Environmental Modelling & Software最新文献_第5页

Innovative knowledge-based system for streamflow hindcasting: A comparative assessment of Gaussian Process-Integrated Neural Network with LSTM and GRU models 创新的基于知识的流后预测系统：基于LSTM和GRU模型的高斯过程集成神经网络的比较评估

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-13 DOI: 10.1016/j.envsoft.2025.106433

Arathy Nair G R , Adarsh S

{"title":"Innovative knowledge-based system for streamflow hindcasting: A comparative assessment of Gaussian Process-Integrated Neural Network with LSTM and GRU models","authors":"Arathy Nair G R , Adarsh S","doi":"10.1016/j.envsoft.2025.106433","DOIUrl":"10.1016/j.envsoft.2025.106433","url":null,"abstract":"<div><div>Lack of historical data is a major bottleneck for hydrologists to proceed with reliable climate change studies. This work proposes Gaussian Process-Integrated Neural Network (GAUSNET) technique for streamflow hindcasting by considering significant hydrological variables and Global climatic oscillations (GCO) identified by Variance Inflation Factor as system inputs. Dynamic Time Warping based Interpolation is utilized to align monthly GCOs with daily streamflows, followed by feature selection and auto-correlation using Gradient Boosting Machines. On applying for streamflow hindcasting of Greater Pamba, Kerala, India, GAUSNET consistently outperformed Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) across all of the input scenarios with an average Nash-Sutcliffe Efficiency (NSE) of 0.93. GAUSNET based hindcasting can overcome issues of data shortage, fill the data gaps and capture extreme events. Moreover, its ability for uncertainty quantification enhances the reliability and make it as robust tool for hydrological modeling, flood risk assessment, and sustainable water management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106433"},"PeriodicalIF":4.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678382","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 Markov Chain Monte Carlo approach for complex lava flow simulations driven by satellite-derived data 卫星数据驱动下复杂熔岩流模拟的马尔可夫链蒙特卡罗方法

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-13 DOI: 10.1016/j.envsoft.2025.106426

Francesco Zuccarello, Giuseppe Bilotta, Flavio Cannavò, Annalisa Cappello, Roberto Guardo, Gaetana Ganci

引用次数: 0

Models and the common good 典范和共同利益

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-12 DOI: 10.1016/j.envsoft.2025.106430

Andrea Saltelli

引用次数: 0

Uncertainty estimation for environmental multimodel predictions: The BLUECAT approach and software 环境多模式预测的不确定性估计：BLUECAT方法和软件

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-12 DOI: 10.1016/j.envsoft.2025.106419

Alberto Montanari , Demetris Koutsoyiannis

引用次数: 0

Analysis of SWAT+ model performance: A comparative study using different software and algorithms SWAT+模型性能分析：使用不同软件和算法的比较研究

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-12 DOI: 10.1016/j.envsoft.2025.106425

Samanta Tolentino Cecconello , Danielle Bressiani , Maria Cândida Moitinho Nunes , Luís Carlos Timm

{"title":"Analysis of SWAT+ model performance: A comparative study using different software and algorithms","authors":"Samanta Tolentino Cecconello , Danielle Bressiani , Maria Cândida Moitinho Nunes , Luís Carlos Timm","doi":"10.1016/j.envsoft.2025.106425","DOIUrl":"10.1016/j.envsoft.2025.106425","url":null,"abstract":"<div><div>The Soil and Water Assessment Tool plus (SWAT+) model is widely used to analyze water dynamics in hydrological processes. It improves upon the earlier SWAT version by incorporating decision tables that allow for the specification of different land use management activities and scenarios. However, accurate watershed representation requires proper calibration and validation. Among the available open-source tools for this purpose, SWAT + Toolbox and R-SWAT remain underexplored in scientific literature. This study aimed to compare and evaluate the performance of SWAT + using R-SWAT and SWAT + Toolbox by applying different calibration and validation algorithms in a rural watershed in southern Rio Grande do Sul, Brazil. The Sequential Uncertainty Fitting 2 (SUFI-2) and Dynamically Dimensioned Search (DDS) algorithms were employed to calibrate and validate both monthly and daily streamflow from 2015 to 2017, using 23 monthly and 825 daily observations. Model performance was assessed using the Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) metrics. The results showed that SWAT + Toolbox outperformed R-SWAT, achieving better accuracy in monthly calibration (NSE = 0.69; KGE = 0.78) and daily validation (NSE = 0.74; KGE = 0.78). These findings highlight the greater precision of SWAT + Toolbox in streamflow modeling, although both tools exhibited limitations in representing baseflow. Additionally, the uncertainty analysis underscored the need for more precise input data, particularly regarding soil characterization. Future research should explore the implementation of more advanced calibration algorithms available in R-SWAT and improvements in soil characterization to enhance hydrological simulation accuracy, reduce uncertainties, and improve model reliability. Moreover, a comparative analysis between SWAT+ and its predecessor is recommended.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106425"},"PeriodicalIF":4.8,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637427","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 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