Environmental Modelling & Software最新文献_第5页

Machine learning assisted predictive urban digital twin for intelligent monitoring of air quality index for smart city environment 机器学习辅助预测城市数字孪生，实现智慧城市环境空气质量指标的智能监测

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-06-04 DOI: 10.1016/j.envsoft.2025.106559

Khazina Naveed , Tariq Umer , Aamer Bilal Asghar , Muhammad Aslam , Krzysztof Ejsmont , Ahmed Sayed Mohammed Metwally , Kien Nguyen Thanh

{"title":"Machine learning assisted predictive urban digital twin for intelligent monitoring of air quality index for smart city environment","authors":"Khazina Naveed , Tariq Umer , Aamer Bilal Asghar , Muhammad Aslam , Krzysztof Ejsmont , Ahmed Sayed Mohammed Metwally , Kien Nguyen Thanh","doi":"10.1016/j.envsoft.2025.106559","DOIUrl":"10.1016/j.envsoft.2025.106559","url":null,"abstract":"<div><div>Environmental factors such as urban air pollutants have detrimental effects on human health. In this research a digital twin (DT) based innovative strategy is presented for accurately forecasting Air Quality Index (AQI) in smart city environment. The historic data of Delhi city is collected, and six different deep learning algorithms are implemented to forecast AQI. The 3D model of the smart city is developed in the Blender, and its urban DT is developed in Microsoft Azure. The InfluxDB database is used for storage and retrieval of time-series data. The experimental results show that the CNN-1D-2 layer model outperforms all other algorithms with MAPE of 0.01231, MSLE of 0.00036, R<sup>2</sup> score reaching 0.99951, and model accuracy of 97.950647. The 3D urban DT model highlights the polluted areas with different colors based on AQI thresholds and DT Grafana dashboard displays the graphical values of AQI and different pollutants along with their trends.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106559"},"PeriodicalIF":4.8,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271412","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

Why is arid zone hydrology a scientific desert - and how does the hydrological community move forward? 为什么干旱区水文学是科学上的沙漠？水文界如何向前发展？

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-06-04 DOI: 10.1016/j.envsoft.2025.106561

Howard Wheater

{"title":"Why is arid zone hydrology a scientific desert - and how does the hydrological community move forward?","authors":"Howard Wheater","doi":"10.1016/j.envsoft.2025.106561","DOIUrl":"10.1016/j.envsoft.2025.106561","url":null,"abstract":"<div><div>While the world's hot arid climates are, by definition, water stressed, many of these regions face existential challenges as population growth, economic development and climate change exacerbate both the natural water scarcity and the risks from extreme flood events. A strong science base and effective modelling tools are needed for effective water management but hydrological processes in these regions remain poorly understood and quantified. This personal reflection aims to answer the key questions: What are the challenges for hydrological science in hot desert zones, why have they arisen, and how should the hydrological community move forward to address them?</div><div>There remains a critical need for: a) high quality observational basins across the arid regions to provide consistent long-term data and a platform for detailed experimental work to elucidate critical processes, b) improved quantification of spatial rainfall, infiltration into desert soils and groundwater recharge from ephemeral flows, c) integrated assessment of stream-aquifer interactions, and d) modelling tools that recognize uncertainty in process representation and parameterization. We note the potential for remote sensing to provide new insights, particularly in data-scarce areas, and stress the importance of traditional knowledge and stakeholder engagement in water management for these highly stressed regions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106561"},"PeriodicalIF":4.8,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222593","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

FPQ-iTransformer: A novel iTransformer-based model for accurate seawater salinity prediction FPQ-iTransformer：一种新的基于itransformer的精确海水盐度预测模型

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-06-04 DOI: 10.1016/j.envsoft.2025.106529

Wanhai Jia, Jing Sun, Shaopeng Guan

{"title":"FPQ-iTransformer: A novel iTransformer-based model for accurate seawater salinity prediction","authors":"Wanhai Jia, Jing Sun, Shaopeng Guan","doi":"10.1016/j.envsoft.2025.106529","DOIUrl":"10.1016/j.envsoft.2025.106529","url":null,"abstract":"<div><div>Accurate seawater salinity prediction is crucial for marine ecosystem management yet challenged by climate-driven dynamic fluctuations. We develop an enhanced iTransformer model FPQ-iTransformer incorporating three novel components: ProbSparse self-attention in the encoder for efficient temporal pattern extraction, a hybrid decoder combining multivariate attention with sparsity mechanisms for spatiotemporal dependency capture, and quantile loss integration for noise-robust prediction. Validated on British Columbia’s mariculture monitoring data, our framework demonstrates superior performance over eight state-of-the-art models (CNN, LSTM, Transformer, etc.), achieving 12.7% MAE reduction and 15.4% <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> improvement compared to the best baseline. The model’s prediction accuracy shows increasing advantages with extended time horizons, particularly in capturing abrupt salinity variations caused by extreme weather events. This advancement provides a reliable computational tool for real-time aquaculture decision-making and coastal environmental protection, with potential applicability to other hydrodynamic parameter predictions in climate-sensitive regions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106529"},"PeriodicalIF":4.8,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213172","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

Towards simulating solute transport in complex, regional-scale fracture networks: a rapid upscaled approach 在复杂的区域尺度裂缝网络中模拟溶质运移：一种快速升级的方法

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-06-03 DOI: 10.1016/j.envsoft.2025.106555

Mohamed Khafagy , Sarah Dickson-Anderson , Wael El-Dakhakhni

{"title":"Towards simulating solute transport in complex, regional-scale fracture networks: a rapid upscaled approach","authors":"Mohamed Khafagy , Sarah Dickson-Anderson , Wael El-Dakhakhni","doi":"10.1016/j.envsoft.2025.106555","DOIUrl":"10.1016/j.envsoft.2025.106555","url":null,"abstract":"<div><div>Currently, the most common approaches for simulating solute transport in fractured aquifers are the single- or dual-continuum and the discrete fracture network (DFN) methods. However, continuum approaches often lack accuracy due to averaging, whereas DFN approaches may be computationally prohibitive for large-scale fracture networks. To address these challenges, this study presents an Upscaled Fracture Network (UFN) model, developed by discretizing complex fracture networks into elementary volumes, identifying solute transport flow channels and calculating breakthrough curves within an elementary volume. The identified flow channels within the micro-scale DFN are collectively employed to construct the residence time at the macro-scale DFN. Validated against a random walk particle tracking (RWPT) DFN-based approach, the UFN model accurately captures solute transport processes in saturated fracture networks at the macro scale, and represents a significant advancement in simulating solute transport in complex, regional-scale aquifers due to its computational efficiency, simple implementation, and high level of accuracy.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106555"},"PeriodicalIF":4.8,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242951","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

Quantitative framework for soil burn severity from numerical wildfire models 野火数值模型中土壤烧伤严重程度的定量框架

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-31 DOI: 10.1016/j.envsoft.2025.106552

Hamid Vahdat-Aboueshagh, Sean A. McKenna

引用次数: 0

Estimating soil redistribution rates with the RadEro model: A physically based compartmental model for 137Cs analysis in an R package 用RadEro模型估计土壤再分配率：R包中用于137Cs分析的基于物理的区隔模型

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-30 DOI: 10.1016/j.envsoft.2025.106551

Arturo Catalá, Borja Latorre, Leticia Gaspar, Ana Navas

{"title":"Estimating soil redistribution rates with the RadEro model: A physically based compartmental model for 137Cs analysis in an R package","authors":"Arturo Catalá, Borja Latorre, Leticia Gaspar, Ana Navas","doi":"10.1016/j.envsoft.2025.106551","DOIUrl":"10.1016/j.envsoft.2025.106551","url":null,"abstract":"<div><div>Quantifying soil redistribution rates cannot be fully addressed by conventional methodologies. Fallout radionuclides (FRNs), particularly <sup>137</sup>Cs, serve as reliable tracers of soil movement. Numerous models have emerged to estimate soil erosion rates using FRNs, though a versatile, physically-based <sup>137</sup>Cs model remains absent. We present RadEro, a model designed to estimate soil redistribution rates from <sup>137</sup>Cs inventories in both ploughed and undisturbed soils, using sectioned or bulk soil profiles based on the mass balance model by Soto and Navas (2004, 2008). The model is built within an R package that offers clear plotting and presentation of results while maintaining a user-friendly format and open-source code. RadEro model accounts for annual <sup>137</sup>Cs fallout, radioactive decay, stoniness through fine fraction density and effective volume of <sup>137</sup>Cs and vertical diffusion processes. The RadEro model, validated through sectioned and bulk soil profiles, demonstrates high reliability in simulating experimental inventories, enabling effective land management decisions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106551"},"PeriodicalIF":4.8,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144194030","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

Prediction of maximum scour depth downstream of bed sills using integrated machine learning algorithms 利用集成机器学习算法预测河床下游最大冲刷深度

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-29 DOI: 10.1016/j.envsoft.2025.106548

Dawei Guan , Zhanchen Li , Haoran Zheng , Jian-Hao Hong , Tiago Fazeres-Ferradosa , Richard Asumadu

{"title":"Prediction of maximum scour depth downstream of bed sills using integrated machine learning algorithms","authors":"Dawei Guan , Zhanchen Li , Haoran Zheng , Jian-Hao Hong , Tiago Fazeres-Ferradosa , Richard Asumadu","doi":"10.1016/j.envsoft.2025.106548","DOIUrl":"10.1016/j.envsoft.2025.106548","url":null,"abstract":"<div><div>Accurate prediction of maximum relative scour depth (<em>y</em><sub><em>s</em></sub>/<em>H</em><sub><em>s</em></sub>) is critical for hydraulic infrastructure resilience. This study advances scour depth prediction downstream of bed sills by establishing three ensemble models—Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and XGBoost—trained on a comprehensive dataset combining 328 standardized flume experiments (clear-water and live-bed conditions) with 73 field measurements. Validation using 29 field datasets from Maso River reveals MAE reductions of 32.5 %, 28.7 %, and 30.2 % for RF, GBDT, and XGBoost, respectively, compared to laboratory-trained models, translating to at least 31.6 % higher accuracy than traditional empirical approaches. Comprehensive sensitivity analysis identifies four dimensionless parameters as critical predictors, ranked by their relative importance to scour development: morphological transition coefficient (a<sub>1</sub>/H<sub>s</sub>) > sediment sorting coefficient (a<sub>1</sub>/ΔD<sub>95</sub>) > weir spacing ratio (L/H<sub>s</sub>) > channel slope (S<sub>0</sub>). By integrating lab and field data, this approach enhances scour prediction accuracy for fluvial risk management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106548"},"PeriodicalIF":4.8,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144194029","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 systematic review of machine learning in groundwater monitoring 地下水监测中机器学习的系统综述

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-29 DOI: 10.1016/j.envsoft.2025.106549

Mrunmayee Dhapre , Shrikant Jadhav , Debanjana Das , Jehanzeb Khan , Youngsoo Kim , Sen Chiao , Thomas Danielson

{"title":"A systematic review of machine learning in groundwater monitoring","authors":"Mrunmayee Dhapre , Shrikant Jadhav , Debanjana Das , Jehanzeb Khan , Youngsoo Kim , Sen Chiao , Thomas Danielson","doi":"10.1016/j.envsoft.2025.106549","DOIUrl":"10.1016/j.envsoft.2025.106549","url":null,"abstract":"<div><div>With increasing concerns about water scarcity, groundwater has become crucial since this resource provides most of the freshwater needs. However, various human and natural activities often contaminate the groundwater, making it unsuitable for use. Over the years, scientists and engineers have used many methods to predict and track groundwater contamination as part of environmental monitoring. Consequently, there is an urgent need for improved methods, particularly in the face of increasing contamination. Machine learning has sometimes been used to monitor groundwater, air quality, and climate. Traditional methods must be improved due to the complexity and large amount of environmental data. This includes using hybrid models that combine traditional and new techniques. Despite the use of machine learning in many scientific areas, there is a lack of comprehensive reviews focusing on its use in environmental monitoring, especially groundwater monitoring. We aim to fill this gap by exploring machine-learning applications in groundwater monitoring. We discuss relevant methods, their limitations, and future potential. We summarize research on automating data processing and model training using groundwater sensor data. Our research underscores the transformative potential of machine learning to revolutionize long-term groundwater monitoring and contamination detection, providing valuable insights for future research and practical applications.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106549"},"PeriodicalIF":4.8,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144194035","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

How to use general AI for task-specific applications: A case study of monitoring water level trends with river cameras 如何将通用人工智能用于特定任务应用：用河流摄像机监测水位趋势的案例研究

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-28 DOI: 10.1016/j.envsoft.2025.106550

Ze Wang , Heng Lyu , Yanqing Guo , Shun'an Zhou , Chi Zhang

{"title":"How to use general AI for task-specific applications: A case study of monitoring water level trends with river cameras","authors":"Ze Wang , Heng Lyu , Yanqing Guo , Shun'an Zhou , Chi Zhang","doi":"10.1016/j.envsoft.2025.106550","DOIUrl":"10.1016/j.envsoft.2025.106550","url":null,"abstract":"<div><div>Water level variations influence geochemical and hydrological processes within river networks. Water segmentation from river camera images using deep learning supports water level trend monitoring, but domain-specific model accuracies are constrained by limited annotated data. To improve accuracy, this study proposes a framework combining domain-specific models with General AI. The framework uses the Segment Anything Model (SAM) as backbone, with a pre-trained ResUnet model identifying highest-probability water pixels as a prompt to SAM, without any requirement for human intervention or local annotation. Applied to river camera images from Tewkesbury, UK, the framework increased the Intersection over Union (<em>IoU</em>) by over 15 % compared to the single ResUnet. <em>Point prompt</em> was identified as the optimal mode for feeding water-related prior knowledge to SAM. The static observer flooding index derived from segmented masks showed a strong correlation (0.90) with real water level, surpassing the ResUnet's 0.54. Our framework allows for the supplementation of river monitoring networks with camera gauges, providing robust water level trend observations.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106550"},"PeriodicalIF":4.8,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144189609","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

Predicting climate change: A comparative analysis of time series models for CO2 concentrations and temperature anomalies 预测气候变化：CO2浓度和温度异常的时间序列模式的比较分析

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-05-26 DOI: 10.1016/j.envsoft.2025.106533

Zahra Rezaei , Sara Safi Samghabadi , Mohammad Amin Amini , Dingjing Shi , Yaser Mike Banad

{"title":"Predicting climate change: A comparative analysis of time series models for CO2 concentrations and temperature anomalies","authors":"Zahra Rezaei , Sara Safi Samghabadi , Mohammad Amin Amini , Dingjing Shi , Yaser Mike Banad","doi":"10.1016/j.envsoft.2025.106533","DOIUrl":"10.1016/j.envsoft.2025.106533","url":null,"abstract":"<div><div>This study presents a novel, integrated modeling framework that combines machine learning (ML) techniques with physics-based approaches to forecast both CO<sub>2</sub> emissions and global temperature anomalies. Unlike prior research that typically addresses these components in isolation, this work concurrently applies and compares five advanced ML models—Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), Convolutional Neural Network (CNN), Facebook Prophet, and a hybrid CNN-LSTM—alongside two physics-based models: a zero-dimensional Energy Balance Model (EBM) and a simplified General Circulation Model (GCM) adapted from NASA's GISS framework.Using monthly global datasets from January 2000 to April 2024, obtained from the National Oceanic and Atmospheric Administration (NOAA) and the Scripps Institution of Oceanography, the models are evaluated based on predictive accuracy (RMSE, MSE, MAE, R<sup>2</sup>), scalability, and interpretability. Prophet demonstrated the highest accuracy for CO<sub>2</sub> emission forecasting (RMSE = 0.035), while LSTM achieved the best performance in temperature anomaly prediction (RMSE = 0.086). Physics-based models provided interpretable and computationally efficient long-term projections but lacked short-term flexibility.To facilitate reproducibility and practical application, we developed ClimateChange-ML, an open-source software package that implements all proposed models, includes trained weights, and provides full documentation and visualization tools.The novelty of this work lies in its dual-modeling strategy and comprehensive comparative evaluation, highlighting the complementary strengths of data-driven and physically grounded methods. This integrated approach offers a more holistic framework for climate forecasting across multiple temporal scales, providing valuable insights for both scientific understanding and climate policy planning.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106533"},"PeriodicalIF":4.8,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170527","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