Environmental Modelling & Software最新文献_第3页

Accurate calibration of hydrological models with evolutionary computation multi-method ensembles 基于进化计算多方法组合的水文模型精确定标

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-10-01 DOI: 10.1016/j.envsoft.2025.106698

Eugenio Lorente-Ramos , Francisco Gomariz-Castillo , Francisco Pellicer-Martínez , Laura Cornejo-Bueno , Jorge Pérez-Aracil , Sancho Salcedo-Sanz

{"title":"Accurate calibration of hydrological models with evolutionary computation multi-method ensembles","authors":"Eugenio Lorente-Ramos , Francisco Gomariz-Castillo , Francisco Pellicer-Martínez , Laura Cornejo-Bueno , Jorge Pérez-Aracil , Sancho Salcedo-Sanz","doi":"10.1016/j.envsoft.2025.106698","DOIUrl":"10.1016/j.envsoft.2025.106698","url":null,"abstract":"<div><div>Climate change significantly impacts the hydrological cycle, posing challenges for regional water resource management and Sustainable Development Goals. Hydrological modelling is essential for planning and adaptation, being necessary to dispose different mathematical computing tools to be able to calibrate them properly. This study introduces the Dynamic Probabilistic Coral Reefs Optimization algorithm with Substrate Layer (DPCRO-SL), a multi-method ensemble approach, to enhance hydrological model calibration. The algorithm was applied to the <span><math><mrow><mi>a</mi><mi>b</mi><mi>c</mi><mi>d</mi></mrow></math></span> hydrological model in two Spanish river basins, using lumped and semi-distributed structures to test adaptability. Results were compared with the SCE-UA algorithm, a benchmark in hydrology, using metrics such as Nash–Sutcliffe Efficiency (NSE), Mean Squared Error (MSE), Kling–Gupta Efficiency (KGE), and Percent Bias (Pbias). For instance, in THRB basin during the test period, the proposed DPCRO-SL algorithm achieved NSE = 0.765, KGE = 0.875, MSE = 171.9, and Pbias = 2.6, whereas the reference SCE-UA algorithm obtained NSE = 0.689, KGE = 0.748, MSE = 227.8, and Pbias = –14.8. DPCRO-SL consistently outperformed SCE-UA, especially in test scenarios reflecting forward projections. These findings underscore the potential of DPCRO-SL as a robust tool for hydrological modelling and climate adaptation, offering improved accuracy and reliability in model calibration.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106698"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227285","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

Snow water equivalent forecasting in sub-arctic and arctic regions: Efficient recurrent neural networks approach 亚北极和北极地区雪水当量预报：高效递归神经网络方法

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-10-01 DOI: 10.1016/j.envsoft.2025.106695

Miika Malin , Jarkko Okkonen , Jaakko Suutala

{"title":"Snow water equivalent forecasting in sub-arctic and arctic regions: Efficient recurrent neural networks approach","authors":"Miika Malin , Jarkko Okkonen , Jaakko Suutala","doi":"10.1016/j.envsoft.2025.106695","DOIUrl":"10.1016/j.envsoft.2025.106695","url":null,"abstract":"<div><div>Snow water equivalent (SWE) expresses the amount of liquid water in the snow pack. Accurate SWE forecasts are essential for reliable hydrological modeling, as direct SWE measuring is labor-intensive. In this study, we systematically compared gated recurrent unit (GRU) and long short-term memory (LSTM) architectures, showing that GRU models achieve comparable accuracy with greater computational efficiency. By applying Bayesian optimization, data preprocessing, and a time-to-vector representation of temporal features, we introduce two novel GRU-based models: a lightweight model (321 parameters; average NSE = 0.91) and a more complex model (51973 parameters, average NSE = 0.95). Importantly, these models generalize effectively across geographically distant stations, demonstrating robust predictive performance under varied climatic conditions. The primary novelty of our study is identifying GRU as a computationally efficient, accurate alternative to LSTM in SWE forecasting, combined with demonstrating that compact models with smaller hidden states provide strong spatial generalization and excellent accuracy.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106695"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227286","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

Optimizing soil remediation volumes via inverse analytical modeling 通过逆分析模型优化土壤修复量

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-10-01 DOI: 10.1016/j.envsoft.2025.106704

Alexis Gris , Jacques Bodin , Laurent Caner

引用次数: 0

PREP: A software for assessing and visualizing seasonal and spatial representativeness of climate proxies PREP：用于评估和可视化气候代用物的季节和空间代表性的软件

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-10-01 DOI: 10.1016/j.envsoft.2025.106702

Yang Liu , Yiwen Gao , Jingyun Zheng

{"title":"PREP: A software for assessing and visualizing seasonal and spatial representativeness of climate proxies","authors":"Yang Liu , Yiwen Gao , Jingyun Zheng","doi":"10.1016/j.envsoft.2025.106702","DOIUrl":"10.1016/j.envsoft.2025.106702","url":null,"abstract":"<div><div>Historical temperature reconstructions are primarily derived from climate proxy data such as tree-ring. Discrepancies among temperature reconstructions have prompted considerable debate, with much of this variation attributable to differences in the representativeness of the underlying proxy data. Representativeness refers to the specific seasonal temperature variations indicated by the proxies and the strength of their correlation with instrumental records, as well as the spatial extent over which they are applicable. At present, visualizations of historical climate change through science platforms predominantly rely on reconstruction curves, lacking effective methods to convey the representativeness of proxy data. In this study, we developed the Proxy Representativeness Evaluation Package (PREP), a software that employs a \"clock-layout\" to depict seasonality by dividing the clock dial into twelve 30° segments (one per month) and encodes correlation by fill color. We further optimized the algorithm for plotting representativeness maps, achieving more than a 50-fold increase in computational efficiency.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106702"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227402","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

AI-driven IoT and gamification for smart water management: Real-time monitoring and predictive analytics 智能水管理的人工智能驱动物联网和游戏化：实时监控和预测分析

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-09-30 DOI: 10.1016/j.envsoft.2025.106711

Madhukrishna Priyadarsini, Rahul, Reetanjali Paikra

{"title":"AI-driven IoT and gamification for smart water management: Real-time monitoring and predictive analytics","authors":"Madhukrishna Priyadarsini, Rahul, Reetanjali Paikra","doi":"10.1016/j.envsoft.2025.106711","DOIUrl":"10.1016/j.envsoft.2025.106711","url":null,"abstract":"<div><div>Water conservation remains a pressing global challenge, worsened by inefficient usage and delayed leak detection. This study presents the Smart Gamified Water Conservation System (SGWCS), a novel framework that integrates IoT-based water metering, AI-driven analytics, and adaptive user engagement. SGWCS employs a CNN-Attention-LSTM model for real-time demand forecasting, achieving 97.2% accuracy, and a hybrid rule-ML anomaly detection system with 92.8% sensitivity, reducing false positives by 38% in industrial trials. A gamification module with AI-personalized nudges increased user participation by 28% and led to an average 12.5% reduction in residential water use. The system was deployed across residential, industrial, and municipal sites in Raipur, India, using a privacy-by-design edge-cloud architecture. Evaluation metrics include prediction accuracy, leak detection performance, and user retention over time. These results demonstrate SGWCS as a scalable, intelligent, and ethically deployable platform for data-driven water resource optimization.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106711"},"PeriodicalIF":4.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262085","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

Estimation of grassland aboveground biomass in the Three-Rivers Source Region with explainable machine learning 基于可解释机器学习的三江源草地地上生物量估算

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-09-29 DOI: 10.1016/j.envsoft.2025.106710

Zhe Wang , Xiuying Wang , Baocheng Wei , Qiang Bie , Yaowen Xie , Ruixiang Xiao , Xiaoyun Wang , Xiaodong Li , Binrong Zhou , Zecheng Guo , Bin Qiao

{"title":"Estimation of grassland aboveground biomass in the Three-Rivers Source Region with explainable machine learning","authors":"Zhe Wang , Xiuying Wang , Baocheng Wei , Qiang Bie , Yaowen Xie , Ruixiang Xiao , Xiaoyun Wang , Xiaodong Li , Binrong Zhou , Zecheng Guo , Bin Qiao","doi":"10.1016/j.envsoft.2025.106710","DOIUrl":"10.1016/j.envsoft.2025.106710","url":null,"abstract":"<div><div>Grassland aboveground biomass (AGB) is a key indicator of ecosystem function. While machine learning (ML) has improved AGB estimation from remote sensing, limited interpretability restricts its application in management. This study applied the SHapley Additive exPlanations (SHAP) method with the optimal ML model in the Three-River Source Region (TRSR) to quantify the main and interactive effects of climatic drivers on AGB and reveal their nonlinear responses. Results showed that vegetation indices contributed most to AGB estimation. AGB showed threshold responses to temperature and precipitation, with peak positive effects at 10–12 °C for current-month temperature. Warming enhanced AGB under low antecedent temperature or moderate precipitation but had diminishing or negative effects under extreme hydrothermal conditions. From 2003 to 2022, AGB increased in 56.4 % and declined in 35.15 % of the area. This study provides an interpretable AGB model and insights into climate-biomass relationships, supporting adaptive grassland management in alpine regions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"195 ","pages":"Article 106710"},"PeriodicalIF":4.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145204365","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 hybrid model with a physics-constrained neural network to improve hydrodynamic prediction 利用物理约束神经网络的混合模型改进水动力预测

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-09-20 DOI: 10.1016/j.envsoft.2025.106699

Wangjiayi Liu , Guanghua Guan , Xin Tian , Xiaonan Chen , Liangsheng Shi , Guangtao Fu

{"title":"A hybrid model with a physics-constrained neural network to improve hydrodynamic prediction","authors":"Wangjiayi Liu , Guanghua Guan , Xin Tian , Xiaonan Chen , Liangsheng Shi , Guangtao Fu","doi":"10.1016/j.envsoft.2025.106699","DOIUrl":"10.1016/j.envsoft.2025.106699","url":null,"abstract":"<div><div>Accurate hydrodynamic prediction is vital for water transfer systems to ensure delivery efficiency and prevent damage. Traditional physics-based models use predefined or estimated offtake discharges as lateral boundaries, neglecting interactions between real-time hydraulic states and future offtake discharge, then causing water level predictive errors. To address this, we propose a hybrid model with a physics-constrained neural network (PcNN) for real-time offtake discharge prediction. The PcNN employs long short-term memory (LSTM), incorporating physical constraints into the input layer and loss function from prior knowledge and a hydrodynamic model. Applied to a large-scale water transfer system in China, the hybrid model improves offtake discharge prediction by 30 %–70 % over the baseline and boosts water level forecasting, with Nash-Sutcliffe efficiency coefficients reaching 0.84 and 0.92 in upstream and downstream sections. The results demonstrate its effectiveness in integrating system hydrodynamics with data patterns, offering a robust tool for real-time decision support in water resource management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106699"},"PeriodicalIF":4.6,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109560","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

Pitfalls of XAI interpretation in environmental modeling: A warning on model bias in air quality data analysis 环境建模中XAI解释的陷阱：对空气质量数据分析中模型偏差的警告

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-09-19 DOI: 10.1016/j.envsoft.2025.106700

Souichi Oka , Takuma Yamazaki , Yoshiyasu Takefuji

{"title":"Pitfalls of XAI interpretation in environmental modeling: A warning on model bias in air quality data analysis","authors":"Souichi Oka , Takuma Yamazaki , Yoshiyasu Takefuji","doi":"10.1016/j.envsoft.2025.106700","DOIUrl":"10.1016/j.envsoft.2025.106700","url":null,"abstract":"<div><div>Jung et al. (2025) achieved high predictive accuracy in interpolating missing ozone data using graph machine learning (ML) and conducted feature importance analysis with explainable AI (XAI). This correspondence acknowledges their significant contribution but discusses the limitations and biases inherent in ML models and XAI methods (e.g., Random Forest/Bootstrap Test, SHapley Additive exPlanations (SHAP)) and their impact on the reliability of derived feature importance. High predictive accuracy does not necessarily guarantee trustworthy interpretation of feature relevance, as evidenced by inconsistent importance rankings across models and XAI techniques. To enhance interpretability and scientific reliability, we advocate a validation strategy integrating ML with rigorous statistical analysis. It combines model-driven insights with statistical measures such as Spearman's rho and Kendall's tau, and information-theoretic metrics like Mutual Information and Total Correlation to capture complex, non-linear dependencies. Such integration improves the robustness of feature importance assessments and supports more reliable interpretations in environmental modeling.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106700"},"PeriodicalIF":4.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109561","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

Latent diffusion model for quantitative precipitation estimation and forecast at km scale 千米尺度降水定量估计和预报的潜在扩散模式

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-09-19 DOI: 10.1016/j.envsoft.2025.106701

Weidong Li , Baoxiang Pan , Tiejian Li , Congyi Nai , Zhaoxi Li , Jie Chao , Bo Lu , Qingyun Duan , Ming Pan

{"title":"Latent diffusion model for quantitative precipitation estimation and forecast at km scale","authors":"Weidong Li , Baoxiang Pan , Tiejian Li , Congyi Nai , Zhaoxi Li , Jie Chao , Bo Lu , Qingyun Duan , Ming Pan","doi":"10.1016/j.envsoft.2025.106701","DOIUrl":"10.1016/j.envsoft.2025.106701","url":null,"abstract":"<div><div>Accurate high-resolution precipitation estimation remains a significant challenge in weather prediction due to computational limitations and sub-grid process parameterization difficulties. We present a latent diffusion modeling (LDM) framework that estimates 4 km resolution precipitation using 25 km resolution atmospheric and topographic inputs. The LDM transforms precipitation data into a compact Quasi-Gaussian latent space and progressively refines predictions through neural network-guided diffusion, effectively avoiding common deep learning issues such as mode collapse and blurry artifacts. Compared to traditional numerical models and other deep learning approaches, LDM achieves superior performance with over 30 % reduction in root mean squared error and 40 % improvement in critical success index for extreme events. For the extreme precipitation event (>300 mm/d) in California on October 25, 2021, LDM maintained effective 7-day forecast skill using circulation predictions from a data-driven weather forecasting model. The framework demonstrates significant potential for operational weather prediction applications.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106701"},"PeriodicalIF":4.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155451","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 framework for the acceptance testing of geospatial search engines 地理空间搜索引擎验收测试框架

IF 4.6 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-09-18 DOI: 10.1016/j.envsoft.2025.106692

Dagoberto José Herrera-Murillo , Javier Nogueras-Iso , Paloma Abad-Power , Miguel Á. Latre , Francisco J. Lopez-Pellicer

{"title":"A framework for the acceptance testing of geospatial search engines","authors":"Dagoberto José Herrera-Murillo , Javier Nogueras-Iso , Paloma Abad-Power , Miguel Á. Latre , Francisco J. Lopez-Pellicer","doi":"10.1016/j.envsoft.2025.106692","DOIUrl":"10.1016/j.envsoft.2025.106692","url":null,"abstract":"<div><div>Geospatial search engines are an essential component of spatial data infrastructures and enable a broad spectrum of environmental applications. The back-end implementation of these search engines has evolved from traditional text-based information retrieval systems into more specialised search engines. However, to assess the actual improvement brought by this evolution, thorough testing is needed. The aim of this work is to propose a framework for the acceptance testing of geospatial search engines that assesses their functionality, effectiveness, and user-friendliness. For each quality attribute, the framework proposes different testing design techniques and guidelines for their practical implementation. To demonstrate its feasibility, it has been applied to the evaluation of a geospatial semantic search engine of the Spanish National Geographic Institute. The evaluated search engine showed a sufficient level of functionality and effectiveness. However, the usability results were barely satisfactory due to perceived problems associated with complexity, inconsistency, and low learnability.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"194 ","pages":"Article 106692"},"PeriodicalIF":4.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118784","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