Environmental Modelling & Software最新文献

An urban flood inundation model accelerated by the parallel acceleration technology

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-24 DOI: 10.1016/j.envsoft.2025.106441

Wei Zhu , Zhe Cao , Pingping Luo , Shuangtao Wang , Chengyi Xv , Yongxing Ji

{"title":"An urban flood inundation model accelerated by the parallel acceleration technology","authors":"Wei Zhu , Zhe Cao , Pingping Luo , Shuangtao Wang , Chengyi Xv , Yongxing Ji","doi":"10.1016/j.envsoft.2025.106441","DOIUrl":"10.1016/j.envsoft.2025.106441","url":null,"abstract":"<div><div>Due to factors such as changes in land use and climate change, floods are increasingly occurring worldwide, resulting in excessive property damage and casualties in urban areas. Numerical simulation techniques can provide valuable support in mitigating urban flood risks. This study developed a coupled flood inundation model for one-dimensional sewer and two-dimensional surface based on parallel acceleration technology. The main findings include: 1. The model was validated in Omihachiman City and Shanghai City, demonstrating satisfactory results in flood inundation simulations and confirming the model's reliability in simulating flood processes. 2. A comparison of simulation times between the surface inundation model's serial version, CPU-accelerated version, and GPU-accelerated version was conducted. The GPU-accelerated version showed significant speed-up compared to the CPU model using the same numerical algorithms, with better performance as computational units increased. 3. The performance of the model is significantly influenced by the underground sewer model.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"189 ","pages":"Article 106441"},"PeriodicalIF":4.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746779","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 surrogate machine learning model using random forests for real-time flood inundation simulations

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-21 DOI: 10.1016/j.envsoft.2025.106439

Santosh Kumar Sasanapuri, C.T. Dhanya, A.K. Gosain

{"title":"A surrogate machine learning model using random forests for real-time flood inundation simulations","authors":"Santosh Kumar Sasanapuri, C.T. Dhanya, A.K. Gosain","doi":"10.1016/j.envsoft.2025.106439","DOIUrl":"10.1016/j.envsoft.2025.106439","url":null,"abstract":"<div><div>Real-time simulation of flood inundation helps to mitigate the catastrophic effects on human lives by facilitating emergency evacuations. Traditional two-dimensional (2D) physics-based hydrodynamic models, though accurate, require significant computational time, thereby rendering them unsuitable for such real-time applications. To address this limitation, we developed Random Forest (RF) models as surrogate hydrodynamic models for predicting maximum flood depth and velocity under complex fluvial conditions with backwater effects. These models integrate hydrological parameters, such as upstream discharge, physical catchment characteristics, to enhance predictive accuracy and generalizability. A comprehensive assessment revealed that the inclusion of physical characteristics increased the prediction accuracy of RF models by 1.72 times and 2.60 times for depth and velocity models with root mean square error of 0.494 m and 0.148 m/s respectively, compared to baseline models. Furthermore, the RF models required only 1.5 %–4 % (for minor flood event and major flood event respectively) of the computational time needed by hydrodynamic models. With its ability to understand complex flooding scenarios with high prediction accuracy and computing efficiency, the proposed RF models have demonstrated great potential for real-time flood inundation modelling. Efforts in this direction to improve the real-time flood inundation predictions may greatly aid the decision makers for undertaking emergency evacuations during catastrophic flood events.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106439"},"PeriodicalIF":4.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705312","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

ZHPO-LightXBoost an integrated prediction model based on small samples for pesticide residues in crops

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-21 DOI: 10.1016/j.envsoft.2025.106440

Xiaopeng Sha , Yuejie Zhu , Xiaoying Sha , Zheng Guan , Shuyu Wang

引用次数: 0

Revealing the causal response in landslide hydrology with MT-InSAR and spatial-temporal CCM: A case study in Jinsha River

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-20 DOI: 10.1016/j.envsoft.2025.106434

Xiao Ling , Dongping Ming , Zhi Zhang , Jianao Cai , Wenyi Zhao , Mingzhi Zhang , Yongshuang Zhang , Bingbo Gao

{"title":"Revealing the causal response in landslide hydrology with MT-InSAR and spatial-temporal CCM: A case study in Jinsha River","authors":"Xiao Ling , Dongping Ming , Zhi Zhang , Jianao Cai , Wenyi Zhao , Mingzhi Zhang , Yongshuang Zhang , Bingbo Gao","doi":"10.1016/j.envsoft.2025.106434","DOIUrl":"10.1016/j.envsoft.2025.106434","url":null,"abstract":"<div><div>Convergent Cross Mapping (CCM) is a powerful tool for analyzing causality in complex dynamic systems. However, standard CCM and Geographical CCM (GCCM) focus exclusively on temporal or spatial attributes, failing to integrate both dimensions. This study introduces a spatial-temporal CCM that quantifies the state of convergence to enable batched analyses of large-scale spatial datasets. The proposed method captures variations in causality and delayed responses across different spatial locations, thereby enhancing spatial-temporal data utility and the efficiency of causal inference. Using this model, we analyzed the relationship between landslides and hydrology. The results revealed that Areas with High Displacement (AHDs) responded more rapidly to hydrological factors than stable regions, with deep-layer soil moisture (100–289 cm depth) exhibiting the strongest causality and the fastest response. Building on these findings, we identified zones of minimal instability within each AHD (areas that displayed the quickest response to hydrological changes).</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106434"},"PeriodicalIF":4.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705314","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

pytRIBS: An open, modular, and reproducible python-based framework for distributed hydrologic modeling

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-19 DOI: 10.1016/j.envsoft.2025.106432

L. Wren Raming , Enrique R. Vivoni , C. Josh Cederstrom , M. Akram Hossain , Jose A. Becerra

引用次数: 0

A machine learning model integrating spatiotemporal attention and residual learning for predicting periodic air pollutant concentrations

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-18 DOI: 10.1016/j.envsoft.2025.106438

Farun An , Dong Yang , Xiaoyue Sun , Haibin Wei , Feilong Chen

{"title":"A machine learning model integrating spatiotemporal attention and residual learning for predicting periodic air pollutant concentrations","authors":"Farun An , Dong Yang , Xiaoyue Sun , Haibin Wei , Feilong Chen","doi":"10.1016/j.envsoft.2025.106438","DOIUrl":"10.1016/j.envsoft.2025.106438","url":null,"abstract":"<div><div>The variations in pollutant concentrations during tunnel construction, in urban atmosphere, and along pedestrian paths exhibit distinct periodicity. Accurate prediction of pollutant concentrations is crucial for improving the quality of the construction and living environments. Using tunnel construction scenarios as a case, this study proposes a Convolutional Neural Network and Bidirectional Long Short-Term Memory model (CNN-BiLSTM) integrating spatiotemporal attention mechanisms and residual learning. The model employs experimental and field measurement data, with Pearson correlation analysis used for preliminary data screening. The proposed model was evaluated using eight specific metrics and compared against eight baseline models. The model exhibited strong predictive performance, with R<sup>2</sup> of 0.9826 for CO and 0.9844 for PM<sub>2.5</sub>. For 15-step CO predictions, R<sup>2</sup> was 0.9584 with MSE of 0.031. Urban-scale predictions showed R<sup>2</sup> of 0.9599 for CO and 0.9774 for PM<sub>2.5</sub>, while traffic-related predictions were 0.9316 for CO and 0.9525 for PM<sub>2.5</sub>, indicating improved accuracy and applicability.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106438"},"PeriodicalIF":4.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143678381","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

Carbon emissions prediction based on ensemble models: An empirical analysis from China

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-18 DOI: 10.1016/j.envsoft.2025.106437

Song Hu , Shixuan Li , Lin Gong , Dan Liu , Zhe Wang , Gangyan Xu

{"title":"Carbon emissions prediction based on ensemble models: An empirical analysis from China","authors":"Song Hu , Shixuan Li , Lin Gong , Dan Liu , Zhe Wang , Gangyan Xu","doi":"10.1016/j.envsoft.2025.106437","DOIUrl":"10.1016/j.envsoft.2025.106437","url":null,"abstract":"<div><div>The global warming problem has seriously threatened the sustainable development of human society. In order to effectively control carbon emissions, this study integrates economic, social, energy, and environment (ESEE) factors to develop a comprehensive, multi-dimensional carbon emissions prediction (CEP) index system, crucial for analyzing the determinants of carbon emissions and forecasting future emissions. Then employ a grey correlation analysis (GRA), followed by a genetic algorithm-based (GA-based) feature extraction method to demonstrate the strong correlation between selected factors and carbon emissions and refine the input of single and ensemble machine learning models for predicting carbon emissions in China. The number of selected economic factors is the highest, followed by energy factors, with only one social and environmental factors being selected. Meanwhile, the prediction results show that Bagging-ANN outperforms other algorithms with the lowest R<sup>2</sup> value of 0.8792, followed by Voting, Stacking, ANN, Bagging-SVR and SVR.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106437"},"PeriodicalIF":4.8,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684795","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

Evaluating Australian forest fire rate of spread models using VIIRS satellite observations 利用 VIIRS 卫星观测数据评估澳大利亚森林火灾蔓延速度模型

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-17 DOI: 10.1016/j.envsoft.2025.106436

Matthew G. Gale, Geoffrey J. Cary

引用次数: 0

AquaNutriOpt II: A multi-period bi-objective nutrient optimization python tool for controlling harmful algal blooms — A case study of Lake Okeechobee

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-16 DOI: 10.1016/j.envsoft.2025.106428

Ashim Khanal , Osama M. Tarabih , Mauricio E. Arias , Qiong Zhang , Hadi Charkhgard

{"title":"AquaNutriOpt II: A multi-period bi-objective nutrient optimization python tool for controlling harmful algal blooms — A case study of Lake Okeechobee","authors":"Ashim Khanal , Osama M. Tarabih , Mauricio E. Arias , Qiong Zhang , Hadi Charkhgard","doi":"10.1016/j.envsoft.2025.106428","DOIUrl":"10.1016/j.envsoft.2025.106428","url":null,"abstract":"<div><div>We introduce a significantly enhanced version of AquaNutriOpt, now equipped with advanced mathematical optimization capabilities absent in its initial release (Khanal et al., 2024). AquaNutriOpt II is a user-friendly, free, open-source Python tool designed to address the complex challenge of optimizing nutrient management for controlling harmful algal blooms. In this latest version, users gain the flexibility to incorporate multiple time periods into their analyses. Moreover, they can now optimize the management of two nutrients concurrently (primarily phosphorus and nitrogen) through an innovative multi-objective optimization framework. Building upon its predecessor, AquaNutriOpt II continues to streamline the identification of optimal Best Management Practices (BMPs) and Treatment Technologies (TTs), including determining the most suitable locations for implementation while considering budgetary constraints. To showcase the efficacy and advantages of AquaNutriOpt II, we apply it to a real-world case study centered on Lake Okeechobee in Florida, USA.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106428"},"PeriodicalIF":4.8,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643037","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

WigglyRivers: A tool to characterize the multiscale nature of meandering channels

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-03-14 DOI: 10.1016/j.envsoft.2025.106423

Daniel Gonzalez-Duque , Jesus D. Gomez-Velez

{"title":"WigglyRivers: A tool to characterize the multiscale nature of meandering channels","authors":"Daniel Gonzalez-Duque , Jesus D. Gomez-Velez","doi":"10.1016/j.envsoft.2025.106423","DOIUrl":"10.1016/j.envsoft.2025.106423","url":null,"abstract":"<div><div>Channel sinuosity is ubiquitous along river networks, producing complex patterns that encapsulate and influence morphodynamic processes and ecosystem services. Accurately characterizing these patterns is challenging with traditional curvature-based algorithms. Here, we present WigglyRivers, a Python package that builds on existing wavelet-based methods to create an unsupervised meander identification and characterization tool. The package uses planimetric information the user provides or from the USGS’s High-Resolution National Hydrography Dataset to characterize individual reaches or entire river networks. WigglyRivers also includes a supervised river identification tool for manually selecting individual meandering features. Here, we provide examples of idealized river transects and show the capabilities of WigglyRivers. We also use the supervised identification tool to validate the unsupervised identification on river transects across the continental US. WigglyRivers is a tool to understand better the multiscale characteristics of river networks and the link between river geomorphology and river corridor connectivity.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106423"},"PeriodicalIF":4.8,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642324","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