Environmental Modelling & Software最新文献

英文中文

A geospatial model for real-time predicting rural fire propagation velocity using dynamic algorithms and open data for advanced emergency management

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106355

Carlos Brys , David Luis La Red Martínez , Marcelo Marinelli

When a fire is detected in a rural environment, it is imperative to know the dynamics of the fire's development. Knowing the fire's trajectory is vital since the firefront will have shifted when first responders reach the ignition site. We developed a fast rural fire propagation calculation algorithm that can predict the fire front trajectory 6 h from the time of detection, taking as input data only the latitude and longitude coordinates of the detected hot spot, and obtaining all the necessary data from open online sources. In response to the pressing demand for effective fire control strategies in rural areas, this paper introduces a computational analytical model to predict the fire speed of rural fire behavior. By integrating topographic, meteorological, and land use data, our system offers on-demand fire behavior forecasts, addressing a critical need in the field. With the key component, a predictor, our system identifies patterns and provides crucial information to decision-makers. This comprehensive approach positions our system as an invaluable tool for rescue teams and decision-makers engaged in the proactive battle against rural fires.

{"title":"A geospatial model for real-time predicting rural fire propagation velocity using dynamic algorithms and open data for advanced emergency management","authors":"Carlos Brys , David Luis La Red Martínez , Marcelo Marinelli","doi":"10.1016/j.envsoft.2025.106355","DOIUrl":"10.1016/j.envsoft.2025.106355","url":null,"abstract":"<div><div>When a fire is detected in a rural environment, it is imperative to know the dynamics of the fire's development. Knowing the fire's trajectory is vital since the firefront will have shifted when first responders reach the ignition site. We developed a fast rural fire propagation calculation algorithm that can predict the fire front trajectory 6 h from the time of detection, taking as input data only the latitude and longitude coordinates of the detected hot spot, and obtaining all the necessary data from open online sources. In response to the pressing demand for effective fire control strategies in rural areas, this paper introduces a computational analytical model to predict the fire speed of rural fire behavior. By integrating topographic, meteorological, and land use data, our system offers on-demand fire behavior forecasts, addressing a critical need in the field. With the key component, a predictor, our system identifies patterns and provides crucial information to decision-makers. This comprehensive approach positions our system as an invaluable tool for rescue teams and decision-makers engaged in the proactive battle against rural fires.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106355"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143336704","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

Spatiotemporal flood depth and velocity dynamics using a convolutional neural network within a sequential Deep-Learning framework 时序深度学习框架中使用卷积神经网络的时空洪水深度和速度动态

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106307

Mohamed M. Fathi , Zihan Liu , Anjali M. Fernandes , Michael T. Hren , Dennis O. Terry , C. Nataraj , Virginia Smith

Computational hydrodynamic models support river science and management. However, current physics-based models face computational challenges; they require extensive processing time for large-scale two-dimensional flood simulations. Despite the success of Deep Learning (DL) applications in generating inundation maps, accurate prediction of unsteady flood hydrodynamic maps remains challenging. This paper compares traditional approaches to a novel DL approach, which integrates convolutional neural networks with long short-term memory, to deliver precise, rapid, and continuous simulation of the spatiotemporal dynamics of river floods. This is the first DL framework able to generate essential hydrodynamic variables: water depth, velocity magnitude, and flow direction maps. Water depth and velocity magnitude predictions across the testing dataset are robust, with average RMSE of 0.14 m and 0.02 m/s, respectively. The DL predictions are 415 times faster compared to traditional computational approaches, representing a paradigm shift in hydrodynamics modeling that advances long-term flood simulations and resilient river management.

计算水动力模型支持河流科学和管理。然而，目前基于物理的模型面临着计算方面的挑战；大规模二维洪水模拟需要大量的处理时间。尽管深度学习（DL）在生成淹没图方面取得了成功，但准确预测非定常洪水水动力图仍然具有挑战性。本文将传统方法与一种新颖的深度学习方法进行了比较，该方法将卷积神经网络与长短期记忆相结合，以提供精确、快速和连续的河流洪水时空动态模拟。这是第一个能够生成基本流体动力学变量的深度学习框架：水深、速度大小和流向图。整个测试数据集的水深和速度大小预测非常稳健，平均RMSE分别为0.14 m和0.02 m/s。与传统计算方法相比，DL预测速度快415倍，代表了水动力学建模的范式转变，推动了长期洪水模拟和弹性河流管理。

{"title":"Spatiotemporal flood depth and velocity dynamics using a convolutional neural network within a sequential Deep-Learning framework","authors":"Mohamed M. Fathi , Zihan Liu , Anjali M. Fernandes , Michael T. Hren , Dennis O. Terry , C. Nataraj , Virginia Smith","doi":"10.1016/j.envsoft.2024.106307","DOIUrl":"10.1016/j.envsoft.2024.106307","url":null,"abstract":"<div><div>Computational hydrodynamic models support river science and management. However, current physics-based models face computational challenges; they require extensive processing time for large-scale two-dimensional flood simulations. Despite the success of Deep Learning (DL) applications in generating inundation maps, accurate prediction of unsteady flood hydrodynamic maps remains challenging. This paper compares traditional approaches to a novel DL approach, which integrates convolutional neural networks with long short-term memory, to deliver precise, rapid, and continuous simulation of the spatiotemporal dynamics of river floods. This is the first DL framework able to generate essential hydrodynamic variables: water depth, velocity magnitude, and flow direction maps. Water depth and velocity magnitude predictions across the testing dataset are robust, with average RMSE of 0.14 m and 0.02 m/s, respectively. The DL predictions are 415 times faster compared to traditional computational approaches, representing a paradigm shift in hydrodynamics modeling that advances long-term flood simulations and resilient river management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106307"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905661","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

Parameter estimation and uncertainty quantification of rainfall-runoff models using data assimilation methods based on deep learning and local ensemble updates 基于深度学习和局部集合更新的数据同化方法的降雨径流模型参数估计和不确定性量化

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106332

Lei Yao , Jiangjiang Zhang , Chenglong Cao , Feifei Zheng

Rainfall-runoff (RR) modeling is crucial for flood preparedness and water resource management. Accurate RR model predictions depend on effective parameter estimation and uncertainty quantification using observed data through data assimilation (DA). Traditional DA methods often struggle with challenges such as non-Gaussianity and equifinality. To address these challenges, this study introduces two ensemble smoother methods, i.e., ES_DL with a deep learning-based update, and ES_LU with a local ensemble update, aiming to enhance the calibration of RR models. To demonstrate the effectiveness of our proposed methods, we conduct a comprehensive analysis involving various DA techniques applied to parameter estimation of RR models. We compare these methods with traditional approaches, evaluating deep neural network architectures, iteration numbers, and measurement errors. The results unequivocally showcase the consistent reliability of ES_DL and ES_LU, especially the latter one, across diverse scenarios, establishing them as promising approaches for the effective calibration and uncertainty quantification of RR models.

降雨径流（RR）模型对防洪和水资源管理至关重要。准确的RR模型预测依赖于通过数据同化（DA）对观测数据进行有效的参数估计和不确定性量化。传统的数据分析方法经常面临非高斯性和等价性等问题。为了解决这些问题，本研究引入了两种集成平滑方法，即基于深度学习更新的ESDL和基于局部集成更新的ESLU，旨在增强RR模型的校准。为了证明我们提出的方法的有效性，我们进行了综合分析，涉及各种数据挖掘技术应用于RR模型的参数估计。我们将这些方法与传统方法进行比较，评估深度神经网络架构、迭代次数和测量误差。结果明确表明，ESDL和ESLU在不同情景下具有一致的可靠性，特别是后者，这表明它们是有效校准和不确定度量化RR模型的有希望的方法。

{"title":"Parameter estimation and uncertainty quantification of rainfall-runoff models using data assimilation methods based on deep learning and local ensemble updates","authors":"Lei Yao , Jiangjiang Zhang , Chenglong Cao , Feifei Zheng","doi":"10.1016/j.envsoft.2025.106332","DOIUrl":"10.1016/j.envsoft.2025.106332","url":null,"abstract":"<div><div>Rainfall-runoff (RR) modeling is crucial for flood preparedness and water resource management. Accurate RR model predictions depend on effective parameter estimation and uncertainty quantification using observed data through data assimilation (DA). Traditional DA methods often struggle with challenges such as non-Gaussianity and equifinality. To address these challenges, this study introduces two ensemble smoother methods, i.e., ES<sub>DL</sub> with a deep learning-based update, and ES<sub>LU</sub> with a local ensemble update, aiming to enhance the calibration of RR models. To demonstrate the effectiveness of our proposed methods, we conduct a comprehensive analysis involving various DA techniques applied to parameter estimation of RR models. We compare these methods with traditional approaches, evaluating deep neural network architectures, iteration numbers, and measurement errors. The results unequivocally showcase the consistent reliability of ES<sub>DL</sub> and ES<sub>LU</sub>, especially the latter one, across diverse scenarios, establishing them as promising approaches for the effective calibration and uncertainty quantification of RR models.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106332"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020245","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 Concawe NO2 source apportionment viewer: A web-application to mitigate NO2 pollution from traffic and other sources Concawe二氧化氮来源分配查看器：一个减少交通和其他来源的二氧化氮污染的网络应用程序

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106315

Bart Degraeuwe , Robin Houdmeyers , Stijn Janssen , Wouter Lefebvre , Athanasios Megaritis

To mitigate air pollution, source apportionment is a key element for the design of effective measures. However, source apportionment often involves complex model chains only accessible to expert users. In this paper we present a new web-application, the Concawe NO₂ source apportionment viewer. It allows experts and non-expert users to evaluate the contributions of different sectors and the impact of measures in the road transport sector on current and future NO₂ pollution in the EU27+UK in a fast and user-friendly way. The methodology behind the viewer was described in a previous paper byDegraeuwe et al. (2024). Here we describe the user interface and give some examples; the contribution of different sectors to the NO₂ concentrations in the 3136 monitoring stations, and the impact of specific transport policies (e.g., Euro 7/VII standard, urban access regulations) on the NO₂ concentrations in 948 European cities.

为了减轻空气污染，污染源分配是设计有效措施的关键要素。然而，源分配通常涉及只有专家用户才能访问的复杂模型链。在本文中，我们提出了一个新的web应用程序，Concawe NO2源分配查看器。它允许专家和非专家用户以快速和用户友好的方式评估不同部门的贡献以及道路运输部门措施对欧盟27国+英国当前和未来二氧化氮污染的影响。degraeuwe等人（2024）在之前的一篇论文中描述了观看者背后的方法。这里我们描述了用户界面并给出了一些例子；不同部门对3136个监测站NO2浓度的贡献，以及特定交通政策（如欧7/VII标准、城市通行条例）对948个欧洲城市NO2浓度的影响。

引用次数: 0

An integrated modeling approach to assess water-energy nexus in a semi-arid watershed 半干旱流域水-能关系综合建模方法研究

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106326

Zeynep Özcan , Merih Aydınalp Köksal , Emre Alp

The synergies and conflicts between the energy and water systems, necessitate the collaboration between these sectors. Effective management of the interdependent energy and water systems requires a nexus approach that acknowledges these interconnections, as opposed to regarding them as distinct systems. We applied an integrated modeling approach for evaluating the Water-Energy Nexus based on a variety of criteria as water consumption, energy production, and CO₂ emissions. According to the simulations, 96% reduction in water savings can be achieved when wet cooling systems of the thermal power plant (TPP) are converted to dry. Moreover, if the TPPs are shut down to reduce CO₂ emissions, the hydroelectric power plants can only cover 16% of the total electricity production. Hence, securing energy while reducing CO₂ emissions is a challenging task. Despite producing only 10–15% of total energy, HPPs account for 70–100% of total water consumption in all scenarios.

能源和水系统之间的协同作用和冲突需要这些部门之间的合作。有效管理相互依赖的能源和水系统需要一种联系的方法，承认这些相互联系，而不是将它们视为不同的系统。我们采用了一种综合建模方法，基于水消耗、能源生产和二氧化碳排放等多种标准来评估水-能源关系。模拟结果表明，将火电厂湿式冷却系统转换为干式冷却系统可节约96%的用水。此外，如果为了减少二氧化碳排放而关闭tpp，水力发电厂只能满足总发电量的16%。因此，在确保能源安全的同时减少二氧化碳排放是一项具有挑战性的任务。尽管仅生产总能源的10-15%，但在所有情况下，HPPs占总用水量的70-100%。

引用次数: 0

Fire dynamic vision: Image segmentation and tracking for multi-scale fire and plume behavior 火灾动态视觉：多尺度火灾和烟羽行为的图像分割与跟踪

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106286

Daryn Sagel, Bryan Quaife

The increasing frequency and severity of wildfires highlight the need for accurate fire and plume spread models. We introduce an approach that effectively isolates and tracks fire and plume behavior across various spatial and temporal scales and image types, identifying physical phenomena in the system and providing insights useful for developing and validating models. Our method combines image segmentation and graph theory to delineate fire fronts and plume boundaries. We demonstrate that the method effectively distinguishes fires and plumes from visually similar objects. Results demonstrate the successful isolation and tracking of fire and plume dynamics across various image sources, ranging from synoptic-scale (

1 0^{4}

–

1 0^{5}

m) satellite images to sub-microscale (

1 0^{0}

–

1 0^{1}

m) images captured close to the fire environment. Furthermore, the methodology leverages image inpainting and spatio-temporal dataset generation for use in statistical and machine learning models.

野火发生的频率和严重程度不断增加，这凸显了对精确的火灾和烟羽蔓延模型的需求。我们介绍了一种方法，该方法可有效隔离和跟踪各种时空尺度和图像类型中的火灾和烟羽行为，识别系统中的物理现象，并提供对开发和验证模型有用的见解。我们的方法结合了图像分割和图论来划分火锋和烟羽边界。我们证明，该方法能有效地将火灾和烟羽与视觉上相似的物体区分开来。结果表明，该方法可成功隔离和跟踪各种图像来源中的火灾和羽流动态，包括从同步尺度（104-105 米）卫星图像到接近火灾环境的亚微观尺度（100-101 米）图像。此外，该方法还利用了图像内绘和时空数据集生成技术，可用于统计和机器学习模型。

{"title":"Fire dynamic vision: Image segmentation and tracking for multi-scale fire and plume behavior","authors":"Daryn Sagel, Bryan Quaife","doi":"10.1016/j.envsoft.2024.106286","DOIUrl":"10.1016/j.envsoft.2024.106286","url":null,"abstract":"<div><div>The increasing frequency and severity of wildfires highlight the need for accurate fire and plume spread models. We introduce an approach that effectively isolates and tracks fire and plume behavior across various spatial and temporal scales and image types, identifying physical phenomena in the system and providing insights useful for developing and validating models. Our method combines image segmentation and graph theory to delineate fire fronts and plume boundaries. We demonstrate that the method effectively distinguishes fires and plumes from visually similar objects. Results demonstrate the successful isolation and tracking of fire and plume dynamics across various image sources, ranging from synoptic-scale (<span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>4</mn></mrow></msup></mrow></math></span>–<span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>5</mn></mrow></msup></mrow></math></span> m) satellite images to sub-microscale (<span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>0</mn></mrow></msup></mrow></math></span>–<span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>1</mn></mrow></msup></mrow></math></span> m) images captured close to the fire environment. Furthermore, the methodology leverages image inpainting and spatio-temporal dataset generation for use in statistical and machine learning models.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106286"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825321","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

AMPSOM: A measureable pool soil organic carbon and nitrogen model for arable cropping systems 一个可测量的耕地种植系统土壤有机碳和氮模型

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106291

Inès Astrid Tougma , Marijn Van de Broek , Johan Six , Thomas Gaiser , Maire Holz , Isabel Zentgraf , Heidi Webber

Most cropping system models simulate conceptual soil organic matter (SOM) pools, such as active, passive and slow pools that cannot be measured, complicating model calibration. In reality, SOM can be described in terms of quantifiable pools of particulate organic matter (POM) and mineral-associated organic matter (MAOM) which respond differently to management and climate. We present the AMPSOM model, integrated in a cropping system modelling framework (SIMPLACE). AMPSOM simulates carbon and nitrogen dynamics in MAOM and POM in response to crop growth and management, as well as soil texture, water and nitrogen content and temperature. It also simulates the radiocarbon isotope (¹⁴C) of soil organic carbon (SOC) to constrain the turnover time of slowly cycling SOC pools. Model calibration and evaluation were performed for thirty six sandy and loamy arable soils in Brandenburg, Germany. Results show that AMPSOM can reproduce observed patterns of SOC and nitrogen stocks in POM and MAOM along depth profiles across different soil types.

大多数种植系统模型模拟的是概念土壤有机质（SOM）库，如无法测量的主动、被动和缓慢库，使模型校准复杂化。在现实中，SOM可以用可量化的颗粒有机质（POM）和矿物相关有机质（MAOM）池来描述，它们对管理和气候的响应不同。我们提出了集成在种植系统建模框架（SIMPLACE）中的AMPSOM模型。AMPSOM模拟MAOM和POM中碳氮动态，以响应作物生长和管理，以及土壤质地，水氮含量和温度。模拟土壤有机碳（SOC）的放射性碳同位素（14C），以约束缓慢循环的有机碳库的周转时间。对德国勃兰登堡地区36种沙质和壤土进行了模型标定和评价。结果表明，AMPSOM能沿不同土壤类型重现POM和MAOM土壤有机碳和氮储量的分布规律。

{"title":"AMPSOM: A measureable pool soil organic carbon and nitrogen model for arable cropping systems","authors":"Inès Astrid Tougma , Marijn Van de Broek , Johan Six , Thomas Gaiser , Maire Holz , Isabel Zentgraf , Heidi Webber","doi":"10.1016/j.envsoft.2024.106291","DOIUrl":"10.1016/j.envsoft.2024.106291","url":null,"abstract":"<div><div>Most cropping system models simulate conceptual soil organic matter (SOM) pools, such as active, passive and slow pools that cannot be measured, complicating model calibration. In reality, SOM can be described in terms of quantifiable pools of particulate organic matter (POM) and mineral-associated organic matter (MAOM) which respond differently to management and climate. We present the AMPSOM model, integrated in a cropping system modelling framework (SIMPLACE). AMPSOM simulates carbon and nitrogen dynamics in MAOM and POM in response to crop growth and management, as well as soil texture, water and nitrogen content and temperature. It also simulates the radiocarbon isotope (<sup>14</sup>C) of soil organic carbon (SOC) to constrain the turnover time of slowly cycling SOC pools. Model calibration and evaluation were performed for thirty six sandy and loamy arable soils in Brandenburg, Germany. Results show that AMPSOM can reproduce observed patterns of SOC and nitrogen stocks in POM and MAOM along depth profiles across different soil types.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106291"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825340","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

Balancing simulation performance and computational intensity of CA models for large-scale land-use change simulations 平衡大规模土地利用变化模拟中 CA 模型的模拟性能和计算强度

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106293

Zhewei Liang , Xun Liang , Xintong Jiang , Tingyu Li , Qingfeng Guan

Large-scale land-use change simulations are crucial for understanding land dynamics, investigating climate change, and shaping policy regulations. However, conducting fine-resolution land-use change simulations on a large scale is challenging due to high computational demands. Conversely, land-use change simulations with coarse-resolution data distort spatial details, thereby reducing simulation performance. Parallel computing can reduce computational demands but requires significant computational resources. Mixed-cell CA models offer a solution to balance simulation performance and computational intensity. The comparison experiments using various resolution land use datasets demonstrate that mixed-cell CA models, even those with coarse-resolution data, achieve results comparable to those of pure-cell CA models using fine-resolution data, but with significantly reduced simulation time. This highlights the efficiency of mixed-cell CA models in achieving comparable performance with lower computational intensity. Additionally, this study provides a measurement method for the uncertainty of mixed-cell CA models. In summary, this study reveals the unique advantages of mixed-cell CA models in efficient large-scale land use simulations, thereby providing valuable insights and guidance for future land use management and policy decisions.

大规模土地利用变化模拟对于理解土地动态、调查气候变化和制定政策法规至关重要。然而，由于高计算需求，在大尺度上进行高分辨率土地利用变化模拟具有挑战性。相反，使用粗分辨率数据的土地利用变化模拟会扭曲空间细节，从而降低模拟性能。并行计算可以减少计算需求，但需要大量的计算资源。混合单元CA模型提供了平衡仿真性能和计算强度的解决方案。使用不同分辨率土地利用数据集的对比实验表明，混合元胞CA模型，即使是使用粗分辨率数据的混合元胞CA模型，也可以获得与使用精细分辨率数据的纯元胞CA模型相当的结果，但可以显著缩短模拟时间。这突出了混合单元CA模型在较低计算强度下实现相当性能的效率。此外，本研究还提供了一种混合单元CA模型不确定度的测量方法。综上所述，本研究揭示了混合单元CA模型在高效大规模土地利用模拟中的独特优势，从而为未来的土地利用管理和政策决策提供了有价值的见解和指导。

{"title":"Balancing simulation performance and computational intensity of CA models for large-scale land-use change simulations","authors":"Zhewei Liang , Xun Liang , Xintong Jiang , Tingyu Li , Qingfeng Guan","doi":"10.1016/j.envsoft.2024.106293","DOIUrl":"10.1016/j.envsoft.2024.106293","url":null,"abstract":"<div><div>Large-scale land-use change simulations are crucial for understanding land dynamics, investigating climate change, and shaping policy regulations. However, conducting fine-resolution land-use change simulations on a large scale is challenging due to high computational demands. Conversely, land-use change simulations with coarse-resolution data distort spatial details, thereby reducing simulation performance. Parallel computing can reduce computational demands but requires significant computational resources. Mixed-cell CA models offer a solution to balance simulation performance and computational intensity. The comparison experiments using various resolution land use datasets demonstrate that mixed-cell CA models, even those with coarse-resolution data, achieve results comparable to those of pure-cell CA models using fine-resolution data, but with significantly reduced simulation time. This highlights the efficiency of mixed-cell CA models in achieving comparable performance with lower computational intensity. Additionally, this study provides a measurement method for the uncertainty of mixed-cell CA models. In summary, this study reveals the unique advantages of mixed-cell CA models in efficient large-scale land use simulations, thereby providing valuable insights and guidance for future land use management and policy decisions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106293"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825337","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

Facilitating open data and open model integration with generic parameter input file generators in the CyberWater framework

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106266

Daniel Luna , Ranran Chen , Ahmed Sheba , Ryan Young , Yao Liang , Xu Liang

Effective data and model integration is crucial for exploring scientific questions in hydrology and other geosciences. The increasing heterogeneity and complexity of data and models pose integration challenges. CyberWater addresses these with an open-data and open-modeling framework. Featuring GUI-based workflows, it includes Data Agents for accessing diverse online data sources and a Generic Model Agent Toolkit for seamless, code-free model integration. This study introduces the Static Parameter Agent suite, a novel toolkit designed to streamline the creation and organization of parameter files required for various models. The toolkit enables users to efficiently and automatically generate files on demand, minimizing the time-consuming and error-prone manual preparation of complex parameter files. It further logs all changes to parameter values across each model simulation, ensuring a reproducible end-to-end process. It connects seamlessly with Geographic Information System (GIS) engines like GRASS GIS and has been tested on models including VIC, DHSVM, and CASA-CNP.

{"title":"Facilitating open data and open model integration with generic parameter input file generators in the CyberWater framework","authors":"Daniel Luna , Ranran Chen , Ahmed Sheba , Ryan Young , Yao Liang , Xu Liang","doi":"10.1016/j.envsoft.2024.106266","DOIUrl":"10.1016/j.envsoft.2024.106266","url":null,"abstract":"<div><div>Effective data and model integration is crucial for exploring scientific questions in hydrology and other geosciences. The increasing heterogeneity and complexity of data and models pose integration challenges. CyberWater addresses these with an open-data and open-modeling framework. Featuring GUI-based workflows, it includes Data Agents for accessing diverse online data sources and a Generic Model Agent Toolkit for seamless, code-free model integration. This study introduces the Static Parameter Agent suite, a novel toolkit designed to streamline the creation and organization of parameter files required for various models. The toolkit enables users to efficiently and automatically generate files on demand, minimizing the time-consuming and error-prone manual preparation of complex parameter files. It further logs all changes to parameter values across each model simulation, ensuring a reproducible end-to-end process. It connects seamlessly with Geographic Information System (GIS) engines like GRASS GIS and has been tested on models including VIC, DHSVM, and CASA-CNP.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106266"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128006","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

A novel sample-enhancement framework for machine learning-based urban flood susceptibility assessment 基于机器学习的城市洪水易感性评估样本增强框架

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software

Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106314

Huabing Huang, Changpeng Wang, Zhiwen Tao, Jiayin Zhan

The commonly used random sampling method in machine learning-based flood susceptibility studies has two major issues: a default invalid assumption of spatial homogeneity and an inadequate number of non-flood samples. To address these issues, this study proposed a novel sample-enhancement framework to improve the quality of training samples on both flood and non-flood sides. Three one-way enhancements (two flood and one non-flood) and two joint enhancements were designed. The enhancements were evaluated against random sampling using four mainstream machine learning algorithms (ANN, RF, SVM, and XGBoost) across two heterogeneous urban regions in Guangzhou, China. The highest performances are achieved by the joint enhancements, which are followed by one-way enhancements and random sampling (no enhancement). Another important conclusion is that one-way enhancements exhibit divergent yet complementary effects. Flood enhancements primarily affect susceptibility distribution (mean value and standard deviation), while non-flood enhancements mainly influence binary classification performance (AUC).

基于机器学习的洪水敏感性研究中常用的随机抽样方法存在两个主要问题：默认的空间均匀性假设无效和非洪水样本数量不足。为了解决这些问题，本研究提出了一种新的样本增强框架，以提高洪水侧和非洪水侧的训练样本质量。设计了三个单向增强（两个洪水增强和一个非洪水增强）和两个联合增强。使用四种主流机器学习算法（ANN， RF， SVM和XGBoost）在中国广州的两个异质城市区域对随机抽样进行了增强评估。通过联合增强实现了最高的性能，随后是单向增强和随机抽样（无增强）。另一个重要的结论是，单向增强表现出不同但互补的效果。洪水增强主要影响敏感性分布（均值和标准差），非洪水增强主要影响二元分类性能（AUC）。

{"title":"A novel sample-enhancement framework for machine learning-based urban flood susceptibility assessment","authors":"Huabing Huang, Changpeng Wang, Zhiwen Tao, Jiayin Zhan","doi":"10.1016/j.envsoft.2024.106314","DOIUrl":"10.1016/j.envsoft.2024.106314","url":null,"abstract":"<div><div>The commonly used random sampling method in machine learning-based flood susceptibility studies has two major issues: a default invalid assumption of spatial homogeneity and an inadequate number of non-flood samples. To address these issues, this study proposed a novel sample-enhancement framework to improve the quality of training samples on both flood and non-flood sides. Three one-way enhancements (two flood and one non-flood) and two joint enhancements were designed. The enhancements were evaluated against random sampling using four mainstream machine learning algorithms (ANN, RF, SVM, and XGBoost) across two heterogeneous urban regions in Guangzhou, China. The highest performances are achieved by the joint enhancements, which are followed by one-way enhancements and random sampling (no enhancement). Another important conclusion is that one-way enhancements exhibit divergent yet complementary effects. Flood enhancements primarily affect susceptibility distribution (mean value and standard deviation), while non-flood enhancements mainly influence binary classification performance (AUC).</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106314"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905678","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

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Environmental Modelling & Software

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀