Air pollution poses a significant global health hazard. Effective monitoring and predicting air pollutant concentrations are crucial for managing associated health risks. Recent advancements in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), offer the potential for more precise air pollution monitoring and forecasting models. This comprehensive review, conducted according to PRISMA guidelines, analyzed 65 high-quality Q1 journal articles to uncover current trends, challenges, and future AI applications in this field. The review revealed a significant increase in research papers utilizing ML and DL approaches from 2021 onwards. ML techniques currently dominate, with Random Forest being the most frequent method, achieving up to 98.2% accuracy. DL techniques show promise in capturing complex spatiotemporal relationships in air quality data. The study highlighted the importance of integrating diverse data sources to improve model accuracy. Future research should focus on addressing challenges in model interpretability and uncertainty quantification.
{"title":"Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review","authors":"Sreeni Chadalavada , Oliver Faust , Massimo Salvi , Silvia Seoni , Nawin Raj , U. Raghavendra , Anjan Gudigar , Prabal Datta Barua , Filippo Molinari , Rajendra Acharya","doi":"10.1016/j.envsoft.2024.106312","DOIUrl":"10.1016/j.envsoft.2024.106312","url":null,"abstract":"<div><div>Air pollution poses a significant global health hazard. Effective monitoring and predicting air pollutant concentrations are crucial for managing associated health risks. Recent advancements in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), offer the potential for more precise air pollution monitoring and forecasting models. This comprehensive review, conducted according to PRISMA guidelines, analyzed 65 high-quality Q1 journal articles to uncover current trends, challenges, and future AI applications in this field. The review revealed a significant increase in research papers utilizing ML and DL approaches from 2021 onwards. ML techniques currently dominate, with Random Forest being the most frequent method, achieving up to 98.2% accuracy. DL techniques show promise in capturing complex spatiotemporal relationships in air quality data. The study highlighted the importance of integrating diverse data sources to improve model accuracy. Future research should focus on addressing challenges in model interpretability and uncertainty quantification.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106312"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905652","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}
Pub Date : 2025-02-01DOI: 10.1016/j.envsoft.2024.106303
Samuel J. Harris, N.R. McDonald
A two-dimensional model of wildfire spread and merger is presented. Three features affect the wildfire propagation: (i) a constant basic rate of spread term accounting for radiative and convective heat transfer, (ii) the unidirectional, constant ambient wind, and (iii) a fire-induced pyrogenic wind. Two numerical methods are proposed to solve for the pyrogenic potential. The first utilises the conformal invariance of Laplace’s equation, reducing the wildfire system to a single Polubarinova–Galin type equation. The second method uses a AAA-least squares method to find a rational approximation of the pyrogenic potential. Various wildfire scenarios are presented and the effects of the pyrogenic wind and the radiative/convective basic rate of spread terms investigated. Firebreaks such as roads and lakes are also included and solutions are found to match well with existing numerical and experimental results. The methods proposed in this work are suitably fast and new to the field of wildfire modelling.
{"title":"Modelling wildfire spread and spotfire merger using conformal mapping and AAA-least squares methods","authors":"Samuel J. Harris, N.R. McDonald","doi":"10.1016/j.envsoft.2024.106303","DOIUrl":"10.1016/j.envsoft.2024.106303","url":null,"abstract":"<div><div>A two-dimensional model of wildfire spread and merger is presented. Three features affect the wildfire propagation: (i) a constant basic rate of spread term accounting for radiative and convective heat transfer, (ii) the unidirectional, constant ambient wind, and (iii) a fire-induced pyrogenic wind. Two numerical methods are proposed to solve for the pyrogenic potential. The first utilises the conformal invariance of Laplace’s equation, reducing the wildfire system to a single Polubarinova–Galin type equation. The second method uses a AAA-least squares method to find a rational approximation of the pyrogenic potential. Various wildfire scenarios are presented and the effects of the pyrogenic wind and the radiative/convective basic rate of spread terms investigated. Firebreaks such as roads and lakes are also included and solutions are found to match well with existing numerical and experimental results. The methods proposed in this work are suitably fast and new to the field of wildfire modelling.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106303"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884322","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}
Pub Date : 2025-02-01DOI: 10.1016/j.envsoft.2025.106318
Yifan Gao, Changqing Song, Zhifeng Liu, Sijing Ye, Peichao Gao
Land is multifunctional. Among all land change models, the only model capable of modeling multifunctional land changes is the CLUMondo model. However, the CLUMondo model is ineffective and inefficient. In the study, we addressed the problems by improving the CLUMondo model through four strategies, resulting in the improved version named “Land-N2N”. To evaluate the Land-N2N model, we designed six comparative experiments. In these experiments, we established the land systems using an upscaling approach based on Globeland30 data. Our finding shows that the effectiveness and efficiency of the Land-N2N model are better than the CLUMondo model. Specifically, the effectiveness of the Land-N2N model improved by 36% when measured with Kappa and by 377% when measured with Figure of Merit (FoM). Additionally, the efficiency of the Land-N2N model increased by 80%. The utility of the Land-N2N model lies in its ability to offer scientific solutions for land management by forecasting land changes.
{"title":"Land-N2N: An effective and efficient model for simulating the demand-driven changes in multifunctional lands","authors":"Yifan Gao, Changqing Song, Zhifeng Liu, Sijing Ye, Peichao Gao","doi":"10.1016/j.envsoft.2025.106318","DOIUrl":"10.1016/j.envsoft.2025.106318","url":null,"abstract":"<div><div>Land is multifunctional. Among all land change models, the only model capable of modeling multifunctional land changes is the CLUMondo model. However, the CLUMondo model is ineffective and inefficient. In the study, we addressed the problems by improving the CLUMondo model through four strategies, resulting in the improved version named “Land-N2N”. To evaluate the Land-N2N model, we designed six comparative experiments. In these experiments, we established the land systems using an upscaling approach based on Globeland30 data. Our finding shows that the effectiveness and efficiency of the Land-N2N model are better than the CLUMondo model. Specifically, the effectiveness of the Land-N2N model improved by 36% when measured with Kappa and by 377% when measured with Figure of Merit (FoM). Additionally, the efficiency of the Land-N2N model increased by 80%. The utility of the Land-N2N model lies in its ability to offer scientific solutions for land management by forecasting land changes.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106318"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990586","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}
Pub Date : 2025-02-01DOI: 10.1016/j.envsoft.2024.106306
Ehsan Foroumandi , Hamid Moradkhani , Witold F. Krajewski , Fred L. Ogden
The National Weather Service (NWS) operates the National Water Model (NWM) to provide continental-scale streamflow forecasting across the United States. Despite the broad scope of NWM, it faces limitations in delivering operational-level predictions. To overcome these limitations, the NWS embarked on development of the Next Generation Water Resources Modeling Framework (NextGen). However, a key shortcoming of the NextGen and NWM is the lack of robust data assimilation (DA) step. This study provides a DA module that incorporates the Ensemble Kalman Filter (EnKF), and the Particle Filter (PF) for use within the NextGen framework. The effectiveness of the developed module is evaluated by assimilating the in-situ observations to the Conceptual Functional Equivalent model, a simplified version of the current NWM, demonstrating the first advanced DA application on this model. The results show that both DA methods effectively enhance the performance of the model prediction, while the PF outperforms the EnKF.
{"title":"Ensemble data assimilation for operational streamflow predictions in the next generation (NextGen) framework","authors":"Ehsan Foroumandi , Hamid Moradkhani , Witold F. Krajewski , Fred L. Ogden","doi":"10.1016/j.envsoft.2024.106306","DOIUrl":"10.1016/j.envsoft.2024.106306","url":null,"abstract":"<div><div>The National Weather Service (NWS) operates the National Water Model (NWM) to provide continental-scale streamflow forecasting across the United States. Despite the broad scope of NWM, it faces limitations in delivering operational-level predictions. To overcome these limitations, the NWS embarked on development of the Next Generation Water Resources Modeling Framework (NextGen). However, a key shortcoming of the NextGen and NWM is the lack of robust data assimilation (DA) step. This study provides a DA module that incorporates the Ensemble Kalman Filter (EnKF), and the Particle Filter (PF) for use within the NextGen framework. The effectiveness of the developed module is evaluated by assimilating the in-situ observations to the Conceptual Functional Equivalent model, a simplified version of the current NWM, demonstrating the first advanced DA application on this model. The results show that both DA methods effectively enhance the performance of the model prediction, while the PF outperforms the EnKF.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106306"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884320","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}
Pub Date : 2025-02-01DOI: 10.1016/j.envsoft.2025.106322
Atte Moilanen , Pauli Lehtinen
Biodiversity offsets mean compensation for ecological losses caused by construction, development, land use or other human activities. They are commonly implemented via protection, restoration, or maintenance of habitats. The goal of offsetting is usually no net loss (NNL), which means that all net losses to biodiversity are fully compensated by commensurate net gains achieved via said offset actions. Here we collate and develop simple calculations for the determination of offset size (area) in the context of so-called multiplier approaches to offsets. We focus on the analysis of the response of habitat condition to action, which is a critical component of multiplier calculations, because the effectiveness and speed of different conservation actions and interventions can vary significantly. An excel application and R-code are included that implement calculations on offset response functions. The proposed methods are also relevant for other applications, including the generation of biodiversity credits for biodiversity credit markets.
{"title":"Simple analysis of biodiversity response functions and multipliers for biodiversity offsetting and other applications","authors":"Atte Moilanen , Pauli Lehtinen","doi":"10.1016/j.envsoft.2025.106322","DOIUrl":"10.1016/j.envsoft.2025.106322","url":null,"abstract":"<div><div>Biodiversity offsets mean compensation for ecological losses caused by construction, development, land use or other human activities. They are commonly implemented via protection, restoration, or maintenance of habitats. The goal of offsetting is usually no net loss (NNL), which means that all net losses to biodiversity are fully compensated by commensurate net gains achieved via said offset actions. Here we collate and develop simple calculations for the determination of offset size (area) in the context of so-called multiplier approaches to offsets. We focus on the analysis of the response of habitat condition to action, which is a critical component of multiplier calculations, because the effectiveness and speed of different conservation actions and interventions can vary significantly. An excel application and R-code are included that implement calculations on offset response functions. The proposed methods are also relevant for other applications, including the generation of biodiversity credits for biodiversity credit markets.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106322"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020242","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}
Pub Date : 2025-02-01DOI: 10.1016/j.envsoft.2025.106321
Floran Clopin , Ilaria Micella , Jorrit P. Mesman , Ma Cristina Paule-Mercado , Marina Amadori , Shuqi Lin , Lisette N. de Senerpont Domis , Jeroen J.M. de Klein
Eutrophication of inland water bodies is a serious environmental threat. This review explores current integrated models for lake and reservoir ecosystems that focus on nutrient dynamics at a catchment scale. Many studies applied either watershed or lake/reservoir models, however, 49 studies were finally selected that combined both. We derived a list of 21 watershed models, 23 lake/reservoir models, and 6 hybrid models in different sets of combinations, with a range of objectives (e.g. understanding the natural processes, predicting, and analysing climate change and land-use scenarios, or evaluating the different management options). Some integrated models had multiple applications whereas others were only applied once, with an uneven global geographical distribution.
To aid model selection by future users, we present a support tool discriminating the models by their features and application fields. This study encourages the development of open-source tools aiding interdisciplinary collaborations and further research in the field of integrated modelling.
{"title":"Integrated models of nutrient dynamics in lake and reservoir watersheds: A systematic review and integrated modelling decision pathway","authors":"Floran Clopin , Ilaria Micella , Jorrit P. Mesman , Ma Cristina Paule-Mercado , Marina Amadori , Shuqi Lin , Lisette N. de Senerpont Domis , Jeroen J.M. de Klein","doi":"10.1016/j.envsoft.2025.106321","DOIUrl":"10.1016/j.envsoft.2025.106321","url":null,"abstract":"<div><div>Eutrophication of inland water bodies is a serious environmental threat. This review explores current integrated models for lake and reservoir ecosystems that focus on nutrient dynamics at a catchment scale. Many studies applied either watershed or lake/reservoir models, however, 49 studies were finally selected that combined both. We derived a list of 21 watershed models, 23 lake/reservoir models, and 6 hybrid models in different sets of combinations, with a range of objectives (e.g. understanding the natural processes, predicting, and analysing climate change and land-use scenarios, or evaluating the different management options). Some integrated models had multiple applications whereas others were only applied once, with an uneven global geographical distribution.</div><div>To aid model selection by future users, we present a support tool discriminating the models by their features and application fields. This study encourages the development of open-source tools aiding interdisciplinary collaborations and further research in the field of integrated modelling.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106321"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020243","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}
Pub Date : 2025-02-01DOI: 10.1016/j.envsoft.2024.106287
Gerardo Grelle , Luigi Guerriero , Domenico Calcaterra , Diego Di Martire , Chiara Di Muro , Enza Vitale , Giuseppe Sappa
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.
VERE 框架是用 Python 设计和开发的，目的是利用先进的数据分析和机器学习能力，生成地震诱发的滑坡的危险置信度地图。虚拟环境（VE）和真实环境（RE）分别包含数据集和地图集，是该框架的核心。虚拟环境（VE）由数据集组成，其中包括形态测量、岩土工程和水文元数据，这些数据集是根据研究区域中这些参数的代表性经常值假设正态分布生成的。真实环境 (RE) 包括具有通用分辨率的网格数据集，这些数据集是通过对各种空间数据分布（包括 InSAR（干涉合成孔径雷达）数据）进行分析预处理而获得的。这些数据经过处理后，可用于检测正在发生的斜坡不稳定性和已勘测滑坡的活动状态。该框架采用了数值机器学习技术，根据高级简化物理模型得出的元解决方案进行训练。该模型考虑了粘塑性行为以及地震诱发滑动过程中剪切强度向残余状态的降低。考虑到不同初始孔隙水压力和地震强度情况下的共震位移和震后流动性，通过基于 ML 的预测生成了灾害置信度图。试验地点区域是位于意大利南部亚平宁山脉间的塞勒河流域，该地区是地震多发区，近期地震活动频繁。
{"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":"10.1016/j.envsoft.2024.106287","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106287"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","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}
Pub Date : 2025-02-01DOI: 10.1016/j.envsoft.2025.106328
Sümeyye Kaynak , Baran Kaynak , Carlos Erazo Ramirez , Ibrahim Demir
The development of web technologies and their integration into various fields has allowed a new era in data-driven decision-making and public data accessibility, especially through their adoption of monitoring and quantification environmental resources provided by governmental institutions. The use of web technologies has made it possible to create applications that can be accessed and used by a wide user base. However, dealing with the complexity of environmental data and non-standard data formats remains a hindering issue. To overcome these challenges and obtain up-to-date information from different institutions, we present Geo-WC: a web component framework specifically designed for earth and environmental sciences, serving as a bridge across various scientific domains. The Geo-WC utilizes a developer-friendly approach through simple HTML declarative syntax to bring together data in a single interface that is easy for developers to work with, making it accessible to users of varying skill levels. The framework integrates widely used web technologies, facilitating client-side data analysis, visualization, and accessibility within web browsers.
{"title":"Geo-WC: Custom web components for earth science organizations and agencies","authors":"Sümeyye Kaynak , Baran Kaynak , Carlos Erazo Ramirez , Ibrahim Demir","doi":"10.1016/j.envsoft.2025.106328","DOIUrl":"10.1016/j.envsoft.2025.106328","url":null,"abstract":"<div><div>The development of web technologies and their integration into various fields has allowed a new era in data-driven decision-making and public data accessibility, especially through their adoption of monitoring and quantification environmental resources provided by governmental institutions. The use of web technologies has made it possible to create applications that can be accessed and used by a wide user base. However, dealing with the complexity of environmental data and non-standard data formats remains a hindering issue. To overcome these challenges and obtain up-to-date information from different institutions, we present Geo-WC: a web component framework specifically designed for earth and environmental sciences, serving as a bridge across various scientific domains. The Geo-WC utilizes a developer-friendly approach through simple HTML declarative syntax to bring together data in a single interface that is easy for developers to work with, making it accessible to users of varying skill levels. The framework integrates widely used web technologies, facilitating client-side data analysis, visualization, and accessibility within web browsers.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106328"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990594","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}
Pub Date : 2025-02-01DOI: 10.1016/j.envsoft.2025.106331
Eun Taek Shin , Se Hyuck An , Sung Won Park , Seung Oh Lee , Chang Geun Song
Accurate parameter selection is crucial for reliable predictions in fluid dynamics, environmental transport, and urban flood prediction. Traditional manual methods are time-consuming and prone to errors. This study introduces an automated algorithm to optimize roughness and viscosity coefficients in two-dimensional flow analysis models. Our algorithm automates the simulation process within specified parameter ranges, using Root Mean Square Error (RMSE) to compare results with experimental data. Applied to a diverging channel and an abruptly widening channel, the algorithm successfully identified optimal parameters, accurately matching experimental observations. Heatmaps visualize RMSE values, facilitating optimal parameter identification. This advancement enhances model efficiency and accuracy, streamlining the parameter determination process and offering a robust method for hydraulic modeling.
{"title":"Development of optimal parameter determination algorithm for two-dimensional flow analysis model","authors":"Eun Taek Shin , Se Hyuck An , Sung Won Park , Seung Oh Lee , Chang Geun Song","doi":"10.1016/j.envsoft.2025.106331","DOIUrl":"10.1016/j.envsoft.2025.106331","url":null,"abstract":"<div><div>Accurate parameter selection is crucial for reliable predictions in fluid dynamics, environmental transport, and urban flood prediction. Traditional manual methods are time-consuming and prone to errors. This study introduces an automated algorithm to optimize roughness and viscosity coefficients in two-dimensional flow analysis models. Our algorithm automates the simulation process within specified parameter ranges, using Root Mean Square Error (RMSE) to compare results with experimental data. Applied to a diverging channel and an abruptly widening channel, the algorithm successfully identified optimal parameters, accurately matching experimental observations. Heatmaps visualize RMSE values, facilitating optimal parameter identification. This advancement enhances model efficiency and accuracy, streamlining the parameter determination process and offering a robust method for hydraulic modeling.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106331"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020248","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}
Pub Date : 2025-02-01DOI: 10.1016/j.envsoft.2024.106265
M.A. Harris , T.H. Diehl , L.E. Gorman Sanisaca , A.E. Galanter , M.A. Lombard , K.D. Skinner , C. Chamberlin , B.A. McCarthy , R. Niswonger , J.S. Stewart , K.J. Valseth
Thermoelectric (TE) power plants withdraw more water than any other sector of water use in the United States and consume water at rates that can be significant especially in water-stressed regions. Historical TE water-use data have been inconsistent, incomplete, or discrepant, resulting in an increased research focus on improving the accuracy and availability of TE water-use data using modeling approaches. This paper describes and benchmarks new code that was developed to automate and update a physics-based TE water use model that was previously published. Utilizing the automated physics-based model, monthly TE-power water withdrawal and consumption were calculated for a total of 1341 TE power plants for the 2008–2020 historical reanalysis. The updated and automated physics-based thermoelectric-power water-use model provides spatially and temporally relevant TE water-use estimates that are consistent, reproducible, transparent, and can be generated efficiently for water-using, utility-scale TE-power plants across conterminous United States (CONUS).
{"title":"Automating physics-based models to estimate thermoelectric-power water use","authors":"M.A. Harris , T.H. Diehl , L.E. Gorman Sanisaca , A.E. Galanter , M.A. Lombard , K.D. Skinner , C. Chamberlin , B.A. McCarthy , R. Niswonger , J.S. Stewart , K.J. Valseth","doi":"10.1016/j.envsoft.2024.106265","DOIUrl":"10.1016/j.envsoft.2024.106265","url":null,"abstract":"<div><div>Thermoelectric (TE) power plants withdraw more water than any other sector of water use in the United States and consume water at rates that can be significant especially in water-stressed regions. Historical TE water-use data have been inconsistent, incomplete, or discrepant, resulting in an increased research focus on improving the accuracy and availability of TE water-use data using modeling approaches. This paper describes and benchmarks new code that was developed to automate and update a physics-based TE water use model that was previously published. Utilizing the automated physics-based model, monthly TE-power water withdrawal and consumption were calculated for a total of 1341 TE power plants for the 2008–2020 historical reanalysis. The updated and automated physics-based thermoelectric-power water-use model provides spatially and temporally relevant TE water-use estimates that are consistent, reproducible, transparent, and can be generated efficiently for water-using, utility-scale TE-power plants across conterminous United States (CONUS).</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106265"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128135","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}
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