Environmental Modelling & Software最新文献_第9页

A hybrid framework for regional climate seasonality study and trend analysis 区域气候季节性研究与趋势分析的混合框架

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-11 DOI: 10.1016/j.envsoft.2025.106429

Masooma Suleman, Peter A. Khaiter

引用次数: 0

Dust storm detection for ground-based stations with imbalanced machine learning 基于不平衡机器学习的地面站沙尘暴检测

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-08 DOI: 10.1016/j.envsoft.2025.106420

Shikang Du , Siyu Chen , Shanling Cheng , Jiaqi He , Dan Zhao , Xusheng Zhu , Lulu Lian , Xingxing Tu , Qinghong Zhao , Yue Zhang

{"title":"Dust storm detection for ground-based stations with imbalanced machine learning","authors":"Shikang Du , Siyu Chen , Shanling Cheng , Jiaqi He , Dan Zhao , Xusheng Zhu , Lulu Lian , Xingxing Tu , Qinghong Zhao , Yue Zhang","doi":"10.1016/j.envsoft.2025.106420","DOIUrl":"10.1016/j.envsoft.2025.106420","url":null,"abstract":"<div><div>Dust storms, common meteorological hazard in arid and semi-arid regions, have significant environmental and societal impacts. Rapid and accurate detecting dust storms is critical for early warning systems. Over the past few decades, dust storm detection primarily relied on satellite remote sensing techniques using multi-channel imagery, but these methods have limitations in temporal resolution. With the recent expansion of China's observation network, the dense distribution of ground-based sensors offers a promising data source for real-time dust storm detection. This study proposes a machine learning approach to detect dust storms using ground-based sensor networks. By combining undersampling strategies and ensemble algorithms, this method improves model's performance in detecting dust storms. Compared with the state-of-the-art models, this approach improves the Recall rates for different dust storm levels by 24.32% and the G-Mean by 18.58%, achieving superior dust storm detection performance. This approach can offer the near-real-time, hourly updated dust storm detection products.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106420"},"PeriodicalIF":4.8,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637426","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

PyTorchFire: A GPU-accelerated wildfire simulator with Differentiable Cellular Automata PyTorchFire：一个gpu加速的野火模拟器，带有可微分元胞自动机

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-07 DOI: 10.1016/j.envsoft.2025.106401

Zeyu Xia , Sibo Cheng

{"title":"PyTorchFire: A GPU-accelerated wildfire simulator with Differentiable Cellular Automata","authors":"Zeyu Xia , Sibo Cheng","doi":"10.1016/j.envsoft.2025.106401","DOIUrl":"10.1016/j.envsoft.2025.106401","url":null,"abstract":"<div><div>Accurate and rapid prediction of wildfire trends is crucial for effective management and mitigation. However, the stochastic nature of fire propagation poses significant challenges in developing reliable simulators. In this paper, we introduce <span>PyTorchFire</span>, an open-access, <span>PyTorch</span>-based software that leverages GPU acceleration. With our redesigned differentiable wildfire Cellular Automata (CA) model, we achieve millisecond-level computational efficiency, significantly outperforming traditional CPU-based wildfire simulators on real-world-scale fires at high resolution. Real-time parameter calibration is made possible through gradient descent on our model, aligning simulations closely with observed wildfire behavior both temporally and spatially, thereby enhancing the realism of the simulations. Our <span>PyTorchFire</span> simulator, combined with real-world environmental data, demonstrates superior generalizability compared to supervised learning surrogate models. Its ability to predict and calibrate wildfire behavior in real-time ensures accuracy, stability, and efficiency. <span>PyTorchFire</span> has the potential to revolutionize wildfire simulation, serving as a powerful tool for wildfire prediction and management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106401"},"PeriodicalIF":4.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592244","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

Efficient and fine-grained viewshed analysis in a three-dimensional urban complex environment 三维城市复杂环境的高效细粒度视角分析

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-06 DOI: 10.1016/j.envsoft.2025.106359

Yifan Zhang, Mengyu Ma, Jun Li, Anran Yang, Qingren Jia, Zebang Liu

{"title":"Efficient and fine-grained viewshed analysis in a three-dimensional urban complex environment","authors":"Yifan Zhang, Mengyu Ma, Jun Li, Anran Yang, Qingren Jia, Zebang Liu","doi":"10.1016/j.envsoft.2025.106359","DOIUrl":"10.1016/j.envsoft.2025.106359","url":null,"abstract":"<div><div>Performing efficient and fine-grained viewshed analysis in 3D complex urban models, particularly when handling large-scale datasets, presents a significant challenge in Geographic Information Systems (GIS). Existing methods are primarily designed for 2.5D raster models and struggle to effectively manage large-scale data. Furthermore, the commonly utilized approaches for 3D models need large display memory and lack statistical analyses. To address these challenges, this paper adopted a results-oriented approach that diverged from the traditional data-driven paradigm by reformulating the conventional viewshed computation problem as a spatial query problem. Building on this premise, we proposed the Q-View method for oblique photogrammetry data, which involved creating an indexing model for large-scale datasets and enabled parallel querying between the line-of-sight (LOS) and the model. The Q-View method enables efficient and spatially exhaustive analysis, effectively overcoming the complexities associated with traditional viewshed computations. Experimental results showed that our approach achieved a query rate of up to 4 million visibility queries per second on a dataset with 17.6 million triangular meshes. Compared to the latest methods, it offered a 72.45% improvement in operational efficiency and superior accuracy relative to the GPU-rated method. These findings indicated that the proposed method substantially improved both the accuracy and efficiency of viewshed analysis in complex urban environments, providing decision support for urban planning and environmental monitoring.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106359"},"PeriodicalIF":4.8,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577051","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

Advancing open and reproducible water data science by integrating data analytics with an online data repository 通过将数据分析与在线数据存储库集成，推进开放和可复制的水数据科学

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-06 DOI: 10.1016/j.envsoft.2025.106422

Jeffery S. Horsburgh , Scott Black , Anthony Castronova , Pabitra K. Dash

{"title":"Advancing open and reproducible water data science by integrating data analytics with an online data repository","authors":"Jeffery S. Horsburgh , Scott Black , Anthony Castronova , Pabitra K. Dash","doi":"10.1016/j.envsoft.2025.106422","DOIUrl":"10.1016/j.envsoft.2025.106422","url":null,"abstract":"<div><div>Scientific and management challenges in the water domain require synthesis of diverse data. Many analysis tasks are difficult because datasets are large and complex, standard formats are not always agreed upon or mapped to efficient data structures, scientists may lack training for tackling large and complex datasets, and it can be difficult to share and reproduce data science workflows. Overcoming barriers to accessing, organizing, and preparing datasets for analyses can transform how water scientists work. Building on the HydroShare repository's cyberinfrastructure, we created a Python package that automates data retrieval, organization, and curation for analysis, reducing time spent in choosing appropriate data structures and writing data ingestion code. It manages metadata and automates data loading into performant structures consistent with Python's visualization, analysis, and data science capabilities and can be used to build and share more reproducible scientific workflows in HydroShare following FAIR (Findable, Accessible, Interoperable, and Reusable) principles.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106422"},"PeriodicalIF":4.8,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620282","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

Can large language models effectively reason about adverse weather conditions? 大型语言模型能有效地推断出恶劣的天气条件吗？

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-05 DOI: 10.1016/j.envsoft.2025.106421

Nima Zafarmomen , Vidya Samadi

引用次数: 0

An efficient modern convolution-based dynamic spatiotemporal deep learning architecture for ozone prediction 一种高效的基于现代卷积的臭氧预测动态时空深度学习架构

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-05 DOI: 10.1016/j.envsoft.2025.106424

Ao Li, Ji Li, Zhizhang Shen

{"title":"An efficient modern convolution-based dynamic spatiotemporal deep learning architecture for ozone prediction","authors":"Ao Li, Ji Li, Zhizhang Shen","doi":"10.1016/j.envsoft.2025.106424","DOIUrl":"10.1016/j.envsoft.2025.106424","url":null,"abstract":"<div><div>Ozone pollution threatens ecosystems and human health, necessitating accurate forecasting for better management and policy implementation. To address this, we developed O3ConvNet, a convolution-based dynamic spatiotemporal deep learning model. It incorporates ModernTCN, a multivariate time series feature module, and a spatial message passing module using a dynamic adjacency matrix with geographic and DTW-based distances. O3ConvNet balances performance and efficiency across datasets with varying station densities and data qualities. In Los Angeles, the mean absolute error ranges from 6.984 μg/m<sup>3</sup> to 15.990 μg/m<sup>3</sup> for 1-h to 24-h predictions, with <em>R</em><sup>2</sup> values exceeding 0.937. Computational time is reduced by up to 82% compared to the best baseline model. In Wuxi, China, it improves prediction accuracy by 18% and efficiency by 81%. ModernTCN module identifies critical factors for ozone formation, while the dynamic adjacency matrix helps extract spatial dependencies effectively. Overall, this study introduces a robust and generalizable model for regional ozone predictions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106424"},"PeriodicalIF":4.8,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600157","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

Dissolved oxygen prediction in the Dianchi River basin with explainable artificial intelligence based on physical prior knowledge 基于物理先验知识的可解释人工智能滇池流域溶解氧预测

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-05 DOI: 10.1016/j.envsoft.2025.106412

Junhao Wu , Xi Chen , Jinghan Dong , Nen Tan , Xiaoping Liu , Antonis Chatzipavlis , Philip LH. Yu , Adonis Velegrakis , Yining Wang , Yonggui Huang , Heqin Cheng , Diankai Wang

{"title":"Dissolved oxygen prediction in the Dianchi River basin with explainable artificial intelligence based on physical prior knowledge","authors":"Junhao Wu , Xi Chen , Jinghan Dong , Nen Tan , Xiaoping Liu , Antonis Chatzipavlis , Philip LH. Yu , Adonis Velegrakis , Yining Wang , Yonggui Huang , Heqin Cheng , Diankai Wang","doi":"10.1016/j.envsoft.2025.106412","DOIUrl":"10.1016/j.envsoft.2025.106412","url":null,"abstract":"<div><div>Dissolved oxygen (DO) is a critical parameter for monitoring water quality. However, most existing deep learning models have overlooked the physical relationship between DO and other parameters during simulation, leading to simulated values that deviate from the actual physical laws. Moreover, the inherent opacity of deep learning models restricts their applicability. Here, we propose the prior knowledge-constrained bidirectional long short-term memory network (PKBiLSTM) model to simulate DO levels in the Dianchi Lake basin. Our results show that the PKBiLSTM model achieves an average Kling-Gupta efficiency coefficient (KGE) of 0.926, which represents a 3.35% and 2.38% increase compared to the gated recurrent unit (GRU) and categorical boosting (CatBoost) models, respectively. The experiments reveal that pH has the greatest effect on DO concentration within the range of 6.5–10. Furthermore, the primary factors affecting DO exhibit seasonal differences. The findings underscore the potential of our method to enhance the scientific management of watersheds.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106412"},"PeriodicalIF":4.8,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577046","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

Simulation decomposition analysis of the Iowa food-water-energy system 爱荷华州食物-水-能源系统的模拟分解分析

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-05 DOI: 10.1016/j.envsoft.2025.106415

Taeho Jeong , Mariia Kozlova , Leifur Thor Leifsson , Julian Scott Yeomans

{"title":"Simulation decomposition analysis of the Iowa food-water-energy system","authors":"Taeho Jeong , Mariia Kozlova , Leifur Thor Leifsson , Julian Scott Yeomans","doi":"10.1016/j.envsoft.2025.106415","DOIUrl":"10.1016/j.envsoft.2025.106415","url":null,"abstract":"<div><div>This study applies global sensitivity analysis (GSA) to the Iowa Food-Water-Energy system, focusing on nitrogen export into the Mississippi River. A binning method combined with <em>simulation decomposition</em> (SimDec) quantifies and visualizes the influence of crucial aggregate input variables — manure nitrogen (MN), commercial nitrogen (CN), grain nitrogen (GN), and fixation nitrogen (FN) — on nitrogen surplus (NS) at the county level. Unlike traditional Sobol’ indices, the binning method captures dependent variables. In addition, the SimDec procedure provides a detailed visual representation of how these dependencies and interactions drive the nitrogen variability. MN is identified as the most influential factor, followed by CN, with FN and GN having less impact. The study also performs GSA on the low-level input variables, enhancing the overall interpretability of the sensitivity analysis. This approach offers actionable insights for improving nitrogen management practices and contributes to GSA literature by showcasing the analysis of aggregate variables.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106415"},"PeriodicalIF":4.8,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577047","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

FloodGame: An interactive 3D serious game on flood mitigation for disaster awareness and education “防洪游戏”：一款以防洪为主题的互动3D严肃游戏，旨在提高防灾意识和教育

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-04 DOI: 10.1016/j.envsoft.2025.106418

Bekir Z. Demiray , Yusuf Sermet , Enes Yildirim , Ibrahim Demir

引用次数: 0