Environmental Modelling & Software最新文献_第6页

Multi-objective optimization of nature-based solutions in urban stormwater management: A scoping review 基于自然的城市雨水管理解决方案的多目标优化：范围综述

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-08-29 DOI: 10.1016/j.envsoft.2025.106659

A. Bista , K.A.H. Paus , I. Seifert-Dähnn

{"title":"Multi-objective optimization of nature-based solutions in urban stormwater management: A scoping review","authors":"A. Bista , K.A.H. Paus , I. Seifert-Dähnn","doi":"10.1016/j.envsoft.2025.106659","DOIUrl":"10.1016/j.envsoft.2025.106659","url":null,"abstract":"<div><div>Multi-objective optimization (MOO) methods are increasingly used to optimize the multifunctional benefits and costs of Nature-based solutions (NbS) in urban water management. However, comprehensive reviews of current research in this area remain limited. This scoping review analyzed 110 optimization studies focused on NbS in urban water management. It examined key components of the simulation-optimization (S/O) framework, including simulation models, optimization algorithms, objectives, spatial and temporal configurations.</div><div>Twenty-three algorithms were assessed for their applications, strengths and limitations. NSGA-II was the most widely used, while advanced algorithms were applied in a few studies for complex, high-dimensional problems.</div><div>Water quantity (87 %) and costs (93 %) were the most studied objectives, whereas only 11 % of studies addressed other socio-environmental objectives. Various challenges in the S/O framework, such as integrating socio-environmental benefits, model complexity, uncertainties and optimal solution selection, are discussed. Future research should prioritize proper objectives selection, socio-environmental objectives integration and advanced dynamic NbS planning.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106659"},"PeriodicalIF":4.6,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989797","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

Enhanced tensor factorization for spatiotemporal imputation of high-frequency water quality monitoring data 基于增强张量分解的高频水质监测数据时空插值

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-08-28 DOI: 10.1016/j.envsoft.2025.106667

Xuke Wu , Kun Shan , Friedrich Recknagel , Lan Wang , Mingsheng Shang

{"title":"Enhanced tensor factorization for spatiotemporal imputation of high-frequency water quality monitoring data","authors":"Xuke Wu , Kun Shan , Friedrich Recknagel , Lan Wang , Mingsheng Shang","doi":"10.1016/j.envsoft.2025.106667","DOIUrl":"10.1016/j.envsoft.2025.106667","url":null,"abstract":"<div><div>Automatic high-frequency monitoring (AHFM) of water quality is increasingly being deployed to enhance the management and scientific understanding of eutrophic lakes. However, the prevalence of missing data in such systems poses significant challenges, potentially compromising decision-making processes and distorting analytical or modelling outcomes. This study proposes an enhanced tensor factorization model, termed the Diverse Biases-integrated Adaptive Latent-factorization-of-tensors (DBAL), which decomposes high-dimensional data into low-rank components while incorporating diverse linear biases to capture temporal fluctuations and employing a differential evolution algorithm for adaptive hyperparameter optimization. Extensive empirical validation using real-world AHFM datasets from a large eutrophic lake in China demonstrates that our proposed DBAL consistently outperforms the state-of-the-art models, achieving 5.6 %–50.3 % and 5.7 %–43.7 % reductions in RMSE and MAE, alongside a 0.3 %–6.1 % improvement in R<sup>2</sup>. Notably, DBAL demonstrates variable-specific performance, with particularly accurate imputation for stable physical parameters while revealing greater challenges for biologically-active variables that exhibit stronger temporal dynamics.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106667"},"PeriodicalIF":4.6,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916857","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

Improving the fidelity and performance of a conceptual flood inundation mapping approach using a machine learning-based surrogate model 使用基于机器学习的代理模型提高概念性洪水淹没映射方法的保真度和性能

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-08-27 DOI: 10.1016/j.envsoft.2025.106664

Berina Mina Kilicarslan , Qianqiu Longyang , Victor Obi , Sagy Cohen , Ehab Meselhe , Marouane Temimi

{"title":"Improving the fidelity and performance of a conceptual flood inundation mapping approach using a machine learning-based surrogate model","authors":"Berina Mina Kilicarslan , Qianqiu Longyang , Victor Obi , Sagy Cohen , Ehab Meselhe , Marouane Temimi","doi":"10.1016/j.envsoft.2025.106664","DOIUrl":"10.1016/j.envsoft.2025.106664","url":null,"abstract":"<div><div>This study focuses on enhancing the accuracy of Flood Inundation Mapping (FIM) by utilizing a surrogate modeling approach. The Height Above the Nearest Drainage (HAND) method is used as our baseline FIM approach. The terrain-based HAND-FIM framework was developed to allow large-scale applications at low computational costs. A Surrogate Model (SM) is constructed using machine learning-based methodologies to emulate the high-fidelity Hydrologic Engineering Center-River Analysis System (HEC-RAS) model. HAND-FIM, generated using streamflow data from the National Water Model, serves as the input to the SM, while the flood extent predicted by HEC-RAS for the same event serves as the target. Results demonstrate that SM reduces false alarms in HAND-FIM by 18 % while improving the Critical Success Index score by 26 %. Integrating the SM offers a promising approach for enhancing flood prediction accuracy, mitigating HAND-FIM limitations, and providing fast, cost-effective solutions for operational FIM applications, especially in data- and resource-limited regions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106664"},"PeriodicalIF":4.6,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933615","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 Physics-informed deep neural network for the joint prediction of chlorophyll-a and hydrographic fields in the Mediterranean Sea 用于地中海叶绿素- A和水文场联合预测的物理信息深度神经网络

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-08-26 DOI: 10.1016/j.envsoft.2025.106660

Michela Sammartino , Lorenzo Della Cioppa , Simone Colella , Bruno Buongiorno Nardelli

{"title":"A Physics-informed deep neural network for the joint prediction of chlorophyll-a and hydrographic fields in the Mediterranean Sea","authors":"Michela Sammartino , Lorenzo Della Cioppa , Simone Colella , Bruno Buongiorno Nardelli","doi":"10.1016/j.envsoft.2025.106660","DOIUrl":"10.1016/j.envsoft.2025.106660","url":null,"abstract":"<div><div>Monitoring the ocean's four-dimensional state is essential for marine ecosystem preservation. Artificial Intelligence (AI) algorithms represent promising tools to merge satellite and in situ measurements, improving reconstructions of ocean interior dynamics. Here, we describe 4DMED-bionet, an AI-based model developed under the European Space Agency 4DMED-Sea project, designed to infer subsurface properties from surface observations. Combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers, the model reconstructs 4D fields of temperature, salinity, density and chlorophyll-a in the Mediterranean Sea. The algorithm includes a physics-informed loss function that imposes constraints on density predictions, improving its accuracy without degrading other outputs. 4DMED-bionet outperforms different deep learning models, providing a high-quality 4D dataset, available at <span><span>https://doi.org/10.25423/CMCC/4DMEDSEA_BIOPHYS_REP_3D</span><svg><path></path></svg></span>. This dataset includes 4D geostrophic velocities derived from reconstructed physical tracers and surface geostrophic currents. Scientific analysis of 4D data is ongoing, aiming to better understand the processes that couple phytoplankton responses with 3D physical dynamic.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106660"},"PeriodicalIF":4.6,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989798","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

Coherent integration of the terrestrial hydrosphere: Needs and pathways for knowledge convergence 陆地水圈的连贯整合：知识趋同的需要和途径

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-08-25 DOI: 10.1016/j.envsoft.2025.106663

Georgia Destouni

引用次数: 0

Multi-fidelity graph neural networks for efficient and accurate flood hazard mapping 多保真度图神经网络用于高效、准确的洪水灾害制图

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-08-23 DOI: 10.1016/j.envsoft.2025.106654

Mehdi Taghizadeh, Zanko Zandsalimi, Majid Shafiee-Jood, Negin Alemazkoor

{"title":"Multi-fidelity graph neural networks for efficient and accurate flood hazard mapping","authors":"Mehdi Taghizadeh, Zanko Zandsalimi, Majid Shafiee-Jood, Negin Alemazkoor","doi":"10.1016/j.envsoft.2025.106654","DOIUrl":"10.1016/j.envsoft.2025.106654","url":null,"abstract":"<div><div>Generating high-resolution flood hazard maps with traditional hydrodynamic models is computationally prohibitive. While surrogate models like graph neural networks (GNNs) offer a faster alternative, they require large, expensive-to-generate, high-fidelity training datasets. This study addresses the critical challenge of creating accurate surrogate models with limited high-fidelity data. We propose a novel multi-fidelity graph neural network (MFGNN) framework that integrates numerous inexpensive, coarse-resolution simulations with a few high-fidelity runs. The method uses a hierarchical pipeline where one GNN learns broad flood patterns from low-fidelity data, and a second GNN learns to predict and apply a high-resolution correction based on the residual error. Comprehensive validation across diverse fluvial and pluvial flood scenarios demonstrates that the MFGNN framework significantly reduces prediction error compared to a standard GNN trained with an equivalent computational budget. This computationally efficient, novel framework makes development of accurate, high-resolution surrogate models for large floodplains feasible by lowering the data-generation barrier.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106654"},"PeriodicalIF":4.6,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902439","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

Application of machine learning in modeling the quality of acoustic environments: A review 机器学习在声环境质量建模中的应用综述

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-08-23 DOI: 10.1016/j.envsoft.2025.106658

Enyu Tong , Yiming Chen , Yu Bai , Fengying Zhang , Thomas Krafft

引用次数: 0

Validation of the InVEST nutrient retention model across Europe with attribution of model errors 整个欧洲的InVEST营养保留模型的验证与模型错误的归因

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-08-20 DOI: 10.1016/j.envsoft.2025.106657

Danny A.P. Hooftman , Guy Ziv , Paul M. Evans , James M. Bullock

{"title":"Validation of the InVEST nutrient retention model across Europe with attribution of model errors","authors":"Danny A.P. Hooftman , Guy Ziv , Paul M. Evans , James M. Bullock","doi":"10.1016/j.envsoft.2025.106657","DOIUrl":"10.1016/j.envsoft.2025.106657","url":null,"abstract":"<div><div>Intensive fertilisation of farmland leads to substantial nutrient escape into the environment, polluting land, water, and the atmosphere. We used the InVEST NDR model to investigate nitrogen (N) and phosphorus (P) run-off and retention across the European continent at 25 × 25 m resolution, and validated outputs against empirical measurements at 2251 river locations. Mean nutrient retention across Europe was estimated as 93 % for N and 92 % for P, through accumulation by standing vegetation and the soil. Modelled nutrient export to streams matched well to empirical measurements. Model-based uncertainties were related to seasonality, the balance between surface and sub-surface flows, and extremes in slope and rainfall. Uncertainties related to empirical data suggested enhancements to monitoring programmes that would improve nutrient export and erosion modelling, which included higher resolution fertiliser and manure data, differentiation of grassland types, including stocking density categories, and in-river nutrient measurements at low flow.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106657"},"PeriodicalIF":4.6,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890537","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

Evaluation of daily stream temperature predictions (1979–2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm 使用时空感知机器学习算法评估美国相邻地区的日流温度预测（1979-2021）

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-08-19 DOI: 10.1016/j.envsoft.2025.106655

Jeremy Diaz , Samantha Oliver , Galen Gorski

{"title":"Evaluation of daily stream temperature predictions (1979–2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm","authors":"Jeremy Diaz , Samantha Oliver , Galen Gorski","doi":"10.1016/j.envsoft.2025.106655","DOIUrl":"10.1016/j.envsoft.2025.106655","url":null,"abstract":"<div><div>Stream temperature controls a variety of physical and biological processes that affect ecosystems, human health, and economic activities. We used 42 years (1979–2021) of data to predict daily summary statistics of stream temperature across >50,000 stream reaches in the contiguous United States using a recurrent graph convolution network. We comprehensively documented the performance – both across all reaches and by stream type (e.g., reservoir or groundwater influence) – as a baseline for future improvement. The model showed reach-level RMSE of <2 °C with 90 % prediction intervals that contain 90.7 % of observations. We also assessed how the model captured variability in ecologically relevant metrics (e.g., R<sup>2</sup> for annual 7-day maximum = 0.76; R<sup>2</sup> for days exceeding 25 °C = 0.75). This model does not outperform state-of-the-art machine learning efforts (e.g., RMSE ≤1.5 °C) due to a limited input set but does provide the most spatially complete modeling to date to support water availability assessments.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106655"},"PeriodicalIF":4.6,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892113","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

Daily runoff simulation in humid regions using the entropy-weighted ensemble learning models 基于熵加权集合学习模型的湿润地区日径流模拟

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-08-18 DOI: 10.1016/j.envsoft.2025.106653

Zhenyu Tang , Liping Zhang , Chen Hu , Yaze Li , Gangsheng Wang , Zhiling Zhou , Xiao Li , Zhengfeng Bao , Hui Cao , Benjun Jia

{"title":"Daily runoff simulation in humid regions using the entropy-weighted ensemble learning models","authors":"Zhenyu Tang , Liping Zhang , Chen Hu , Yaze Li , Gangsheng Wang , Zhiling Zhou , Xiao Li , Zhengfeng Bao , Hui Cao , Benjun Jia","doi":"10.1016/j.envsoft.2025.106653","DOIUrl":"10.1016/j.envsoft.2025.106653","url":null,"abstract":"<div><div>Traditional hydrological models struggle to meet the accuracy requirements for runoff simulation under climate change and anthropogenic interventions. To address this limitation, we propose ensemble learning models (ELMs) that integrate optimal process-driven and data-driven models for daily runoff simulation in two typical humid basins in China: the Xiangjiang River Basin (XJRB) and Minjiang River Basin (MJRB). Model performance is evaluated by a newly developed comprehensive index <em>CI</em> based on entropy weight method. Our results reveal that the Xin'anjiang model outperforms other process-driven models with NSE values of 0.795 (XJRB) and 0.765 (MJRB), while the Long Short-Term Memory model outperforms other data-driven models (NSE: 0.945 and 0.955, respectively). Furthermore, hybrid ELMs surpass all single models, reducing MAE and RMSE by 15 % and 21 % in XJRB, and improving the NSE by 0.157 in MJRB. This framework enhances simulation accuracy and operational robustness, demonstrating strong potential for flood risk mitigation.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106653"},"PeriodicalIF":4.6,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863458","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