Environmental Modelling & Software最新文献

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 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-12-12 DOI: 10.1016/j.envsoft.2024.106286

Daryn Sagel, Bryan Quaife

{"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":"https://doi.org/10.1016/j.envsoft.2024.106286","url":null,"abstract":"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 (<mml:math altimg=\"si1.svg\" display=\"inline\"><mml:mrow><mml:mn>1</mml:mn><mml:msup><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math>–<mml:math altimg=\"si2.svg\" display=\"inline\"><mml:mrow><mml:mn>1</mml:mn><mml:msup><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>5</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math> m) satellite images to sub-microscale (<mml:math altimg=\"si3.svg\" display=\"inline\"><mml:mrow><mml:mn>1</mml:mn><mml:msup><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math>–<mml:math altimg=\"si4.svg\" display=\"inline\"><mml:mrow><mml:mn>1</mml:mn><mml:msup><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math> 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.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"200 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-12-12","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

An explicit robust optimization framework for multipurpose cascade reservoir operation considering inflow uncertainty 考虑流入量不确定性的多用途梯级水库运行显式稳健优化框架

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-12-10 DOI: 10.1016/j.envsoft.2024.106301

Shaokun He, YiBo Wang, Dimitri Solomatine, Xiao Li

{"title":"An explicit robust optimization framework for multipurpose cascade reservoir operation considering inflow uncertainty","authors":"Shaokun He, YiBo Wang, Dimitri Solomatine, Xiao Li","doi":"10.1016/j.envsoft.2024.106301","DOIUrl":"https://doi.org/10.1016/j.envsoft.2024.106301","url":null,"abstract":"Long-term water resource management involving multipurpose coordination requires robust decision-making in water infrastructure cases to cope with various types of uncertainties. Traditional robust optimization methods generally do not explicitly propagate input or parametric uncertainties into estimates of the robustness of solutions, which limits their ability to address uncertainty comprehensively across solution spaces. In this study, we introduce an explicit robust decision-making framework that blends multiobjective search, probabilistic analysis of robustness, and diagnostic verification tools to identify robust optimal solutions to external uncertainty. The proposed framework is illustrated on four diverse robustness formulations, which capture a wide variety of stakeholder attitudes from highly risk-averse to risk-neutral, for the primary operating objectives (hydropower production, water diversion, and hydrological alteration degree) in China's Hanjiang cascade reservoir system. By analyzing the Pareto front propagated from inflow uncertainty, it is found that optimal robust policies with a significantly higher degree of hydrological alteration are preferred in most formulations to achieve relatively lower joint uncertainty of hydropower and water diversion. These policies also yield sufficiently stable model performance in the case of an out-of-sample streamflow set during diagnostic verification. Furthermore, a comparative analysis of four different formulations suggests that a composite normalized robustness indicator (<ce:italic>NRI</ce:italic>) developed in this study to integrate various robustness metrics can achieve an effective balance for all considered objectives. These findings highlight the benefits of explicit robust optimization for managing hydrological uncertainties in multipurpose cascade reservoirs.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"1 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825336","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

A novel operational water quality mobile prediction system with LSTM-Seq2Seq model 采用 LSTM-Seq2Seq 模型的新型运行水质移动预测系统

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-12-09 DOI: 10.1016/j.envsoft.2024.106290

Lizi Xie, Yanxin Zhao, Pan Fang, Meiling Cheng, Zhuo Chen, Yonggui Wang

{"title":"A novel operational water quality mobile prediction system with LSTM-Seq2Seq model","authors":"Lizi Xie, Yanxin Zhao, Pan Fang, Meiling Cheng, Zhuo Chen, Yonggui Wang","doi":"10.1016/j.envsoft.2024.106290","DOIUrl":"https://doi.org/10.1016/j.envsoft.2024.106290","url":null,"abstract":"An adequate water quality prediction mobile system is crucial for real-time, proactive, and convenient water environment monitoring through mobile devices to reduce or prevent water environmental threats. After exploring the feasibility and superiority of the LSTM-seq2seq model for predicting various water quality indicators, the optimal time step range for different length predictions was proposed. To verify the generalizability and reusability of the model, the performance differences of migrating models was investigated. Based on the entire process, we have developed a cost-effective, widely applicable, and sustainable operational prediction system framework. It was successfully applied in the Huangshui River Basin for two years. Results indicated that the model can achieve an <ce:italic>NSE</ce:italic> of above 0.5 for indicators with high coefficient of variation and above 0.75 for more stable indicators. When carrying out transfer applications, the model can achieve an <ce:italic>NSE</ce:italic> performance of above 0.5 for most sites in short to medium-term forecasting.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"23 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797864","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

EarthObsNet: A comprehensive Benchmark dataset for data-driven earth observation image synthesis

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-12-06 DOI: 10.1016/j.envsoft.2024.106292

Zhouyayan Li, Yusuf Sermet, Ibrahim Demir

引用次数: 0

AMPSOM: A measureable pool soil organic carbon and nitrogen model for arable cropping systems

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-12-06 DOI: 10.1016/j.envsoft.2024.106291

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

{"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":"https://doi.org/10.1016/j.envsoft.2024.106291","url":null,"abstract":"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 (<ce:sup loc=\"post\">14</ce: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.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"16 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-12-06","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":"","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 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-12-06 DOI: 10.1016/j.envsoft.2024.106293

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

{"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":"https://doi.org/10.1016/j.envsoft.2024.106293","url":null,"abstract":"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.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"18 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-12-06","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

VERE Py-framework: Dual environment for physically-informed machine learning in seismic landslide hazard mapping driven by InSAR VERE Py-框架：InSAR 驱动的地震滑坡灾害绘图中物理信息机器学习的双重环境

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-12-06 DOI: 10.1016/j.envsoft.2024.106287

Gerardo Grelle, Luigi Guerriero, Domenico Calcaterra, Diego Di Martire, Chiara Di Muro, Enza Vitale, Giuseppe Sappa

{"title":"VERE Py-framework: Dual environment for physically-informed machine learning in seismic landslide hazard mapping driven by InSAR","authors":"Gerardo Grelle, Luigi Guerriero, Domenico Calcaterra, Diego Di Martire, Chiara Di Muro, Enza Vitale, Giuseppe Sappa","doi":"10.1016/j.envsoft.2024.106287","DOIUrl":"https://doi.org/10.1016/j.envsoft.2024.106287","url":null,"abstract":"The VERE framework was designed and developed in Python to generate hazard confidence maps for seismic-induced landslides, leveraging advanced data analysis and machine learning capabilities. A Virtual Environment (VE) and a Real Environment (RE) containing, respectively, datasets and map sets, are the core of the framework. The Virtual Environment (VE) comprises datasets including morphometric, geotechnical, and hydrological metadata, which are generated assuming a normal distribution, based on representative recurrent values of these parameters in the study area. The Real Environment (RE) includes grid datasets with a common resolution, obtained through analytical preprocessing of various spatial data distributions, including InSAR (Interferometric Synthetic Aperture Radar) data. This data is processed to detect ongoing slope instability and the activity state of surveyed landslides. The framework employs numerical machine learning, trained on meta-solutions derived from an advanced simplified physical model. The model accounts for viscoplastic behavior as well as the reduction of shear strengths toward the residual state during seismic-induced sliding. Hazard confidence maps are produced through an ML-based prediction, considering co-seismic displacements and post-seismic mobility under different initial porewater pressures and seismicity scenarios. The test-site region is the Sele River valley located in an inter-Apennine sector of southern Italy, a seismic-prone area known for its recent seismic activity.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"7 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825339","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

A spatiotemporal autoregressive neural network interpolation method for discrete environmental factors 离散环境因素的时空自回归神经网络插值法

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-12-04 DOI: 10.1016/j.envsoft.2024.106289

Jin Qi, Wenting Lv, Junxia Zhu, Minyu Wang, Zhe Zhang, Guangyuan Zhang, Sensen Wu, Zhenhong Du

{"title":"A spatiotemporal autoregressive neural network interpolation method for discrete environmental factors","authors":"Jin Qi, Wenting Lv, Junxia Zhu, Minyu Wang, Zhe Zhang, Guangyuan Zhang, Sensen Wu, Zhenhong Du","doi":"10.1016/j.envsoft.2024.106289","DOIUrl":"https://doi.org/10.1016/j.envsoft.2024.106289","url":null,"abstract":"The spatiotemporal interpolation model is necessary for generating continuous distributions for spatiotemporally discrete sampling points. However, there remain challenges in spatiotemporal interpolation due to the complex spatiotemporal effect and the imprecise kernel functions. Here, we proposed a spatiotemporal autoregressive neural network interpolation model (STARNN) that incorporates adaptive spatiotemporal distance quantification and supervised learning. The 10-fold cross-validation modelling on sea surface temperature and coastal nutrients demonstrated that the STARNN model performs better than baseline models and can well depict reasonable spatiotemporal distributions for environmental factors. By proposing two stacked neural networks, the STARNN model can accurately integrate spatial and temporal distances and avoids subjective selection of the kernel function. This study developed a novel interpolation model for processing discrete spatiotemporal points by following the data-driven paradigm, which can offer decision support for simulating the spread of sea temperature anomalies and optimizing the distribution of water quality measurement stations.","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"41 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797866","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

Synthetic random environmental time series generation with similarity control, preserving original signal’s statistical characteristics

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-30 DOI: 10.1016/j.envsoft.2024.106283

Ofek Aloni, Gal Perelman, Barak Fishbain

引用次数: 0

QUAL2K water quality model: A comprehensive review of its applications, and limitations

IF 4.9 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2024-11-30 DOI: 10.1016/j.envsoft.2024.106284

Siti Salwa Mohamad Noor, Noor Aida Saad, Muhammad Fitri Mohd Akhir, Muhamad Syafiq Abd Rahim

引用次数: 0