Environmental Modelling & Software最新文献

Integrated modeling of the generation, attenuation, and transport of point-source pollutants at the watershed-scale using SWAT+ 利用SWAT+对流域尺度点源污染物的产生、衰减和运移进行综合模拟

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-26 DOI: 10.1016/j.envsoft.2025.106631

Joan Saló-Grau , Laia Estrada , Oliu Llorente , Wolfgang Gernjak , Xavier Garcia , Natalja Čerkasova , Jeffrey G. Arnold , Vicenç Acuña

{"title":"Integrated modeling of the generation, attenuation, and transport of point-source pollutants at the watershed-scale using SWAT+","authors":"Joan Saló-Grau , Laia Estrada , Oliu Llorente , Wolfgang Gernjak , Xavier Garcia , Natalja Čerkasova , Jeffrey G. Arnold , Vicenç Acuña","doi":"10.1016/j.envsoft.2025.106631","DOIUrl":"10.1016/j.envsoft.2025.106631","url":null,"abstract":"<div><div>Addressing the chemical pollution of surface water bodies requires a good understanding of pollutant and system dynamics. Numerical modeling represents a valuable tool to support sustainable water management, especially due to its capacity to explore climate and management scenarios. We introduce a novel integrated modeling framework to capture the dynamics of point-source pollutants at the watershed-scale. It comprises a pollution generation model, an attenuation and transport model built within SWAT+, and a custom Python library (pySWATPlus) to facilitate model integration and calibration. We tested the framework for ciprofloxacin and venlafaxine in three densely populated Mediterranean basins, and we successfully calibrated it using observed concentrations in the river network. This research advances water quality modeling by integrating point-source pollutant dynamics from source to in-stream fate with the widely used model SWAT+. It also offers a valuable tool for evaluating mitigation strategies, supporting compliance with regulations, and informing sustainable water management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106631"},"PeriodicalIF":4.6,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725120","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

Hybrid-GR4J: A hybrid hydrological model integrating GR4J and deep learning hybrid -GR4J：结合GR4J和深度学习的混合水文模型

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-25 DOI: 10.1016/j.envsoft.2025.106636

Mingbo Sun , Baowei Yan , Xuerui Zhou , Jianbo Chang , Shixiong Du

引用次数: 0

HydroServer: A software stack supporting collection, communication, storage, management, and sharing of data from in situ environmental sensors HydroServer：一种支持收集、通信、存储、管理和共享现场环境传感器数据的软件堆栈

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-24 DOI: 10.1016/j.envsoft.2025.106637

Jeffery S. Horsburgh , Kenneth Lippold , Daniel L. Slaugh , Maurier Ramirez

{"title":"HydroServer: A software stack supporting collection, communication, storage, management, and sharing of data from in situ environmental sensors","authors":"Jeffery S. Horsburgh , Kenneth Lippold , Daniel L. Slaugh , Maurier Ramirez","doi":"10.1016/j.envsoft.2025.106637","DOIUrl":"10.1016/j.envsoft.2025.106637","url":null,"abstract":"<div><div>Scientists and practitioners who collect data using in situ environmental sensors need effective software for managing and sharing the data they produce. The HydroServer software stack was designed as a standards-based system that enables collection, storage, management, and sharing of environmental sensor data. Originally part of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS), the HydroServer software has been modernized to use the Open Geospatial Consortium's (OGC) SensorThings application programming interface (API) and data model standard to better enable flexible and standardized ingestion of data from multiple sensors and sources, along with query and retrieval of hosted data for a variety of different users and use cases. The HydroServer software is open source with deployment instructions targeted for cloud deployments, which makes it accessible and useable by both researchers and practitioners.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106637"},"PeriodicalIF":4.6,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722970","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 differential evolution optimized hybrid XGBoost for accurate carbon emission prediction 差分进化优化混合动力XGBoost精确碳排放预测

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-23 DOI: 10.1016/j.envsoft.2025.106627

Lingfei Zhang , Gang Lu , Xiaoqing Yan , Peng Xia , Zhong Chen , Di Wu

{"title":"A differential evolution optimized hybrid XGBoost for accurate carbon emission prediction","authors":"Lingfei Zhang , Gang Lu , Xiaoqing Yan , Peng Xia , Zhong Chen , Di Wu","doi":"10.1016/j.envsoft.2025.106627","DOIUrl":"10.1016/j.envsoft.2025.106627","url":null,"abstract":"<div><div>Predicting carbon emissions is essential for combating climate change and supporting green development. Carbon emissions are influenced by complex factors, such as economy, population and new energy generation. Traditional methods struggle with these uncertainties, while machine learning offers data-driven solutions. However, some models lack data selection strategies, resulting in the neglect of critical features. To tackle this issue, this paper proposes a Differential Evolution Optimized Hybrid XGBoost (DEOH-XGBoost) approach. DEOH-XGBoost includes three main components: feature engineering, model construction, and model integration. First, in each correlation analysis, features are selected through fuzzy membership functions. Second, XGBoost-based models are constructed on each feature set to predict separately. Third, the models are integrated by a differential evolution optimized weighting strategy. As such, DEOH-XGBoost effectively uncovers the intrinsic connections between multi-type data to achieve accurate carbon emission prediction. Extensive experiments demonstrate that our DEOH-XGBoost has significantly better prediction accuracy than related state-of-the-art methods. Our source code and datasets can be found at the following link: <span><span>https://github.com/lingfei0804/DEOHXGBOOST</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106627"},"PeriodicalIF":4.8,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713991","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 end-to-end pipeline based on a Two-Dimensional Convolutional Neural Network for monitoring desertification in Africa using remote sensing images 基于二维卷积神经网络的非洲沙漠化遥感监测端到端管道

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-23 DOI: 10.1016/j.envsoft.2025.106622

Farah Chouikhi , Ali Ben Abbes , Imed Riadh Farah

{"title":"An end-to-end pipeline based on a Two-Dimensional Convolutional Neural Network for monitoring desertification in Africa using remote sensing images","authors":"Farah Chouikhi , Ali Ben Abbes , Imed Riadh Farah","doi":"10.1016/j.envsoft.2025.106622","DOIUrl":"10.1016/j.envsoft.2025.106622","url":null,"abstract":"<div><div>Desertification is a major environmental challenge in Africa, influenced by climate change, deforestation, and unsustainable land use. Effective monitoring is crucial for sustainable land management. This paper presents an end-to-end pipeline based on a Two-Dimensional Convolutional Neural Network (2D-CNN), achieving a classification accuracy of over 91% across a dataset derived from MODIS imagery collected over Africa between 2015 and 2023. The pipeline encompasses data acquisition, preprocessing, model development, evaluation, and prediction, facilitating large-scale desertification sensitivity analysis. The model’s performance was rigorously assessed using multiple metrics, including precision (90%), recall (89%), F1-score (89.5%), balanced accuracy (74.32%), and Matthews Correlation Coefficient (MCC) (0.86). Our proposed 2D-CNN consistently outperforms traditional machine learning models, including Random Forest (RF), XGBoost, Recurrent Neural Network (RNN), and Variational Autoencoder (VAE), demonstrating superior classification performance. The analysis reveals significant desertification expansion in the Sahel and Southern Africa regions, emphasizing the urgency for proactive intervention strategies.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106622"},"PeriodicalIF":4.8,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703879","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

Machine learning-based methods for dust concentration distribution prediction by utilizing back scattering images 基于机器学习的后向散射图像粉尘浓度分布预测方法

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-23 DOI: 10.1016/j.envsoft.2025.106620

Xutong Liu , Xuhui Kong , Wufan Xuan , Jialin Li , Andrew Nyakundi , Yuxuan Zhang , Lina Zheng , Fubao Zhou

{"title":"Machine learning-based methods for dust concentration distribution prediction by utilizing back scattering images","authors":"Xutong Liu , Xuhui Kong , Wufan Xuan , Jialin Li , Andrew Nyakundi , Yuxuan Zhang , Lina Zheng , Fubao Zhou","doi":"10.1016/j.envsoft.2025.106620","DOIUrl":"10.1016/j.envsoft.2025.106620","url":null,"abstract":"<div><div>Dust is a significant environmental pollutant that poses serious risks to human health, so its proper monitoring is quite significant. However, traditional light scattering single-point measurements are limited to reflecting the dust concentration distribution in a large-scale environment. In this paper, we introduced the light intensity distance and obtained a large-scale dust concentration distribution prediction model by analyzing the relationship between dust concentration and the attenuation of light intensity caused by dust scattering and absorption in the optical path. Fifteen classical machine learning algorithms were applied, which proved the importance of light intensity distance in predicting dust concentration. When only fitting the relationship between light intensity and concentration, each algorithm gave the result with R2 of about 0.9000, while Kolmogorov–Arnold Networks (KAN) had the best prediction result 0.9076. When light intensity distance was added, the prediction accuracy of each algorithm was improved correspondingly. Light Gradient Boosting Machine (LightGBM) methods have the best performance (R2: 0.9500), and KAN followed (R2: 0.9472). We added another set of <span><math><msub><mrow><mi>SiO</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> experiments and sensitivity analyses for light intensity distance to demonstrate the applicability of the model. Finally, LightGBM was used to predict the dust concentration distribution in the whole process.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106620"},"PeriodicalIF":4.6,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722971","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 coupling approach for long-term 3D morphological evolution of sandy coasts under sea-level rise 海平面上升下沙质海岸长期三维形态演化的耦合方法

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-23 DOI: 10.1016/j.envsoft.2025.106624

Mohammad Traboulsi , Déborah Idier , Bruno Castelle , Arthur Robinet , Vincent Marieu , Elsa Durand , R. Jak McCarroll

{"title":"A coupling approach for long-term 3D morphological evolution of sandy coasts under sea-level rise","authors":"Mohammad Traboulsi , Déborah Idier , Bruno Castelle , Arthur Robinet , Vincent Marieu , Elsa Durand , R. Jak McCarroll","doi":"10.1016/j.envsoft.2025.106624","DOIUrl":"10.1016/j.envsoft.2025.106624","url":null,"abstract":"<div><div>This study presents a modular modeling framework for simulating medium- to long-term (decadal to centennial) coastal evolution, focusing on shoreface translation under the combined effects of sea-level rise (SLR) and waves. On these timescales, short-term storm-driven processes are treated as noise superimposed on longer-term trends. We couple the one-dimensional ShoreTrans model, which simulates SLR-driven profile adjustment, with the two-dimensional reduced-complexity model LX-Shore, which captures longshore sediment transport gradients. This coupling enables efficient simulation of three-dimensional morphological change across diverse sandy coastal settings, including environments with dunes, barriers, and hard structures. The framework is first applied to synthetic test cases to explore sensitivity to coupling strategies, then tested on a 5-km beach-dune system in southwest France fronted by a 1.2-km seawall. Results show reasonable agreement with observed shoreline evolution and demonstrate the value of the coupled approach in capturing morphodynamic feedbacks and trajectory shifts not reproduced when shoreline and shoreface processes are modeled independently.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106624"},"PeriodicalIF":4.8,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703880","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

Bayesian calibration of a 2D hydraulic model using a convolutional neural network emulator 基于卷积神经网络仿真器的二维水工模型贝叶斯校正

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-23 DOI: 10.1016/j.envsoft.2025.106621

Jose Zevallos , Eduardo Chávarri-Velarde , Ronald R. Gutierrez , Waldo Lavado-Casimiro

{"title":"Bayesian calibration of a 2D hydraulic model using a convolutional neural network emulator","authors":"Jose Zevallos , Eduardo Chávarri-Velarde , Ronald R. Gutierrez , Waldo Lavado-Casimiro","doi":"10.1016/j.envsoft.2025.106621","DOIUrl":"10.1016/j.envsoft.2025.106621","url":null,"abstract":"<div><div>This study presents a Bayesian calibration framework for 2D hydraulic models using convolutional neural networks (CNNs) as surrogate emulators of TELEMAC-2D. Applied to the Lower Piura River Basin in northern Peru, the method estimates spatially distributed Manning’s roughness coefficients while accounting for structural model error. A CNN trained on a simulation ensemble predicts flood depth under varying roughness scenarios, enabling substantial computational savings. The emulator is embedded in a Bayesian inference scheme with a Gaussian Process discrepancy model to capture systematic deviations. Validation with synthetic scenarios demonstrates accurate roughness retrieval in hydraulically sensitive areas. Additionally, a real-case validation was performed using PeruSAT-1, a high-resolution Earth observation satellite operated by the Peruvian Space Agency (CONIDA), acquired during the 04/10/2017 flood. This confirmed the framework’s ability to reproduce observed depth patterns under data scarcity. The method provides a scalable solution for parameter inference in flood-prone regions where conventional validation approaches remain limited.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106621"},"PeriodicalIF":4.8,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703881","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 review of economic calibrated mathematical programming models for agricultural water reallocation 农业用水再分配的经济校准数学规划模型综述

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-19 DOI: 10.1016/j.envsoft.2025.106628

Giammauro Soriano , Francesco Sapino , C. Dionisio Pérez-Blanco

引用次数: 0

A novel salient object detection network for burned area segmentation in high-resolution remote sensing images 一种新的高分辨率遥感图像烧伤区域分割显著目标检测网络

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-19 DOI: 10.1016/j.envsoft.2025.106629

Yuxiang Fu, Wei Fang

{"title":"A novel salient object detection network for burned area segmentation in high-resolution remote sensing images","authors":"Yuxiang Fu, Wei Fang","doi":"10.1016/j.envsoft.2025.106629","DOIUrl":"10.1016/j.envsoft.2025.106629","url":null,"abstract":"<div><div>Burned area segmentation (BAS) in remote sensing images (RSIs) is critical for forest fire monitoring, as it helps locate and extract damaged areas, providing a scientific basis for post-disaster recovery. However, existing BAS methods underperform on high-resolution RSIs due to diluted location information and blurred edges during sampling. To this, we propose PANet, a novel salient object detection (SOD) network designed for BAS in high-resolution RSIs. PANet introduces two key modules: Path Aggregation Decoder (PAD) and Progressive Multi-level Aggregation Predictor (PMAP). PAD integrates multi-level features for richer semantics, using detail feature flow to enhance edge quality and refined location feature flow to improve spatial accuracy. PMAP progressively fuses features from PAD to predict saliency maps, leveraging higher-level features to complement lower-level ones. We also constructed a new dataset for high-resolution BAS. Experiments on two BAS datasets show that PANet outperforms state-of-the-art methods. Code is available at: <span><span>https://github.com/Voruarn/PANet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106629"},"PeriodicalIF":4.8,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664916","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