Environmental Modelling & Software最新文献_第5页

Spatiotemporal flood depth and velocity dynamics using a convolutional neural network within a sequential Deep-Learning framework 时序深度学习框架中使用卷积神经网络的时空洪水深度和速度动态

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106307

Mohamed M. Fathi , Zihan Liu , Anjali M. Fernandes , Michael T. Hren , Dennis O. Terry , C. Nataraj , Virginia Smith

{"title":"Spatiotemporal flood depth and velocity dynamics using a convolutional neural network within a sequential Deep-Learning framework","authors":"Mohamed M. Fathi , Zihan Liu , Anjali M. Fernandes , Michael T. Hren , Dennis O. Terry , C. Nataraj , Virginia Smith","doi":"10.1016/j.envsoft.2024.106307","DOIUrl":"10.1016/j.envsoft.2024.106307","url":null,"abstract":"<div><div>Computational hydrodynamic models support river science and management. However, current physics-based models face computational challenges; they require extensive processing time for large-scale two-dimensional flood simulations. Despite the success of Deep Learning (DL) applications in generating inundation maps, accurate prediction of unsteady flood hydrodynamic maps remains challenging. This paper compares traditional approaches to a novel DL approach, which integrates convolutional neural networks with long short-term memory, to deliver precise, rapid, and continuous simulation of the spatiotemporal dynamics of river floods. This is the first DL framework able to generate essential hydrodynamic variables: water depth, velocity magnitude, and flow direction maps. Water depth and velocity magnitude predictions across the testing dataset are robust, with average RMSE of 0.14 m and 0.02 m/s, respectively. The DL predictions are 415 times faster compared to traditional computational approaches, representing a paradigm shift in hydrodynamics modeling that advances long-term flood simulations and resilient river management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106307"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parameter estimation and uncertainty quantification of rainfall-runoff models using data assimilation methods based on deep learning and local ensemble updates 基于深度学习和局部集合更新的数据同化方法的降雨径流模型参数估计和不确定性量化

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106332

Lei Yao , Jiangjiang Zhang , Chenglong Cao , Feifei Zheng

{"title":"Parameter estimation and uncertainty quantification of rainfall-runoff models using data assimilation methods based on deep learning and local ensemble updates","authors":"Lei Yao , Jiangjiang Zhang , Chenglong Cao , Feifei Zheng","doi":"10.1016/j.envsoft.2025.106332","DOIUrl":"10.1016/j.envsoft.2025.106332","url":null,"abstract":"<div><div>Rainfall-runoff (RR) modeling is crucial for flood preparedness and water resource management. Accurate RR model predictions depend on effective parameter estimation and uncertainty quantification using observed data through data assimilation (DA). Traditional DA methods often struggle with challenges such as non-Gaussianity and equifinality. To address these challenges, this study introduces two ensemble smoother methods, i.e., ES<sub>DL</sub> with a deep learning-based update, and ES<sub>LU</sub> with a local ensemble update, aiming to enhance the calibration of RR models. To demonstrate the effectiveness of our proposed methods, we conduct a comprehensive analysis involving various DA techniques applied to parameter estimation of RR models. We compare these methods with traditional approaches, evaluating deep neural network architectures, iteration numbers, and measurement errors. The results unequivocally showcase the consistent reliability of ES<sub>DL</sub> and ES<sub>LU</sub>, especially the latter one, across diverse scenarios, establishing them as promising approaches for the effective calibration and uncertainty quantification of RR models.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106332"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020245","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

Facilitating open data and open model integration with generic parameter input file generators in the CyberWater framework

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106266

Daniel Luna , Ranran Chen , Ahmed Sheba , Ryan Young , Yao Liang , Xu Liang

{"title":"Facilitating open data and open model integration with generic parameter input file generators in the CyberWater framework","authors":"Daniel Luna , Ranran Chen , Ahmed Sheba , Ryan Young , Yao Liang , Xu Liang","doi":"10.1016/j.envsoft.2024.106266","DOIUrl":"10.1016/j.envsoft.2024.106266","url":null,"abstract":"<div><div>Effective data and model integration is crucial for exploring scientific questions in hydrology and other geosciences. The increasing heterogeneity and complexity of data and models pose integration challenges. CyberWater addresses these with an open-data and open-modeling framework. Featuring GUI-based workflows, it includes Data Agents for accessing diverse online data sources and a Generic Model Agent Toolkit for seamless, code-free model integration. This study introduces the Static Parameter Agent suite, a novel toolkit designed to streamline the creation and organization of parameter files required for various models. The toolkit enables users to efficiently and automatically generate files on demand, minimizing the time-consuming and error-prone manual preparation of complex parameter files. It further logs all changes to parameter values across each model simulation, ensuring a reproducible end-to-end process. It connects seamlessly with Geographic Information System (GIS) engines like GRASS GIS and has been tested on models including VIC, DHSVM, and CASA-CNP.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106266"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143128006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel sample-enhancement framework for machine learning-based urban flood susceptibility assessment 基于机器学习的城市洪水易感性评估样本增强框架

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106314

Huabing Huang, Changpeng Wang, Zhiwen Tao, Jiayin Zhan

{"title":"A novel sample-enhancement framework for machine learning-based urban flood susceptibility assessment","authors":"Huabing Huang, Changpeng Wang, Zhiwen Tao, Jiayin Zhan","doi":"10.1016/j.envsoft.2024.106314","DOIUrl":"10.1016/j.envsoft.2024.106314","url":null,"abstract":"<div><div>The commonly used random sampling method in machine learning-based flood susceptibility studies has two major issues: a default invalid assumption of spatial homogeneity and an inadequate number of non-flood samples. To address these issues, this study proposed a novel sample-enhancement framework to improve the quality of training samples on both flood and non-flood sides. Three one-way enhancements (two flood and one non-flood) and two joint enhancements were designed. The enhancements were evaluated against random sampling using four mainstream machine learning algorithms (ANN, RF, SVM, and XGBoost) across two heterogeneous urban regions in Guangzhou, China. The highest performances are achieved by the joint enhancements, which are followed by one-way enhancements and random sampling (no enhancement). Another important conclusion is that one-way enhancements exhibit divergent yet complementary effects. Flood enhancements primarily affect susceptibility distribution (mean value and standard deviation), while non-flood enhancements mainly influence binary classification performance (AUC).</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106314"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905678","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

Environmental-Health Convergence: A deep learning-oriented decision support system for catalyzing sustainable healthy food systems 环境与健康融合：一个面向深度学习的决策支持系统，用于催化可持续健康食品系统

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106309

Prince Agyemang , Ebenezer M. Kwofie , Jamie I. Baum , Dongyi Wang , Emmanuel A. Kwofie

{"title":"Environmental-Health Convergence: A deep learning-oriented decision support system for catalyzing sustainable healthy food systems","authors":"Prince Agyemang , Ebenezer M. Kwofie , Jamie I. Baum , Dongyi Wang , Emmanuel A. Kwofie","doi":"10.1016/j.envsoft.2024.106309","DOIUrl":"10.1016/j.envsoft.2024.106309","url":null,"abstract":"<div><div>To generate evidence to address food system challenges, we developed an adaptable framework for multimodel assessment of the convergence effect of health and environmental drivers in food systems. We achieved this goal by developing a modeling framework that facilitates testing and applying four deep-learning algorithms using a case study of the United States's food system. Among the models tested, the bidirectional and single-layer long short-term memory models outperformed the others with α<sub><em>E</em></sub>(2.75) and α<sub><em>H</em></sub>(3.51) when predicting environmental drivers and health drivers, respectively. All the models tested performed better at predicting environmental than health drivers. The best-performing model for each dimension was deployed into the Food System Rapid Overview Assessment through Scenarios (FS-ROAS) tool. As we approach the endpoint of the transformative 2030 agenda, FS-ROAS can be a timely toolkit that enables stakeholders to explore diverse intervention scenarios in the context of short-medium and long-term goals for future food systems and generate evidence to guide future actions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"185 ","pages":"Article 106309"},"PeriodicalIF":4.8,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review 人工智能在大气污染监测与预报中的应用综述

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106312

Sreeni Chadalavada , Oliver Faust , Massimo Salvi , Silvia Seoni , Nawin Raj , U. Raghavendra , Anjan Gudigar , Prabal Datta Barua , Filippo Molinari , Rajendra Acharya

{"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}

引用次数: 0

Modelling wildfire spread and spotfire merger using conformal mapping and AAA-least squares methods 用保角映射和aaa最小二乘法模拟野火蔓延和火点合并

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106303

Samuel J. Harris, N.R. McDonald

引用次数: 0

Land-N2N: An effective and efficient model for simulating the demand-driven changes in multifunctional lands Land-N2N：一个模拟多功能土地需求驱动变化的有效模型

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106318

Yifan Gao, Changqing Song, Zhifeng Liu, Sijing Ye, Peichao Gao

引用次数: 0

Ensemble data assimilation for operational streamflow predictions in the next generation (NextGen) framework 下一代（NextGen）框架中用于操作流预测的集成数据同化

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2024.106306

Ehsan Foroumandi , Hamid Moradkhani , Witold F. Krajewski , Fred L. Ogden

引用次数: 0

Simple analysis of biodiversity response functions and multipliers for biodiversity offsetting and other applications 生物多样性响应函数和乘数在生物多样性补偿和其他应用中的简单分析

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-02-01 DOI: 10.1016/j.envsoft.2025.106322

Atte Moilanen , Pauli Lehtinen

引用次数: 0