Environmental Modelling & Software最新文献_第3页

Transformer-embedded 1D VGG convolutional neural network for regional landslides detection boosted by multichannel data inputs 利用多通道数据输入促进区域山体滑坡检测的变压器嵌入式一维 VGG 卷积神经网络

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-08 DOI: 10.1016/j.envsoft.2024.106261

Bangjie Fu , Yange Li , Chen Wang , Zheng Han , Nan Jiang , Wendu Xie , Changli Li , Haohui Ding , Weidong Wang , Guangqi Chen

{"title":"Transformer-embedded 1D VGG convolutional neural network for regional landslides detection boosted by multichannel data inputs","authors":"Bangjie Fu , Yange Li , Chen Wang , Zheng Han , Nan Jiang , Wendu Xie , Changli Li , Haohui Ding , Weidong Wang , Guangqi Chen","doi":"10.1016/j.envsoft.2024.106261","DOIUrl":"10.1016/j.envsoft.2024.106261","url":null,"abstract":"<div><div>Up-to-date studies have proved the effectiveness of Convolutional Neural Networks (CNN) in landslide detection. With the rapid development of Remote Sensing and Geographic Information System technologies, an increasing amount of spectral and non-spectral information is available for CNN modeling. It offering a comprehensive perspective for landslide detection, but also presents challenges to CNNs, especially in efficiently learning long-range feature associations. Therefore, we proposed a novel Transformer-improved VGG network (Trans-VGG). It takes spectral (RGB images) and non-spectral information (elevation, slope, and PCA components) as data inputs and integrating both local and global feature in modeling. The method is tested in two landslide cluster areas in Litang County, China. The results in site a show that the Trans-VGG model demonstrates an improvement in F1-score, ranging from 4% to 21%, compared with the conventional machine learning and CNN models. The validation result in site b further proved the validity of our proposed method.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106261"},"PeriodicalIF":4.8,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654737","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

Data-driven fire modeling: Learning first arrival times and model parameters with neural networks 数据驱动的火灾建模：利用神经网络学习首批到达时间和模型参数

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-06 DOI: 10.1016/j.envsoft.2024.106253

Xin Tong , Bryan Quaife

引用次数: 0

Combining residual convolutional LSTM with attention mechanisms for spatiotemporal forest cover prediction 将残差卷积 LSTM 与注意力机制相结合，用于时空森林覆盖率预测

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-04 DOI: 10.1016/j.envsoft.2024.106260

Bao Liu , Siqi Chen , Lei Gao

{"title":"Combining residual convolutional LSTM with attention mechanisms for spatiotemporal forest cover prediction","authors":"Bao Liu , Siqi Chen , Lei Gao","doi":"10.1016/j.envsoft.2024.106260","DOIUrl":"10.1016/j.envsoft.2024.106260","url":null,"abstract":"<div><div>Understanding spatiotemporal variations in forest cover is crucial for effective forest resource management. However, existing models often lack accuracy in simultaneously capturing temporal continuity and spatial correlation. To address this challenge, we developed ResConvLSTM-Att, a novel hybrid model integrating residual neural networks, Convolutional Long Short-Term Memory (ConvLSTM) networks, and attention mechanisms. We evaluated ResConvLSTM-Att against four deep learning models: LSTM, combined convolutional neural network and LSTM (CNN-LSTM), ConvLSTM, and ResConvLSTM for spatiotemporal prediction of forest cover in Tasmania, Australia. ResConvLSTM-Att achieved outstanding prediction performance, with an average root mean square error (RMSE) of 6.9% coverage and an impressive average coefficient of determination of 0.965. Compared with LSTM, CNN-LSTM, ConvLSTM, and ResConvLSTM, ResConvLSTM-Att achieved RMSE reductions of 31.2%, 43.0%, 10.1%, and 6.5%, respectively. Additionally, we quantified the impacts of explanatory variables on forest cover dynamics. Our work demonstrated the effectiveness of ResConvLSTM-Att in spatiotemporal data modelling and prediction.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106260"},"PeriodicalIF":4.8,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654735","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

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

Environmental Modelling & Software 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

{"title":"EcoCommons Australia virtual laboratories with cloud computing: Meeting diverse user needs for ecological modeling and decision-making","authors":"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","doi":"10.1016/j.envsoft.2024.106255","DOIUrl":"10.1016/j.envsoft.2024.106255","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106255"},"PeriodicalIF":4.8,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655281","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

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

Environmental Modelling & Software 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

{"title":"An adaptable dead fuel moisture model for various fuel types and temporal scales tailored for wildfire danger assessment","authors":"Nicolò Perello , Andrea Trucchia , Mirko D’Andrea , Silvia Degli Esposti , Paolo Fiorucci , Andrea Gollini , Dario Negro","doi":"10.1016/j.envsoft.2024.106254","DOIUrl":"10.1016/j.envsoft.2024.106254","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106254"},"PeriodicalIF":4.8,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654736","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

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

Environmental Modelling & Software Pub Date : 2024-11-02 DOI: 10.1016/j.envsoft.2024.106256

Carlo Gualtieri

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

Environmental Modelling & Software 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

{"title":"A QGIS framework for physically-based probabilistic modelling of landslide susceptibility: QGIS-FORM","authors":"Jian Ji , Bin Tong , Hong-Zhi Cui , Xin-Tao Tang , Marcel Hürlimann , Shigui Du","doi":"10.1016/j.envsoft.2024.106258","DOIUrl":"10.1016/j.envsoft.2024.106258","url":null,"abstract":"<div><div>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 (<span><math><mrow><msub><mi>T</mi><mn>3</mn></msub><mi>X</mi></mrow></math></span>) 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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106258"},"PeriodicalIF":4.8,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672816","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 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

Environmental Modelling & Software Pub Date : 2024-10-28 DOI: 10.1016/j.envsoft.2024.106251

Zhao Sun, Yongxian Wang

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

Environmental Modelling & Software 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

{"title":"An R package to partition observation data used for model development and evaluation to achieve model generalizability","authors":"Yiran Ji , Feifei Zheng , Jinhua Wen , Qifeng Li , Junyi Chen , Holger R. Maier , Hoshin V. Gupta","doi":"10.1016/j.envsoft.2024.106238","DOIUrl":"10.1016/j.envsoft.2024.106238","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106238"},"PeriodicalIF":4.8,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529818","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

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

Environmental Modelling & Software Pub Date : 2024-10-22 DOI: 10.1016/j.envsoft.2024.106250

Sicheng Wu, Ruo-Qian Wang

{"title":"Dynamics of real-time forecasting failure and recovery due to data gaps: A study using EnKF-based assimilation with the Lorenz model","authors":"Sicheng Wu, Ruo-Qian Wang","doi":"10.1016/j.envsoft.2024.106250","DOIUrl":"10.1016/j.envsoft.2024.106250","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106250"},"PeriodicalIF":4.8,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529744","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