Environmental Modelling & Software最新文献_第6页

Spatiotemporal variations of the precipitation in the Yellow River Basin considering climate and instrumental disturbance 黄河流域降水的时空变化（考虑气候和仪器干扰

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-09-10 DOI: 10.1016/j.envsoft.2024.106204

Wenzhuo Wang , Ningpeng Dong , Jinjun You , Zengchuan Dong , Li Ren , Lianqing Xue

{"title":"Spatiotemporal variations of the precipitation in the Yellow River Basin considering climate and instrumental disturbance","authors":"Wenzhuo Wang , Ningpeng Dong , Jinjun You , Zengchuan Dong , Li Ren , Lianqing Xue","doi":"10.1016/j.envsoft.2024.106204","DOIUrl":"10.1016/j.envsoft.2024.106204","url":null,"abstract":"<div><div>Climate change and instrumental disturbance make accurate identification of hydrometeorological period challenging. This study presents the hierarchical discrete-continuous wavelet decomposition (HDCWD) model to identify period with considering climate and instrumental disturbance. The method provides a three-layer identification framework of detrending, denoising and mining by combining discrete wavelet transform and continuous wavelet transform. The dominating periods and their spatiotemporal features of precipitation in the Yellow River Basin are identified by HDCWD. Results show the following: (1) Precipitation in the Yellow River Basin has the dominating periods of 2–4 years and 7–9 years (1956–1984), and period of 2 years from (1998–2002). (2) The periods of catchments in higher latitude exhibit longer and those in the lower east exhibit shorter. The results illustrate that although the precipitation in the Yellow River Basin differs in space and time, there is a certain evolution law. The results can provide information for water resources management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106204"},"PeriodicalIF":4.8,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312824","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 integrated, automated and modular approach for real-time weather monitoring of surface meteorological variables and short-range forecasting using machine learning 利用机器学习对地表气象变量和短程预报进行实时天气监测的综合、自动化和模块化方法

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-09-10 DOI: 10.1016/j.envsoft.2024.106203

R. Tsela, S. Maladaki, S. Kolios

{"title":"An integrated, automated and modular approach for real-time weather monitoring of surface meteorological variables and short-range forecasting using machine learning","authors":"R. Tsela, S. Maladaki, S. Kolios","doi":"10.1016/j.envsoft.2024.106203","DOIUrl":"10.1016/j.envsoft.2024.106203","url":null,"abstract":"<div><p>Weather monitoring and forecasting plays a vital role in a great variety of human activities such as agriculture, transportation, and extreme weather phenomena. This study presents the first outcomes of the development of a fully automated system regarding the real-time recording of basic meteorological parameters and their short-range forecasting (nowcasting). The system itself is divided into five core components: a hardware system for monitoring atmospheric conditions (Commercial Off-The-Shelf structures), a system for storing and managing data, a module for distributing data to support applications, a machine learning algorithm for nowcasting, and a user-friendly interface, all made by modern tools and methods, described analytically. Finally, the nowcasting procedure along with the relative accuracy results, is presented. The nowcasting procedure is based on a Long Short-Term Memory (LSTM) model scheme which is parametrized in such a way that reliable forecasts, up to 2 h ahead of time, can be provided.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106203"},"PeriodicalIF":4.8,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239906","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

The Fogees system for forecasting particulate matter concentrations in urban areas 预报城市地区颗粒物浓度的 Fogees 系统

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-09-08 DOI: 10.1016/j.envsoft.2024.106205

Krzysztof Brzozowski, Łukasz Drąg, Lucyna Brzozowska

{"title":"The Fogees system for forecasting particulate matter concentrations in urban areas","authors":"Krzysztof Brzozowski, Łukasz Drąg, Lucyna Brzozowska","doi":"10.1016/j.envsoft.2024.106205","DOIUrl":"10.1016/j.envsoft.2024.106205","url":null,"abstract":"<div><p>Air quality forecasting requires appropriate models and data sources. The Fogees system presented in this paper enables mapping, evaluation and forecasting of the level of PM<sub>x</sub> pollution in the air, for urban and suburban areas. Input data were downloaded from the Meteoblue service, the GIS database and – in the case of integration with existing measurement systems – also from local air quality monitoring stations. The system uses a diagnostic model to determine the velocity field and a Lagrange model to describe pollution dispersion. The system has an autocalibration model and uses proprietary algorithms to estimate emissions from domestic heating, transport and background pollution levels. Modular design and parallel computation facilitate simultaneous calculation of forecasts for existing emission conditions and calculations for alternative emission conditions. The system permits forecasting for the next hour and for several consecutive hours. The validation results confirm that the system reliable forecasting of PM concentrations.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106205"},"PeriodicalIF":4.8,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815224002664/pdfft?md5=dee05a8bbe60a87643a3d588dc154ed1&pid=1-s2.0-S1364815224002664-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239905","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

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-09-02 DOI: 10.1016/j.envsoft.2024.106192

Fransiskus Serfian Jogo

引用次数: 0

stagg:: A data pre-processing R package for climate impacts analysis stagg:：用于气候影响分析的数据预处理 R 软件包

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-09-02 DOI: 10.1016/j.envsoft.2024.106202

Tyler Liddell , Anna S. Boser , Sara Orofino , Tracey Mangin , Tamma Carleton

{"title":"stagg:: A data pre-processing R package for climate impacts analysis","authors":"Tyler Liddell , Anna S. Boser , Sara Orofino , Tracey Mangin , Tamma Carleton","doi":"10.1016/j.envsoft.2024.106202","DOIUrl":"10.1016/j.envsoft.2024.106202","url":null,"abstract":"<div><p>The increasing availability of high-resolution climate data has greatly expanded the study of how the climate impacts humans and society. However, the processing of these multi-dimensional datasets poses significant challenges for researchers in this growing field, most of whom are social scientists. This paper introduces stagg, or “space-time aggregator”, a new R package that streamlines three critical components of climate data processing for impacts analysis: nonlinear transformation, spatial and temporal aggregation, and spatial weighting by social or economic variables. The package consolidates the data processing pipeline into a few lines of code, lowering barriers to entry for researchers and facilitating a larger and more diverse research community. The paper provides an overview of stagg's functions, followed by an applied example demonstrating the package's utility in climate impacts research. stagg has the potential to be a valuable tool in generating evidence-based estimates of the likely impacts of future climate change.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106202"},"PeriodicalIF":4.8,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815224002639/pdfft?md5=4dad2ebfab8518075310ab0209474b6b&pid=1-s2.0-S1364815224002639-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169169","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

PyMTRD: A Python package for calculating the metrics of temporal rainfall distribution PyMTRD：用于计算时间降雨分布指标的 Python 软件包

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-09-01 DOI: 10.1016/j.envsoft.2024.106201

Zhengxu Guo , Yang Wang , Caiqin Liu , Wanhong Yang , Junzhi Liu

引用次数: 0

Analysis of the spatial heterogeneity of glacier melting in Tibet Autonomous Region and its influential factors using the K-means and XGBoost-SHAP algorithms 利用 K-means 和 XGBoost-SHAP 算法分析西藏自治区冰川融化的空间异质性及其影响因素

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-08-31 DOI: 10.1016/j.envsoft.2024.106194

Tingting Xu , Aohua Tian , Jay Gao , Haoze Yan , Chang Liu

{"title":"Analysis of the spatial heterogeneity of glacier melting in Tibet Autonomous Region and its influential factors using the K-means and XGBoost-SHAP algorithms","authors":"Tingting Xu , Aohua Tian , Jay Gao , Haoze Yan , Chang Liu","doi":"10.1016/j.envsoft.2024.106194","DOIUrl":"10.1016/j.envsoft.2024.106194","url":null,"abstract":"<div><p>This study employed machine learning to comprehensively analyze glacier melting in Tibet Autonomous Region (TAR) and its vital influencing factors. Existing machine learning research often lacks detailed explanations, leading to generalized predictions without considering essential driving factors necessary for yielding an insightful understanding of glacier melting dynamics. To overcome these limitations and fulfill multi-level analysis requirements for comprehending glacier melting, this study identifies factors contributing to glacier melting heterogeneity and assesses distinct melting causes in three spatial melted glacier clusters. We utilized K-means unsupervised classification to cluster Tibet melted glaciers into three categories based on temperature, sunshine hours, evapotranspiration, precipitation, normalized vegetation index, and slope. XGBoost algorithm explores the nonlinear relationships of glacier melting with these features and Shapley values were used for model transparency, quantifying feature's influence on the melting process. Investigating geographical heterogeneity among clusters enhanced our understanding of the observed changes. High fitting accuracy (>0.98) enhanced the result reliability, as well. The results show that Tibetan glaciers melt significantly from 2010 to 2020, and the cluster analysis reveals its unique melting characteristics. Melting glaciers in the same cluster are not only similar in characteristics, but also in spatial and geographical distribution, with two of the clusters concentrating in the eastern part of TAR, and the third cluster scattered in the western part of the country. the XGBoost-SHAP analysis efficiently quantifies the contribution of each cluster feature to the glacier melting, revealing the different roles of different clustered features.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"182 ","pages":"Article 106194"},"PeriodicalIF":4.8,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144027","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

XR-based interactive visualization platform for real-time exploring dynamic earth science data 基于 XR 的交互式可视化平台，用于实时探索动态地球科学数据

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-08-29 DOI: 10.1016/j.envsoft.2024.106193

Xuelei Zhang , Hu Yang , Chunhua Liu , Qingqing Tong , Aijun Xiu , Lingsheng Kong , Mo Dan , Chao Gao , Meng Gao , Huizheng Che , Xin Wang , Guangjian Wu

{"title":"XR-based interactive visualization platform for real-time exploring dynamic earth science data","authors":"Xuelei Zhang , Hu Yang , Chunhua Liu , Qingqing Tong , Aijun Xiu , Lingsheng Kong , Mo Dan , Chao Gao , Meng Gao , Huizheng Che , Xin Wang , Guangjian Wu","doi":"10.1016/j.envsoft.2024.106193","DOIUrl":"10.1016/j.envsoft.2024.106193","url":null,"abstract":"<div><p>The transition from 2D planar displays to immersive holographic 3D environments has brought advancements in visualization technology. However, there remains a lack of effective interactive visualization tools for complex multi-dimensional structured or unstructured datasets in immersive space. To address this gap, we have developed MetIVA, a state-of-the-art multiscale interactive data visualization platform that leverages the Extended Reality (XR) and cloud rendering technology for immersive data exploration. In this paper, we firstly outline the historical development of scientific visualization and the recent shift towards 3D and higher-dimensional visualization, and then basically introduce the conceptual framework and platform structure of MetIVA, and finally present the evaluation results from recruited potential users. The results confirm that MetIVA is a powerful tool to accelerate data exploration and decision-making processes. Its interactive and intuitive features, along with ongoing optimization efforts, make it a valuable tool for researchers and practitioners in the field of Earth science.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106193"},"PeriodicalIF":4.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142158419","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

Bayesian Optimization for Anything (BOA): An open-source framework for accessible, user-friendly Bayesian optimization 贝叶斯优化（BOA）：一个可访问的、用户友好的贝叶斯优化开源框架

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-08-24 DOI: 10.1016/j.envsoft.2024.106191

Madeline E. Scyphers , Justine E.C. Missik , Haley Kujawa , Joel A. Paulson , Gil Bohrer

{"title":"Bayesian Optimization for Anything (BOA): An open-source framework for accessible, user-friendly Bayesian optimization","authors":"Madeline E. Scyphers , Justine E.C. Missik , Haley Kujawa , Joel A. Paulson , Gil Bohrer","doi":"10.1016/j.envsoft.2024.106191","DOIUrl":"10.1016/j.envsoft.2024.106191","url":null,"abstract":"<div><p>We introduce Bayesian Optimization for Anything (BOA), a high-level Bayesian Optimization (BO) framework and model wrapping toolkit, which presents a novel approach to simplifying BO, with the goal of making it more accessible and user-friendly, particularly for those with limited expertise in the field. BOA addresses common barriers in implementing BO, focusing on ease of use, reducing the need for deep domain knowledge, and cutting down on extensive coding requirements. A notable feature of BOA is its language-agnostic architecture, which facilitates broader application in various fields and to a wider audience. We showcase BOA's application through three examples: a high-dimensional optimization with 184 parameters of the SWAT + watershed model, a highly parallelized optimization of this intrinsically non-parallel model, and a multi-objective optimization of the FETCH Tree-Crown Hydrodynamics model. These test cases illustrate BOA's effectiveness in addressing complex optimization challenges in diverse scenarios.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"182 ","pages":"Article 106191"},"PeriodicalIF":4.8,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101385","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

ForestAdvisor: A multi-modal forest decision-making system based on carbon emissions ForestAdvisor：基于碳排放的多模式森林决策系统

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-08-23 DOI: 10.1016/j.envsoft.2024.106190

Tong Ji , Yifeng Lin , Yuer Yang

{"title":"ForestAdvisor: A multi-modal forest decision-making system based on carbon emissions","authors":"Tong Ji , Yifeng Lin , Yuer Yang","doi":"10.1016/j.envsoft.2024.106190","DOIUrl":"10.1016/j.envsoft.2024.106190","url":null,"abstract":"<div><p>Effectively balancing carbon emission reduction with economic viability through regional forest management is a significant challenge for global ecosystems. This paper introduces an innovative multi-modal forest decision-making system, integrating deep learning and natural language processing technologies, aimed at optimizing forest management strategies. Experimental validation of this system was conducted in three distinct forested regions. Utilizing a deep learning model, the system analyzed and predicted daily carbon emissions data. The experiments demonstrated remarkable accuracy, with the model achieving a coefficient of determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) of up to 0.94, 0.98, and 0.99 across datasets from all three regions, thereby justifying its use for forecasting carbon emission trends over the following months. Subsequently, the system employed natural language processing to assess the importance of various collected forest management strategies. Finally, the system fine-tuned these strategy combinations in response to the predicted carbon emission trends, ensuring flexibility and effectiveness in addressing the complex dynamics of carbon emission fluctuations.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"181 ","pages":"Article 106190"},"PeriodicalIF":4.8,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077006","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