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EcoCommons Australia virtual laboratories with cloud computing: Meeting diverse user needs for ecological modeling and decision-making 澳大利亚生态社区利用云计算建立虚拟实验室:满足用户对生态建模和决策的不同需求
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-03 DOI: 10.1016/j.envsoft.2024.106255
Elisa Bayraktarov , Samantha Low-Choy , Abhimanyu Raj Singh , Linda J. Beaumont , Kristen J. Williams , John B. Baumgartner , Shawn W. Laffan , Daniela Vasco , Robert Cosgrove , Jenna Wraith , Jessica Fenker Antunes , Brendan Mackey
Biodiversity decline and climate change are among the most important environmental issues society faces. Information to address these issues has benefited from increasing big data, advances in cloud computing, and subsequent new tools for analytics. Accessing such tools is streamlined by virtual laboratories for ecological analysis, like the ‘Biodiversity and Climate Change Virtual Laboratory’ (BCCVL) and ‘ecocloud’. These platforms help reduce time and effort spent on developing programming skills, data acquisition and curation, plus model building. Recently this functionality was extended, producing EcoCommons Australia—a web-based ecological modeling platform for environmental problem-solving—with upgraded infrastructure and improved ensemble modeling, post-model analysis, workflow transparency and reproducibility. We outline our user-centered approach to systems design, from initial surveys of stakeholder needs to user involvement in testing, and collaboration with specialists. We illustrate EcoCommons and compare model evaluation statistics through four case studies, highlighting how the modular platform meets users' needs.
生物多样性减少和气候变化是社会面临的最重要的环境问题之一。解决这些问题的信息得益于不断增加的大数据、云计算的进步以及随之而来的新分析工具。生物多样性和气候变化虚拟实验室"(BCCVL)和 "ecocloud "等生态分析虚拟实验室简化了对这些工具的访问。这些平台有助于减少开发编程技能、数据采集和整理以及建立模型所花费的时间和精力。最近,这一功能得到了扩展,产生了澳大利亚生态共用平台(EcoCommons Australia)--一个用于解决环境问题的基于网络的生态建模平台--升级了基础设施,改进了集合建模、建模后分析、工作流程透明度和可重复性。我们概述了以用户为中心的系统设计方法,从最初的利益相关者需求调查到用户参与测试以及与专家合作。我们通过四个案例研究来说明生态共享系统并比较模型评估统计数据,突出模块化平台是如何满足用户需求的。
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引用次数: 0
An adaptable dead fuel moisture model for various fuel types and temporal scales tailored for wildfire danger assessment 为野火危险评估量身定制的适用于各种燃料类型和时间尺度的死燃料湿度模型
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-02 DOI: 10.1016/j.envsoft.2024.106254
Nicolò Perello , Andrea Trucchia , Mirko D’Andrea , Silvia Degli Esposti , Paolo Fiorucci , Andrea Gollini , Dario Negro
Estimating the Dead Fuel Moisture Content (DFMC) is crucial in wildfire risk management, representing a key component in forest fire danger rating systems and wildfire simulation models. DFMC fluctuates sub-daily and spatially, influenced by local weather and fuel characteristics. This necessitates models that provide sub-daily fuel moisture conditions for improving wildfire risk management. Many forest fire danger rating systems typically rely on daily fuel moisture models that overlook local fuel characteristics, with consequent impact on wildfire management. The semi-empirical parametric DFMC model proposed addresses these issues by providing hourly dead fuel moisture dynamics, with specific parameters to consider local fuel characteristics. A calibration framework is proposed by adopting Particle Swarm Optimization-type algorithm. In the present study, the calibration framework has been tested by using hourly 10-h fuel sticks measurements. Implementing this model in forest fire danger rating systems would enhance detail in forest fire danger conditions, advancing wildfire risk management.
估算枯燃料水分含量(DFMC)对野火风险管理至关重要,是森林火险评级系统和野火模拟模型的关键组成部分。受当地天气和燃料特性的影响,死燃料水分含量每天都有不同程度的波动。这就需要建立能提供次日燃料湿度条件的模型,以改善野外火险管理。许多森林火险等级系统通常依赖于每日燃料湿度模型,而这些模型会忽略当地的燃料特征,从而影响野火管理。所提出的半经验参数化 DFMC 模型通过提供每小时死燃料湿度动态来解决这些问题,并提供特定参数以考虑当地燃料特性。采用粒子群优化算法提出了一个校准框架。在本研究中,利用每小时 10 小时的燃料棒测量结果对校准框架进行了测试。在森林火险等级系统中实施该模型将提高森林火险状况的详细程度,从而推进野外火险管理。
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引用次数: 0
Physics of Complex Systems: Discovery in the Age of Gödel Dragutin T. Mihailović, Darko Kapor, Siniša Crvenković and Anja Mihailović CRC Press 2024, 202 pp.eBook ISBN: 978-1-003-27857-3, Hardcover ISBN: 978-1-032-22801-3 复杂系统物理学:Dragutin T. Mihailović、Darko Kapor、Siniša Crvenković 和 Anja Mihailović CRC Press 2024,202 pp.电子书 ISBN:978-1-003-27857-3,精装 ISBN:978-1-032-22801-3
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-11-02 DOI: 10.1016/j.envsoft.2024.106256
Carlo Gualtieri
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引用次数: 0
A QGIS framework for physically-based probabilistic modelling of landslide susceptibility: QGIS-FORM 基于物理的滑坡易发性概率建模 QGIS 框架:QGIS-FORM
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-29 DOI: 10.1016/j.envsoft.2024.106258
Jian Ji , Bin Tong , Hong-Zhi Cui , Xin-Tao Tang , Marcel Hürlimann , Shigui Du
Earthquake-induced regional landslides frequently result in substantial economic losses and casualties. Conducting landslide susceptibility assessments is essential for mitigating these risks and minimizing potential damage. To address the diverse needs of professionals in various disciplines, we have developed an open-source plugin for QGIS, named QGIS-FORM. This plugin integrates functions of both physically-based model (PM) and physically-based probabilistic model (PPM). The PM employs pseudo-static infinite slope stability model, while the PPM utilizes an improved first order reliability method (FORM) to perform landslide probability analysis over a spatial region. To verify its effectiveness, the plugin was applied to the Maerkang landslide event in 2022. Based on the PM and the PPM, the landslide susceptibility assessments were evaluated using several parameters including slope, aspect, stratum, and PGA. In addition, the Receiver Operating Characteristic (ROC) curve and Balanced Accuracy were employed to assess their predictive performance. The landslide susceptibility results indicate that landslides in Maerkang are mostly concentrated in slopes between 30° and 50°, and the geological conditions of the Xinduqiao Formation (T3X) are more prone to landslides. Compared to PM, the PPM can achieve higher AUC values when the parameter uncertainties are properly characterized. Overall, the PPM exhibits higher accuracy and is more capable of identifying potential landslides than the physically-based model, thereby providing a more reliable way and/or offering a scientific basis for the management and mitigation of landslide disaster risks.
地震引发的区域性山体滑坡经常造成重大经济损失和人员伤亡。进行滑坡易发性评估对于降低这些风险和减少潜在损失至关重要。为了满足各学科专业人员的不同需求,我们为 QGIS 开发了一个开源插件,名为 QGIS-FORM。该插件集成了基于物理的模型(PM)和基于物理的概率模型(PPM)的功能。物理模型采用伪静态无限边坡稳定性模型,而概率模型则利用改进的一阶可靠性方法(FORM)对空间区域进行滑坡概率分析。为验证其有效性,该插件被应用于 2022 年的马康滑坡事件。在 PM 和 PPM 的基础上,使用多个参数(包括坡度、坡向、地层和 PGA)对滑坡易发性进行了评估。此外,还采用了接收者工作特征曲线(ROC)和平衡精度来评估其预测性能。滑坡易发性结果表明,马尔康的滑坡主要集中在 30° 至 50° 的斜坡上,新都桥地层(T3X)的地质条件更容易发生滑坡。与 PM 相比,当参数的不确定性得到适当描述时,PPM 可获得更高的 AUC 值。总体而言,与基于物理的模型相比,PPM 模型具有更高的精度和更强的识别潜在滑坡的能力,从而为滑坡灾害风险的管理和缓解提供了更可靠的方法和/或科学依据。
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引用次数: 0
A coordination attention residual U-Net model for enhanced short and mid-term sea surface temperature prediction 用于加强短期和中期海面温度预测的协调注意残余 U-Net 模型
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-28 DOI: 10.1016/j.envsoft.2024.106251
Zhao Sun, Yongxian Wang
Sea surface temperature (SST) is crucial for studying global oceans and evaluating ecosystems. Accurately predicting short and mid-term daily SST has been a significant challenge in oceanography. Traditional deep learning methods can handle temporal data and spatial features but often struggle with long-range spatiotemporal dependencies. To address this, we propose a coordination attention residual U-Net(CResU-Net) model designed to better capture the dynamic spatiotemporal correlations of high-resolution SST. The model integrates coordinate attention mechanisms, multiple residual modules, and depthwise separable convolutions to enhance prediction capabilities. The spatiotemporal variations of SST across different areas of the South China Sea are complex, making accurate predictions challenging. Experiments across various regions of the South China Sea show the model’s effectiveness and robust generalization in predicting high-resolution daily SST. For a 10-day forecast period, the model achieves approximately 0.3 °C in RMSE, outperforming several advanced models.
海洋表面温度(SST)对于研究全球海洋和评估生态系统至关重要。准确预测短期和中期的日 SST 一直是海洋学领域的重大挑战。传统的深度学习方法可以处理时间数据和空间特征,但在处理长程时空依赖性时往往力不从心。针对这一问题,我们提出了一种协调注意残差 U-Net 模型(CResU-Net),旨在更好地捕捉高分辨率 SST 的动态时空相关性。该模型整合了协调注意机制、多个残差模块和深度可分离卷积,以增强预测能力。南海不同区域的 SST 时空变化非常复杂,因此准确预测具有挑战性。在南海不同区域进行的实验表明,该模型在预测高分辨率日海温方面效果显著,并具有强大的泛化能力。在 10 天的预报期内,该模式的均方根误差(RMSE)约为 0.3 ℃,优于多个先进模式。
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引用次数: 0
An R package to partition observation data used for model development and evaluation to achieve model generalizability 一个 R 软件包,用于分割用于模型开发和评估的观测数据,以实现模型的普适性
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-25 DOI: 10.1016/j.envsoft.2024.106238
Yiran Ji , Feifei Zheng , Jinhua Wen , Qifeng Li , Junyi Chen , Holger R. Maier , Hoshin V. Gupta
Development of environmental models generally requires available data to be split into “development” and “evaluation” subsets. How this is done can significantly affect a model's outputs and performance. However, data splitting is generally done in a subjective, ad-hoc manner, with little justification, raising questions regarding the reliability of the findings of many modelling studies. To address this issue, we present and demonstrate the value of an R-package along with high-level guidelines for implementing many state-of-the-art data splitting methods in order to develop the model in a considered, defensible, consistent, repeatable and transparent fashion, thereby improving the generalizability of the resulting models. Results from two rainfall-runoff case studies show that models with high generalization ability can be achieved even when the available data contain rare, extreme events. Additionally, data splitting methods can be used to explicitly quantify the parameter uncertainty associated with data splitting and the resulting bounds on model predictions.
开发环境模型通常需要将可用数据分成 "开发 "和 "评估 "两个子集。如何分割会对模型的输出结果和性能产生重大影响。然而,数据分割通常是以主观的、临时的方式进行的,没有什么正当理由,这就对许多建模研究结果的可靠性提出了质疑。为了解决这个问题,我们介绍并演示了 R 软件包的价值,以及实施许多最先进数据拆分方法的高级指南,以便以一种经过深思熟虑、可辩护、一致、可重复和透明的方式开发模型,从而提高所生成模型的可推广性。两个降雨-径流案例研究的结果表明,即使现有数据包含罕见的极端事件,也可以建立具有高泛化能力的模型。此外,数据拆分方法可用于明确量化与数据拆分相关的参数不确定性以及由此产生的模型预测界限。
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引用次数: 0
Dynamics of real-time forecasting failure and recovery due to data gaps: A study using EnKF-based assimilation with the Lorenz model 数据缺口导致的实时预报失败和恢复动态:基于 EnKF 的洛伦兹模式同化研究
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-22 DOI: 10.1016/j.envsoft.2024.106250
Sicheng Wu, Ruo-Qian Wang
Data assimilation-based real-time forecasting is widely used in meteorological and hydrological applications, where continuous data streams are employed to update forecasts and maintain accuracy. However, the reliability of the data source can be compromised due to sensor and communication failures or physical or cyber-attacks, and the impact of data stream failures on the accuracy of the forecasting system is not well understood. This study aims to systematically investigate the process of data stream failure and recovery for the first time. To achieve this, data gaps with varying lengths and timings are introduced to EnKF-based data assimilation system on the Lorenz model operating in both chaotic and periodic modes. Results show that the forecasting error grows exponentially in the chaotic mode but was limited in the periodic mode from the start of the data gap. For chaotic mode, the recovery of the system depends on the length of the data gap if the model error is not saturated; after saturation, the timing of the data stream recovery is important. Moreover, even long after restarting the data assimilation in the chaotic mode, the forecasting system cannot fully restore the original accuracy, while the periodic mode is generally resilient to disruption. This research introduces new metrics for quantifying system resilience and provides crucial insights into the long-term implications of data gaps, advancing our understanding of forecasting system behavior and reliability.
基于数据同化的实时预报广泛应用于气象和水文领域,利用连续数据流更新预报并保持准确性。然而,由于传感器和通信故障、物理或网络攻击等原因,数据源的可靠性可能会受到影响,而数据流故障对预报系统准确性的影响尚不十分清楚。本研究旨在首次系统地研究数据流故障和恢复过程。为此,在基于 EnKF 的数据同化系统中引入了不同长度和时间的数据间隙,这些数据间隙是以混沌和周期模式运行的洛伦兹模型。结果表明,在混沌模式下,预报误差呈指数增长,但在周期模式下,从数据间隙开始,预报误差就受到了限制。在混沌模式下,如果模型误差未达到饱和,系统的恢复取决于数据间隙的长度;饱和之后,数据流恢复的时间非常重要。此外,即使在混沌模式下重启数据同化很长时间后,预报系统也无法完全恢复原来的精度,而周期模式一般对中断具有很强的恢复能力。这项研究引入了量化系统恢复能力的新指标,并对数据缺口的长期影响提出了重要见解,从而推进了我们对预报系统行为和可靠性的理解。
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引用次数: 0
Identification of pedestrian submerged parts in urban flooding based on images and deep learning 基于图像和深度学习识别城市洪水中的行人淹没部分
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-19 DOI: 10.1016/j.envsoft.2024.106252
Jingchao Jiang , Xinle Feng , Jingzhou Huang , Jiaqi Chen , Min Liu , Changxiu Cheng , Junzhi Liu , Anke Xue
During urban flooding, pedestrians are often trapped in floodwater, and some pedestrians even fall or drown. The pedestrian submerged part (i.e., the human body part that water surface reaches) is an important reference indicator for judging dangerous situation of pedestrians. Flood images usually contain the information about pedestrian submerged parts. We proposed an automated method for identifying pedestrian submerged parts from images. This method utilizes relevant deep learning technologies to segment water surfaces, detect the pedestrians in floodwater, and detect the human keypoints of the pedestrians from images, and then identify submerged parts of the pedestrians according to the relationship between the human keypoints and the water surfaces. This method achieves an accuracy of 90.71% in identifying pedestrian submerged parts on an image dataset constructed from Internet images. The result shows that this method could effectively identify pedestrian submerged parts from images with high accuracy.
城市洪水泛滥时,行人往往会被困在洪水中,有些行人甚至会摔倒或溺水。行人的淹没部位(即水面到达的人体部位)是判断行人危险状况的重要参考指标。洪水图像通常包含行人淹没部位的信息。我们提出了一种从图像中自动识别行人淹没部位的方法。该方法利用相关的深度学习技术对水面进行分割,检测洪水中的行人,并从图像中检测行人的人体关键点,然后根据人体关键点与水面的关系识别行人的淹没部位。在由互联网图像构建的图像数据集上,该方法识别行人淹没部分的准确率达到 90.71%。结果表明,该方法能有效、高精度地识别图像中的行人淹没部分。
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引用次数: 0
Avoid backtracking and burn your inputs: CONUS-scale watershed delineation using OpenMP 避免回溯和烧毁输入:使用 OpenMP 进行 CONUS 规模流域划分
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-18 DOI: 10.1016/j.envsoft.2024.106244
Huidae Cho
The Memory-Efficient Watershed Delineation (MESHED) parallel algorithm is introduced for Contiguous United States (CONUS)-scale hydrologic modeling. Delineating tens of thousands of watersheds for a continental-scale study can not only be computationally intensive, but also be memory-consuming. Existing algorithms require separate input and output data stores. However, as the number of watersheds to delineate and the resolution of input data grow significantly, the amount of memory required for an algorithm also quickly increases. MESHED uses one data store for both input and output by destructing input data as processed and a node-skipping depth-first search to further reduce required memory. For 1000 watersheds in Texas, MESHED performed 95 % faster than the Central Processing Unit (CPU) benchmark algorithm using 33 % less memory. In a scaling experiment, it delineated 100,000 watersheds across the CONUS in 13.64 s. Given the same amount of memory, MESHED can solve 50 % larger problems than the CPU benchmark algorithm can.
针对美国毗连区(CONUS)尺度的水文建模,介绍了内存效率流域划分(MESHED)并行算法。为一项大陆尺度的研究划分数以万计的流域不仅计算量大,而且耗费内存。现有算法需要单独的输入和输出数据存储。然而,随着需要划定的流域数量和输入数据分辨率的大幅增加,算法所需的内存量也会迅速增加。MESHED 在处理输入数据时将其销毁,并采用节点跳转深度优先搜索,从而将输入和输出数据存储在同一个数据存储区,进一步减少了所需内存。对于德克萨斯州的 1000 个流域,MESHED 的运行速度比中央处理器(CPU)基准算法快 95%,内存占用率却低 33%。在一次扩展实验中,它在 13.64 秒内就划定了整个美国的 100,000 个流域。在内存容量相同的情况下,MESHED 可以解决的问题比 CPU 基准算法大 50%。
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引用次数: 0
A conceptual data modeling framework with four levels of abstraction for environmental information 环境信息四级抽象概念数据模型框架
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-18 DOI: 10.1016/j.envsoft.2024.106248
David Martínez , Laura Po , Raquel Trillo-Lado , José R.R. Viqueira
Environmental data generated by observation infrastructures and models is widely heterogeneous in both structure and semantics. The design and implementation of an ad hoc data model for each new dataset is costly and creates barriers for data integration. On the other hand, designing a single data model that supports any kind of environmental data has shown to be a complex task, and the resulting tools do not provide the required efficiency. In this paper, a new data modeling framework is proposed that enables the reuse of generic structures among different application domains and specific applications. The framework considers four levels of abstraction for the data models. Levels 1 and 2 provide general data model structures for environmental data, based on those defined by the Observations and Measurements (O&M) standard of the Open Geospatial Consortium (OGC). Level 3 incorporates generic data models for different application areas, whereas specific application models are designed at Level 4, reusing structures of the previous levels. Various use cases were implemented to illustrate the capabilities of the framework. A performance evaluation using six datasets of three different use cases has shown that the query response times achieved over the structures of Level 4 are very good compared to both ad hoc models and to a direct implementation of O&M in a Sensor Observation Service (SOS) tool. A qualitative evaluation shows that the framework fulfills a collection of general requirements not supported by any other existing solution.
观测基础设施和模型产生的环境数据在结构和语义上都存在很大的差异。为每一个新数据集设计和实施一个临时数据模型的成本很高,而且会给数据集成造成障碍。另一方面,设计一个支持各种环境数据的单一数据模型已被证明是一项复杂的任务,而且由此产生的工具无法提供所需的效率。本文提出了一种新的数据建模框架,可在不同应用领域和特定应用之间重用通用结构。该框架考虑了数据模型的四个抽象层次。第 1 层和第 2 层以开放地理空间联盟(OGC)的观测与测量(O&M)标准为基础,提供了环境数据的通用数据模型结构。第 3 级包含不同应用领域的通用数据模型,而具体的应用模型则在第 4 级设计,重复使用前几级的结构。为说明该框架的能力,我们实施了各种用例。使用三个不同用例的六个数据集进行的性能评估表明,与临时模型和在传感器观测服务(SOS)工具中直接实施 O&M 相比,第 4 层结构的查询响应时间非常出色。定性评估显示,该框架满足了一系列其他现有解决方案无法支持的一般要求。
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引用次数: 0
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Environmental Modelling & Software
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