</div><div>This research proves the potential of hybrid AI-human approaches for landslide risk assessment, integrate human expertise with machine learning offers promising advancements for remote and field mapping of landslide. Furthermore, the ExMAD methodology adheres to the European Union's Artificial Intelligence Act, stressing human oversight in high-risk AI applications to enhance trust, control, and efficiency in landslide inventory creation and risk management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106363"},"PeriodicalIF":4.8,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350438","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

SWMManywhere: A workflow for generation and sensitivity analysis of synthetic urban drainage models, anywhere SWMManywhere：生成和敏感性分析合成城市排水模型的工作流程，在任何地方

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-02 DOI: 10.1016/j.envsoft.2025.106358

Barnaby Dobson , Tijana Jovanovic , Diego Alonso-Álvarez , Taher Chegini

{"title":"SWMManywhere: A workflow for generation and sensitivity analysis of synthetic urban drainage models, anywhere","authors":"Barnaby Dobson , Tijana Jovanovic , Diego Alonso-Álvarez , Taher Chegini","doi":"10.1016/j.envsoft.2025.106358","DOIUrl":"10.1016/j.envsoft.2025.106358","url":null,"abstract":"<div><div>Improvements in public geospatial datasets provide opportunities for deriving urban drainage networks and simulation models of these networks (UDMs). We present SWMManywhere, which leverages such datasets for generating synthetic UDMs and creating a Storm Water Management Model for any urban area globally. SWMManywhere's modular and parameterised approach enables customisation to explore hydraulicly feasible network configurations. Key novelties of our workflow are in network topology derivation that accounts for combined effects of impervious area and pipe slope. We assess SWMManywhere by comparing pluvial flooding, drainage network outflows, and design with known networks. The results demonstrate high quality simulations are achievable with a synthetic approach even for large networks. Our sensitivity analysis shows that manholes locations, outfalls, and underlying street network are the most sensitive parameters. We find widespread sensitivity across all parameters without clearly defined values that they should take, thus, recommending an uncertainty driven approach to synthetic drainage network modelling.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106358"},"PeriodicalIF":4.8,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379431","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

Cascading effect modelling of integrating geographic factors in interdependent systems 相互依存系统中整合地理因素的级联效应建模

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.2024.106316

Yong Ge , Mo Zhang , Rongtian Zhao , Die Zhang , Zhiyi Zhang , Daoping Wang , Qiuming Cheng , Yuxue Cui , Jian Liu

{"title":"Cascading effect modelling of integrating geographic factors in interdependent systems","authors":"Yong Ge , Mo Zhang , Rongtian Zhao , Die Zhang , Zhiyi Zhang , Daoping Wang , Qiuming Cheng , Yuxue Cui , Jian Liu","doi":"10.1016/j.envsoft.2024.106316","DOIUrl":"10.1016/j.envsoft.2024.106316","url":null,"abstract":"<div><div>Cascading effects from global disruptions such as natural disasters and pandemics have attracted significant research attention. Current approaches face challenges in adequately integrating geographic and systemic factors, limiting their ability to simulate the intricate dynamics of interdependent systems. Here, we proposed a novel Interdependency Network-based Geographic Cascade (INGC) model, coupling geographic factors to capture cascading shocks across global interdependent networks. By integrating macro-level interdependencies and typical dynamic network modelling approaches, the INGC enables more accurate simulations of hazard damage and shock propagation, highlighting critical nodes and pathways essential for informed policy-making. Through the global lockdown case analysis, the INGC model demonstrated its advantages in identifying critical sectors and regions by revealing heterogenous cascading patterns and their details robustly. This approach offers a scalable framework for future research and policy, ensuring greater resilience in the face of complex global extreme events.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106316"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935471","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 process-based framework for validating forest landscape modeling outcomes 用于验证森林景观建模结果的基于过程的框架

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.2025.106327

Mia M. Wu , Yu Liang , Hong S. He , Jian Yang , Bo Liu , Tianxiao Ma

{"title":"A process-based framework for validating forest landscape modeling outcomes","authors":"Mia M. Wu , Yu Liang , Hong S. He , Jian Yang , Bo Liu , Tianxiao Ma","doi":"10.1016/j.envsoft.2025.106327","DOIUrl":"10.1016/j.envsoft.2025.106327","url":null,"abstract":"<div><div>Forest landscape models (FLMs) simulate forest dynamics by integrating stand- and landscape-scale processes. Thus, evaluating FLMs simulations necessitates including both processes. Thus far, stand-scale processes were evaluated in some FLMs, whereas landscape-scale processes were rarely evaluated. This study presents a framework that evaluates both stand- and landscape-scale processes. For the stand-scale processes, we proposed using stand density management diagrams to evaluate the simulated stand development trajectories that encapsulate the interplay of tree growth, competition, and mortality. For the landscape-scale processes, we evaluated seed dispersal, the basic spatial process driving forest landscape dynamics and not evaluated previously, through comparing simulated tree species colonization pattern against tree age distribution data from inventory data. We demonstrated the applicability of the framework to a 300-year historical forest landscape reconstructed using LANDIS. Given the common features, the framework is applicable to other FLMs or terrestrial ecosystem models operating at large scales.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106327"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990596","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