Environmental Modelling & Software最新文献

Variable sensitivity analysis in groundwater level projections under climate change adopting a hybrid machine learning algorithm 采用混合机器学习算法对气候变化下的地下水位预测进行变量敏感性分析

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-13 DOI: 10.1016/j.envsoft.2024.106264

Ali Sharghi, Mehdi Komasi, Masoud Ahmadi

{"title":"Variable sensitivity analysis in groundwater level projections under climate change adopting a hybrid machine learning algorithm","authors":"Ali Sharghi, Mehdi Komasi, Masoud Ahmadi","doi":"10.1016/j.envsoft.2024.106264","DOIUrl":"10.1016/j.envsoft.2024.106264","url":null,"abstract":"<div><div>Studies on climate change have largely overlooked the delayed response of Ground Water Levels (GWL) to atmospheric conditions. This gap is critical because fluctuations in GWL can lead to hazards like land subsidence. This study addresses the issue by identifying optimal delay times for key variables, which improves GWL projection accuracy. The input data process consists of introducing meteorological and hydrological variables in the form of 42 combinations. Meteorological data under climate change scenarios were obtained by downscaling outputs from the General Circulation Models (GCMs) within the Shared Socio-economic Pathways (SSP) scenarios. So far, no similar study has attempted to rank such a wide array of delay time combinations. This study improves hybrid Random Forest and Genetic Algorithm (RF-GA) projections by introducing the best combination of input variables. The investigation assessed the performance of both the conventional Random Forest (RF) and the RF-GA in simulating groundwater fluctuations. The variable sensitivity analysis results indicated that watershed discharge holds a higher Variable Importance (VI) compared to meteorological variables. The findings in the validation section also demonstrated that the RF-GA outperformed an RF that runs on default hyperparameters. Temperature and evaporation show a 3 and 2-month delay time, respectively. It was discovered that precipitation was the only variable with two possible delay times of 2 and 4-month. Also, combinations with many and few variables performed poorly. The projection results indicate an increase of 6.8 and 7.1 cm in the average GWL in the Silakhor plain under the low-emission SSP1-2.6 and high-emission SSP5-8.5 scenarios, respectively.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106264"},"PeriodicalIF":4.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655283","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

Taxonomy of purposes, methods, and recommendations for vulnerability analysis 脆弱性分析的目的、方法和建议分类学

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-12 DOI: 10.1016/j.envsoft.2024.106269

Nathan Bonham , Joseph Kasprzyk , Edith Zagona

引用次数: 0

Integrated STL-DBSCAN algorithm for online hydrological and water quality monitoring data cleaning 用于在线水文和水质监测数据清理的 STL-DBSCAN 集成算法

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-10 DOI: 10.1016/j.envsoft.2024.106262

Chenyu Song , Jingyuan Cui , Yafei Cui , Sheng Zhang , Chang Wu , Xiaoyan Qin , Qiaofeng Wu , Shanqing Chi , Mingqing Yang , Jia Liu , Ruihong Chen , Haiping Zhang

{"title":"Integrated STL-DBSCAN algorithm for online hydrological and water quality monitoring data cleaning","authors":"Chenyu Song , Jingyuan Cui , Yafei Cui , Sheng Zhang , Chang Wu , Xiaoyan Qin , Qiaofeng Wu , Shanqing Chi , Mingqing Yang , Jia Liu , Ruihong Chen , Haiping Zhang","doi":"10.1016/j.envsoft.2024.106262","DOIUrl":"10.1016/j.envsoft.2024.106262","url":null,"abstract":"<div><div>Online hydrological and water quality monitoring data has become increasingly crucial for water environment management such as assessment and modeling. However, online monitoring data often contains erroneous or incomplete datasets, consequently affecting its operational use. In the study, we developed an automated data cleaning algorithm grounded in Seasonal-Trend decomposition using Loess (STL) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). STL identifies and corrects more obvious anomalies in the time series, followed by DBSCAN for further refinement, in which the reverse nearest neighbor method was employed to enhance the clustering accuracy. To improve anomaly detection, a two-level residual judgment threshold was applied. The algorithm has been successfully applied to three reservoirs in Shanghai, China, achieving the precision rate of 0.91 and recall rate of 0.81 for dissolved oxygen and pH. The proposed algorithm can be potentially applied for cleaning of environment monitoring data with high accuracy and stability.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106262"},"PeriodicalIF":4.8,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655282","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

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

WFNet: A hierarchical convolutional neural network for wildfire spread prediction WFNet：一种用于野火蔓延预测的分层卷积神经网络

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2023-09-30 DOI: 10.1016/j.envsoft.2023.105841

Wenyu Jiang , Yuming Qiao , Guofeng Su , Xin Li , Qingxiang Meng , Hongying Wu , Wei Quan , Jing Wang , Fei Wang

{"title":"WFNet: A hierarchical convolutional neural network for wildfire spread prediction","authors":"Wenyu Jiang , Yuming Qiao , Guofeng Su , Xin Li , Qingxiang Meng , Hongying Wu , Wei Quan , Jing Wang , Fei Wang","doi":"10.1016/j.envsoft.2023.105841","DOIUrl":"https://doi.org/10.1016/j.envsoft.2023.105841","url":null,"abstract":"<div><p>Pattern analysis in wildfire spread behaviors is crucial for rescue actions and disaster reduction. Deep learning methods have the potential to model the wildfire spread despite problems such as continuous time prediction and multimodal environmental encoding. Therefore, we present a novel hierarchical convolutional neural network (CNN) denoted as <em>WFNet</em> to model the spread pattern of wildfires. The core of <em>WFNet</em> is defining the spread spatiotemporal distribution field (SSDF) to describe the process of wildfire spread, enabling global optimization and end-to-end prediction. Then, a hierarchical State-Condition mechanism is implemented to progressively and efficiently encode high-order features pertaining to multimodal elements. The experimental results demonstrate that <em>WFNet</em> has a competitive performance to existing models in computation time and model accuracy. More interestingly, <em>WFNet</em> shows excellent robustness when input fire state is in an uncertain moment, enabling investigators to quickly backward the ignition from the fire perimeter.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"170 ","pages":"Article 105841"},"PeriodicalIF":4.9,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41086202","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