Environmental Modelling & Software最新文献_第8页

ExMAD (Expert-based Multitemporal AI Detector): An open-source methodological framework for remote and field landslide inventory

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-04 DOI: 10.1016/j.envsoft.2025.106363

Michele Licata, Stefano Faga , Giandomenico Fubelli

{"title":"ExMAD (Expert-based Multitemporal AI Detector): An open-source methodological framework for remote and field landslide inventory","authors":"Michele Licata, Stefano Faga , Giandomenico Fubelli","doi":"10.1016/j.envsoft.2025.106363","DOIUrl":"10.1016/j.envsoft.2025.106363","url":null,"abstract":"<div><div>Landslides threaten lives and infrastructure, making accurate inventories crucial for risk management. This study combines expert methods with machine learning to automate and validate landslide detection and timing using Sentinel-2 satellite imagery. We developed ExMAD (Expert-based Multi-temporal AI Detector), an open-source methodological framework (<span><span>https://github.com/NewGeoProjects/ExMAD</span><svg><path></path></svg></span>) to integrate artificial intelligence with human expertise to detect occurrence timing of a targeted landslide. A U-Net neural network was chosen to effectively test ExMAD in landslide detection over Sentinel-2 worldwide multitemporal satellite imagery sequences, and the model was tested through five evaluations. ExMAD was able to effectively extract timing of target landslides on Sentinel-2 images and was able to correctly detect the presence/absence of landslide, proving the suitability of AI systems in landslide temporal mapping task.</div><div>This research proves the potential of hybrid AI-human approaches for landslide risk assessment, integrate human expertise with machine learning offers promising advancements for remote and field mapping of landslide. Furthermore, the ExMAD methodology adheres to the European Union's Artificial Intelligence Act, stressing human oversight in high-risk AI applications to enhance trust, control, and efficiency in landslide inventory creation and risk management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106363"},"PeriodicalIF":4.8,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143350438","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

SWMManywhere: A workflow for generation and sensitivity analysis of synthetic urban drainage models, anywhere

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-02 DOI: 10.1016/j.envsoft.2025.106358

Barnaby Dobson , Tijana Jovanovic , Diego Alonso-Álvarez , Taher Chegini

{"title":"SWMManywhere: A workflow for generation and sensitivity analysis of synthetic urban drainage models, anywhere","authors":"Barnaby Dobson , Tijana Jovanovic , Diego Alonso-Álvarez , Taher Chegini","doi":"10.1016/j.envsoft.2025.106358","DOIUrl":"10.1016/j.envsoft.2025.106358","url":null,"abstract":"<div><div>Improvements in public geospatial datasets provide opportunities for deriving urban drainage networks and simulation models of these networks (UDMs). We present SWMManywhere, which leverages such datasets for generating synthetic UDMs and creating a Storm Water Management Model for any urban area globally. SWMManywhere's modular and parameterised approach enables customisation to explore hydraulicly feasible network configurations. Key novelties of our workflow are in network topology derivation that accounts for combined effects of impervious area and pipe slope. We assess SWMManywhere by comparing pluvial flooding, drainage network outflows, and design with known networks. The results demonstrate high quality simulations are achievable with a synthetic approach even for large networks. Our sensitivity analysis shows that manholes locations, outfalls, and underlying street network are the most sensitive parameters. We find widespread sensitivity across all parameters without clearly defined values that they should take, thus, recommending an uncertainty driven approach to synthetic drainage network modelling.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"186 ","pages":"Article 106358"},"PeriodicalIF":4.8,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379431","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 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 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 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":"10.1016/j.envsoft.2024.106301","url":null,"abstract":"<div><div>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 (<em>NRI</em>) 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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106301"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","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

Analysis and comparison of the flood simulations with the routing model CaMa-Flood at different spatial resolutions in the CONUS CONUS中不同空间分辨率下CaMa-Flood路由模型的洪水模拟分析与比较

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106305

Ruijie Jiang , Hui Lu , Kun Yang , Hiroshi Cho , Dai Yamazaki

{"title":"Analysis and comparison of the flood simulations with the routing model CaMa-Flood at different spatial resolutions in the CONUS","authors":"Ruijie Jiang , Hui Lu , Kun Yang , Hiroshi Cho , Dai Yamazaki","doi":"10.1016/j.envsoft.2024.106305","DOIUrl":"10.1016/j.envsoft.2024.106305","url":null,"abstract":"<div><div>Accurate flood modelling is crucial for disaster prevention. Fine-resolution global routing models can offer more detailed flood information, but balancing model efficiency with accuracy remains challenging. This study examines the conditions under which a fine-resolution model outperforms a coarser one, using the CaMa-Flood model at 0.05°, 0.083°, 0.1°, and 0.25° resolutions across the contiguous United States. The results indicate finer resolution does not improve the simulation of flood timing, but better simulates the daily river discharge and flood peak flow due to better representation of the river network in small rivers. Notably, the improvement in daily discharge simulation is greater than that in peak flow. Nevertheless, uncertainties in channel parameters mean that a more detailed river network does not necessarily yield better flood simulations. For rivers with upstream drainage areas greater than 500 km<sup>2</sup>, a 0.25° model is sufficient if high-precision channel parameters are unavailable.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106305"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884321","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

Estimating landslide trigger factors using distributed lag nonlinear models

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106259

Aadityan Sridharan , Meerna Thomas , Georg Gutjahr , Sundararaman Gopalan

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 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 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":"10.1016/j.envsoft.2024.106290","url":null,"abstract":"<div><div>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 <em>NSE</em> 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 <em>NSE</em> performance of above 0.5 for most sites in short to medium-term forecasting.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106290"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","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

Cascading effect modelling of integrating geographic factors in interdependent systems 相互依存系统中整合地理因素的级联效应建模

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106316

Yong Ge , Mo Zhang , Rongtian Zhao , Die Zhang , Zhiyi Zhang , Daoping Wang , Qiuming Cheng , Yuxue Cui , Jian Liu

{"title":"Cascading effect modelling of integrating geographic factors in interdependent systems","authors":"Yong Ge , Mo Zhang , Rongtian Zhao , Die Zhang , Zhiyi Zhang , Daoping Wang , Qiuming Cheng , Yuxue Cui , Jian Liu","doi":"10.1016/j.envsoft.2024.106316","DOIUrl":"10.1016/j.envsoft.2024.106316","url":null,"abstract":"<div><div>Cascading effects from global disruptions such as natural disasters and pandemics have attracted significant research attention. Current approaches face challenges in adequately integrating geographic and systemic factors, limiting their ability to simulate the intricate dynamics of interdependent systems. Here, we proposed a novel Interdependency Network-based Geographic Cascade (INGC) model, coupling geographic factors to capture cascading shocks across global interdependent networks. By integrating macro-level interdependencies and typical dynamic network modelling approaches, the INGC enables more accurate simulations of hazard damage and shock propagation, highlighting critical nodes and pathways essential for informed policy-making. Through the global lockdown case analysis, the INGC model demonstrated its advantages in identifying critical sectors and regions by revealing heterogenous cascading patterns and their details robustly. This approach offers a scalable framework for future research and policy, ensuring greater resilience in the face of complex global extreme events.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106316"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935471","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 process-based framework for validating forest landscape modeling outcomes 用于验证森林景观建模结果的基于过程的框架

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106327

Mia M. Wu , Yu Liang , Hong S. He , Jian Yang , Bo Liu , Tianxiao Ma

{"title":"A process-based framework for validating forest landscape modeling outcomes","authors":"Mia M. Wu , Yu Liang , Hong S. He , Jian Yang , Bo Liu , Tianxiao Ma","doi":"10.1016/j.envsoft.2025.106327","DOIUrl":"10.1016/j.envsoft.2025.106327","url":null,"abstract":"<div><div>Forest landscape models (FLMs) simulate forest dynamics by integrating stand- and landscape-scale processes. Thus, evaluating FLMs simulations necessitates including both processes. Thus far, stand-scale processes were evaluated in some FLMs, whereas landscape-scale processes were rarely evaluated. This study presents a framework that evaluates both stand- and landscape-scale processes. For the stand-scale processes, we proposed using stand density management diagrams to evaluate the simulated stand development trajectories that encapsulate the interplay of tree growth, competition, and mortality. For the landscape-scale processes, we evaluated seed dispersal, the basic spatial process driving forest landscape dynamics and not evaluated previously, through comparing simulated tree species colonization pattern against tree age distribution data from inventory data. We demonstrated the applicability of the framework to a 300-year historical forest landscape reconstructed using LANDIS. Given the common features, the framework is applicable to other FLMs or terrestrial ecosystem models operating at large scales.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106327"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990596","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 EarthObsNet：用于数据驱动的地球观测图像合成的综合基准数据集

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106292

Zhouyayan Li , Yusuf Sermet , Ibrahim Demir

引用次数: 0

AI-driven forecasting of harmful algal blooms in Persian Gulf and Gulf of Oman using remote sensing 人工智能驱动的波斯湾和阿曼湾有害藻华遥感预测

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106311

Amirreza Shahmiri, Mohamad Hosein Seyed-Djawadi, Seyed Mostafa Siadatmousavi

{"title":"AI-driven forecasting of harmful algal blooms in Persian Gulf and Gulf of Oman using remote sensing","authors":"Amirreza Shahmiri, Mohamad Hosein Seyed-Djawadi, Seyed Mostafa Siadatmousavi","doi":"10.1016/j.envsoft.2024.106311","DOIUrl":"10.1016/j.envsoft.2024.106311","url":null,"abstract":"<div><div>This study develops an artificial intelligence (AI) model to forecast harmful algal blooms (HABs) in the Persian Gulf and Gulf of Oman using freely available remote sensing data, including chlorophyll-a (Chl-a), sea surface temperature (SST), salinity, and wind. The model introduces novel features such as spatial and temporal standard deviations of Chl-a concentration and a derived gradient feature. Correlation analysis indicated that these features enhance predictive capability. A multi-layer artificial neural network (ANN) was trained using a 66%/34% data split for training and testing, achieving 88.7% accuracy in binary classification (bloom/non-bloom) with an area under the ROC curve (AUC) of 90.1%. Overfitting was mitigated by monitoring training and validation loss, both of which consistently decreased over epochs, confirming robust model generalization. The use of standard deviation in SST and salinity highlights their influence on bloom dynamics, providing key insights into algal bloom drivers. The focus on freely available data enables stakeholders to better manage the environmental challenges posed by HABs.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106311"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905668","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