Pub Date : 2025-11-24DOI: 10.1016/j.envsoft.2025.106800
S.J.H.A. Gradussen , A.L. de Jongste , V. Chavarrías
When modelling the morphological response to river interventions, deterministic discharge time series are commonly used as a cost-effective alternative for approximating the most likely morphological changes resulting from Monte Carlo (MC) analyses. The conventional deterministic approach cycles long-term discharge statistics through a Cycled Annual Hydrograph (CyAH), neglecting inter-annual discharge variability. We introduce a new deterministic method, the Multiple Annual Hydrograph (MuAH), that accounts for such inter-annual discharge variations. Using a one-dimensional model, we simulate morphodynamic changes resulting from a hypothetical intervention in an alluvial river to demonstrate the application of these deterministic time series and to evaluate their performance. We find that MuAH time series result in both long-term (quasi-static) evolution and seasonal (dynamic) morphological changes that more closely match MC results and natural discharge time series, compared with the CyAH approach. This enables more accurate assessments of morphological change induced by river interventions when using deterministic time series.
{"title":"Deterministic discharge time series for morphodynamic assessments of river interventions","authors":"S.J.H.A. Gradussen , A.L. de Jongste , V. Chavarrías","doi":"10.1016/j.envsoft.2025.106800","DOIUrl":"10.1016/j.envsoft.2025.106800","url":null,"abstract":"<div><div>When modelling the morphological response to river interventions, deterministic discharge time series are commonly used as a cost-effective alternative for approximating the most likely morphological changes resulting from Monte Carlo (MC) analyses. The conventional deterministic approach cycles long-term discharge statistics through a Cycled Annual Hydrograph (CyAH), neglecting inter-annual discharge variability. We introduce a new deterministic method, the Multiple Annual Hydrograph (MuAH), that accounts for such inter-annual discharge variations. Using a one-dimensional model, we simulate morphodynamic changes resulting from a hypothetical intervention in an alluvial river to demonstrate the application of these deterministic time series and to evaluate their performance. We find that MuAH time series result in both long-term (quasi-static) evolution and seasonal (dynamic) morphological changes that more closely match MC results and natural discharge time series, compared with the CyAH approach. This enables more accurate assessments of morphological change induced by river interventions when using deterministic time series.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106800"},"PeriodicalIF":4.6,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593069","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 : 2025-11-22DOI: 10.1016/j.envsoft.2025.106796
Zhaocai Wang , Cheng Ding , Nannan Xu , Weilong Wang , Xingxing Zhang
Accurate streamflow forecasts are critically important for monitoring flood disasters and managing water resources. The factors influencing streamflow are complex, characterized by significant non-linearity and intricacy. Developing a data-driven hybrid deep learning model for streamflow prediction represents an effective strategy. Consequently, this study introduces an enhanced deep learning model, named CEEMDAN-ISMA-CNN-LSTM-AM-RF (CICLAR), for predicting both streamflow and extreme flood events. This study integrates multi-source heterogeneous data, including remote sensing, meteorological, hydrological, and streamflow data. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is utilized to reduce the complexity, and then multi-source data are input into the CNN-LSTM-AM model. Additionally, the Improved Slime Mould Algorithm (ISMA) is employed to optimize the neural network's hyperparameters. Finally, Random Forest (RF) is used to perform non-linear summation. The study conducted daily streamflow predictions at 11 stations located in the upstream, midstream, and downstream sections of the Jialing River in China, demonstrating that the CICLAR model significantly outperforms other benchmark models. Taking the Beibei Hydrological Station as an example, compared to the conventional Long Short-Term Memory (LSTM) model, the Nash-Sutcliffe Efficiency Coefficient (NSE) of the CICLAR model's prediction results increased by 30 %, and the Mean Absolute Error (MAE) decreased by 75 %. For extreme flood forecasting, compared to the LSTM, the CICLAR model reduced the Mean Relative Error (MRE) by 0.86 and improved the Qualification Rate (QR) by 150 %. The results of this study show that the CICLAR model has significant application value in extreme flood forecasting and water resources management.
{"title":"Interpretable deep learning hybrid streamflow prediction modeling based on multi-source data fusion","authors":"Zhaocai Wang , Cheng Ding , Nannan Xu , Weilong Wang , Xingxing Zhang","doi":"10.1016/j.envsoft.2025.106796","DOIUrl":"10.1016/j.envsoft.2025.106796","url":null,"abstract":"<div><div>Accurate streamflow forecasts are critically important for monitoring flood disasters and managing water resources. The factors influencing streamflow are complex, characterized by significant non-linearity and intricacy. Developing a data-driven hybrid deep learning model for streamflow prediction represents an effective strategy. Consequently, this study introduces an enhanced deep learning model, named CEEMDAN-ISMA-CNN-LSTM-AM-RF (CICLAR), for predicting both streamflow and extreme flood events. This study integrates multi-source heterogeneous data, including remote sensing, meteorological, hydrological, and streamflow data. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is utilized to reduce the complexity, and then multi-source data are input into the CNN-LSTM-AM model. Additionally, the Improved Slime Mould Algorithm (ISMA) is employed to optimize the neural network's hyperparameters. Finally, Random Forest (RF) is used to perform non-linear summation. The study conducted daily streamflow predictions at 11 stations located in the upstream, midstream, and downstream sections of the Jialing River in China, demonstrating that the CICLAR model significantly outperforms other benchmark models. Taking the Beibei Hydrological Station as an example, compared to the conventional Long Short-Term Memory (LSTM) model, the Nash-Sutcliffe Efficiency Coefficient (NSE) of the CICLAR model's prediction results increased by 30 %, and the Mean Absolute Error (MAE) decreased by 75 %. For extreme flood forecasting, compared to the LSTM, the CICLAR model reduced the Mean Relative Error (MRE) by 0.86 and improved the Qualification Rate (QR) by 150 %. The results of this study show that the CICLAR model has significant application value in extreme flood forecasting and water resources management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106796"},"PeriodicalIF":4.6,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575521","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 : 2025-11-22DOI: 10.1016/j.envsoft.2025.106780
Shiqi Zhang , Peihao Peng , Ning Li , Juan Wang , Xuefeng Peng , Zhaozhi Luo
Fine-scale vegetation zoning of evergreen broad-leaved forests (EBFs) is essential for ecological understanding and forest management, yet expert-led schemes often under-represent environmental complexity. This study proposes a multi-source modelling framework that bridges vegetation classification and zonation, demonstrated in Sichuan Province, China. Vegetation classification data for primary units were derived from Landsat-8 and Sentinel-1 imagery using a hierarchical multi-label network, and for secondary units from kernel density estimation of constructive species. A vegetation–environment relationship model then quantifies the influence of climate, topography and soil, converting discrete classes into the continuous indicators required for zoning. Finally, zoning thresholds were defined using confidence interval and Jenks natural breaks, and boundaries refined through upscaling and Gaussian filtering. The resulting map delineates three vegetation areas and fifteen vegetation districts, capturing spatial heterogeneity across the Sichuan Basin-Tibetan Plateau ecotone. The framework provides a replicable tool for vegetation zoning in complex mountain systems.
{"title":"From vegetation classification to vegetation zonation: a multi-source modelling framework for evergreen broad-leaved forests in complex terrain","authors":"Shiqi Zhang , Peihao Peng , Ning Li , Juan Wang , Xuefeng Peng , Zhaozhi Luo","doi":"10.1016/j.envsoft.2025.106780","DOIUrl":"10.1016/j.envsoft.2025.106780","url":null,"abstract":"<div><div>Fine-scale vegetation zoning of evergreen broad-leaved forests (EBFs) is essential for ecological understanding and forest management, yet expert-led schemes often under-represent environmental complexity. This study proposes a multi-source modelling framework that bridges vegetation classification and zonation, demonstrated in Sichuan Province, China. Vegetation classification data for primary units were derived from Landsat-8 and Sentinel-1 imagery using a hierarchical multi-label network, and for secondary units from kernel density estimation of constructive species. A vegetation–environment relationship model then quantifies the influence of climate, topography and soil, converting discrete classes into the continuous indicators required for zoning. Finally, zoning thresholds were defined using confidence interval and Jenks natural breaks, and boundaries refined through upscaling and Gaussian filtering. The resulting map delineates three vegetation areas and fifteen vegetation districts, capturing spatial heterogeneity across the Sichuan Basin-Tibetan Plateau ecotone. The framework provides a replicable tool for vegetation zoning in complex mountain systems.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"197 ","pages":"Article 106780"},"PeriodicalIF":4.6,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575240","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}
Nature-based solutions (NBS) have emerged as sustainable approaches to urban water management, addressing critical challenges like wastewater treatment and stormwater management while delivering additional environmental and social benefits. A significant advantage of NBS is their potential to decentralize urban water management, enabling cities to distribute water treatment and storage systems across multiple locations, thereby alleviating pressure on traditional centralized infrastructure. However, the wide array of NBS options, each tailored to specific contexts, presents a considerable challenge for decision-makers. The Nat4Wat decision-support system (DSS) was developed to help stakeholders select, compare, and evaluate NBS options for wastewater treatment and stormwater management. Nat4Wat streamlines decision-making by integrating a transparent multicriteria evaluation with an open knowledge base that links performance, costs, and cobenefits. By integrating multicriteria decision analysis (MCDA), Nat4Wat evaluates factors —such as cost-effectiveness, environmental performance, operational requirements, and social benefits— guiding users toward the most suitable and sustainable NBS for their specific needs. This paper details the co-development of Nat4Wat, in collaboration with technology providers and decision-makers, showcasing its application in two case studies: wastewater reuse at a rural hotel and stormwater mitigation in an urban area. These examples demonstrate how the tool streamlines decision-making, enhances transparency, and fosters stakeholder participation. As urban areas face increasing water-related challenges driven by climate change and population growth, Nat4Wat serves as a relevant resource for integrating NBS into resilient and sustainable urban water management strategies.
{"title":"Nat4Wat: a co-developed decision-support system for resilient urban water management with nature-based solutions","authors":"Josep Pueyo-Ros , Esther Mendoza , Gianluigi Buttiglieri , Joaquim Comas","doi":"10.1016/j.envsoft.2025.106797","DOIUrl":"10.1016/j.envsoft.2025.106797","url":null,"abstract":"<div><div>Nature-based solutions (NBS) have emerged as sustainable approaches to urban water management, addressing critical challenges like wastewater treatment and stormwater management while delivering additional environmental and social benefits. A significant advantage of NBS is their potential to decentralize urban water management, enabling cities to distribute water treatment and storage systems across multiple locations, thereby alleviating pressure on traditional centralized infrastructure. However, the wide array of NBS options, each tailored to specific contexts, presents a considerable challenge for decision-makers. The Nat4Wat decision-support system (DSS) was developed to help stakeholders select, compare, and evaluate NBS options for wastewater treatment and stormwater management. Nat4Wat streamlines decision-making by integrating a transparent multicriteria evaluation with an open knowledge base that links performance, costs, and cobenefits. By integrating multicriteria decision analysis (MCDA), Nat4Wat evaluates factors —such as cost-effectiveness, environmental performance, operational requirements, and social benefits— guiding users toward the most suitable and sustainable NBS for their specific needs. This paper details the co-development of Nat4Wat, in collaboration with technology providers and decision-makers, showcasing its application in two case studies: wastewater reuse at a rural hotel and stormwater mitigation in an urban area. These examples demonstrate how the tool streamlines decision-making, enhances transparency, and fosters stakeholder participation. As urban areas face increasing water-related challenges driven by climate change and population growth, Nat4Wat serves as a relevant resource for integrating NBS into resilient and sustainable urban water management strategies.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106797"},"PeriodicalIF":4.6,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575238","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 : 2025-11-20DOI: 10.1016/j.envsoft.2025.106789
Luciano Ortenzi , Jacopo Aguzzi , Damianos Chatzievangelou , Eugenio Nerio Nemmi , Michele Ferrari , Ivan Masmitja , Morane Clavel-Henry , Nixon Bahamon , Nathan J. Robinson , Giacomo Picardi , Paula Espina , Simona Violino , Riccardo De Angelis , Simone Figorilli , Lavinia Moscovini , Matteo Gallici , Francesca Antonucci , Alessandro Mei , Corrado Costa
To meet the needs of the future, marine environmental monitoring must develop methods to efficiently combine and utilise data from a diverse range of sources (e.g., satellite imagery, sensor networks, acoustic data). Generative Artificial Intelligence (GenAI) is uniquely suited to aid with this by enabling the synthesis and integration of heterogeneous and often incomplete data. Its ability to learn underlying statistical patterns supports data fusion, imputation, and enhanced interpretation across sources. GenAI also introduces novel modelling approaches to tackle ecological uncertainties and improve predictive insight. Here, we present a comprehensive overview of GenAI applications in marine ecological monitoring, emphasising its potential to improve data quality control, automate species identification, and support the creation of digital twins. We also highlight key research challenges, such as managing model bias and ensuring system transparency, and outline future directions for integrating GenAI into sustainable marine ecological monitoring and management.
{"title":"Generative artificial intelligence and marine ecological monitoring","authors":"Luciano Ortenzi , Jacopo Aguzzi , Damianos Chatzievangelou , Eugenio Nerio Nemmi , Michele Ferrari , Ivan Masmitja , Morane Clavel-Henry , Nixon Bahamon , Nathan J. Robinson , Giacomo Picardi , Paula Espina , Simona Violino , Riccardo De Angelis , Simone Figorilli , Lavinia Moscovini , Matteo Gallici , Francesca Antonucci , Alessandro Mei , Corrado Costa","doi":"10.1016/j.envsoft.2025.106789","DOIUrl":"10.1016/j.envsoft.2025.106789","url":null,"abstract":"<div><div>To meet the needs of the future, marine environmental monitoring must develop methods to efficiently combine and utilise data from a diverse range of sources (e.g., satellite imagery, sensor networks, acoustic data). Generative Artificial Intelligence (GenAI) is uniquely suited to aid with this by enabling the synthesis and integration of heterogeneous and often incomplete data. Its ability to learn underlying statistical patterns supports data fusion, imputation, and enhanced interpretation across sources. GenAI also introduces novel modelling approaches to tackle ecological uncertainties and improve predictive insight. Here, we present a comprehensive overview of GenAI applications in marine ecological monitoring, emphasising its potential to improve data quality control, automate species identification, and support the creation of digital twins. We also highlight key research challenges, such as managing model bias and ensuring system transparency, and outline future directions for integrating GenAI into sustainable marine ecological monitoring and management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106789"},"PeriodicalIF":4.6,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560063","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 : 2025-11-19DOI: 10.1016/j.envsoft.2025.106771
Daniel Velez-Serrano , Alejandro Alvaro-Meca
This study presents a novel deep learning-based model, the Improved Spatio-Temporal Graph Transformer (ISTGT), designed for accurate municipal-level PM2.5 forecasting across Spain. ISTGT integrates Graph Convolutional Networks, Temporal Convolutional Networks, and Transformer Encoders to capture complex spatial relationships and temporal dependencies. An adaptive spatial graph, constructed using Delaunay triangulation, incorporates distance, altitude, and population density to enhance prediction accuracy. Historical data — including air quality, meteorological factors, elevation, population, and public holidays — from 8,076 municipalities facilitated detailed predictions and extrapolation onto a fine-resolution spatial grid (0.1° × 0.1°). Combining ISTGT with ARIMA predictions using a CatBoost stacking approach significantly reduced mean absolute error (MAE) to 1.24, outperforming traditional and hybrid models. The proposed method offers computational efficiency, precise spatial extrapolation, and adaptability to other spatio-temporal tasks, providing a valuable tool for environmental management. Future work may integrate real-time meteorological and satellite data to improve predictions during extreme conditions.
{"title":"Adaptive Graph Neural Network–transformer model for high-resolution PM2.5 forecasting and spatial extrapolation","authors":"Daniel Velez-Serrano , Alejandro Alvaro-Meca","doi":"10.1016/j.envsoft.2025.106771","DOIUrl":"10.1016/j.envsoft.2025.106771","url":null,"abstract":"<div><div>This study presents a novel deep learning-based model, the Improved Spatio-Temporal Graph Transformer (ISTGT), designed for accurate municipal-level PM<sub>2.5</sub> forecasting across Spain. ISTGT integrates Graph Convolutional Networks, Temporal Convolutional Networks, and Transformer Encoders to capture complex spatial relationships and temporal dependencies. An adaptive spatial graph, constructed using Delaunay triangulation, incorporates distance, altitude, and population density to enhance prediction accuracy. Historical data — including air quality, meteorological factors, elevation, population, and public holidays — from 8,076 municipalities facilitated detailed predictions and extrapolation onto a fine-resolution spatial grid (0.1° × 0.1°). Combining ISTGT with ARIMA predictions using a CatBoost stacking approach significantly reduced mean absolute error (MAE) to 1.24, outperforming traditional and hybrid models. The proposed method offers computational efficiency, precise spatial extrapolation, and adaptability to other spatio-temporal tasks, providing a valuable tool for environmental management. Future work may integrate real-time meteorological and satellite data to improve predictions during extreme conditions.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106771"},"PeriodicalIF":4.6,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560062","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 : 2025-11-19DOI: 10.1016/j.envsoft.2025.106791
Fan Liu, Zhao Guo, Chen Ma, Futian Ren, Zenghui Li, Xiaowei Lu, Lei Huang
An AI-enhanced, cloud-native platform for groundwater management integrates physics-based simulation, data-driven surrogates, and Bayesian uncertainty quantification. The framework couples MODFLOW-6 with a Random-Forest (RF) surrogate and a prototype Physics-Informed Neural Network (PINN), supporting ensemble calibration (PyEMU) and surrogate-driven probabilistic inference. In an industrial-park application, the calibrated MF6 reproduced observed heads (RMSE 0.40; MAE 0.32; NSE 0.84). The RF surrogate maintained high fidelity (validation NSE 0.78) with reduced computational cost, while the PINN enforced physical constraints but showed lower pointwise accuracy. Both inference methods identified hydraulic conductivity as the dominant sensitive parameter and provided credible intervals and exceedance probabilities for risk assessment. A web interface enables data ingestion, model setup, scenario exploration, and uncertainty-aware visualization, including 3D flow/plume, residual maps, and time-series warnings. This platform offers a reproducible, scalable, and physically consistent pathway for operational groundwater decision support and future enhancements such as neural operators and reactive transport modeling.
人工智能增强的地下水管理云原生平台集成了基于物理的模拟、数据驱动的替代和贝叶斯不确定性量化。该框架将MODFLOW-6与随机森林（RF）代理和原型物理信息神经网络（PINN）耦合在一起，支持集成校准（PyEMU）和代理驱动的概率推理。在一个工业园区的应用中，校准后的MF6再现了观察到的头部（RMSE 0.40; MAE 0.32; NSE 0.84）。RF代理保持了高保真度（验证NSE 0.78）并降低了计算成本，而PINN强制物理约束但显示出较低的点精度。两种推理方法均将导电性作为主导敏感参数，并为风险评估提供可信区间和超出概率。web界面支持数据摄取、模型设置、场景探索和不确定性感知可视化，包括3D流/羽流、残余地图和时间序列警告。该平台为地下水作业决策支持和未来的增强功能（如神经算子和反应性输运建模）提供了可复制、可扩展和物理一致的途径。
{"title":"AI-enhanced groundwater management platform: A network-driven approach for simulation","authors":"Fan Liu, Zhao Guo, Chen Ma, Futian Ren, Zenghui Li, Xiaowei Lu, Lei Huang","doi":"10.1016/j.envsoft.2025.106791","DOIUrl":"10.1016/j.envsoft.2025.106791","url":null,"abstract":"<div><div>An AI-enhanced, cloud-native platform for groundwater management integrates physics-based simulation, data-driven surrogates, and Bayesian uncertainty quantification. The framework couples MODFLOW-6 with a Random-Forest (RF) surrogate and a prototype Physics-Informed Neural Network (PINN), supporting ensemble calibration (PyEMU) and surrogate-driven probabilistic inference. In an industrial-park application, the calibrated MF6 reproduced observed heads (RMSE 0.40; MAE 0.32; NSE 0.84). The RF surrogate maintained high fidelity (validation NSE 0.78) with reduced computational cost, while the PINN enforced physical constraints but showed lower pointwise accuracy. Both inference methods identified hydraulic conductivity as the dominant sensitive parameter and provided credible intervals and exceedance probabilities for risk assessment. A web interface enables data ingestion, model setup, scenario exploration, and uncertainty-aware visualization, including 3D flow/plume, residual maps, and time-series warnings. This platform offers a reproducible, scalable, and physically consistent pathway for operational groundwater decision support and future enhancements such as neural operators and reactive transport modeling.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106791"},"PeriodicalIF":4.6,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145553967","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 : 2025-11-19DOI: 10.1016/j.envsoft.2025.106795
Huili Wang , Bin Xu , Xinman Qin , Xinrong Wang , Jianyun Zhang , Guoqing Wang , Fubao Yang , Ping-an Zhong , Ran Mo , Xuesong Yang
Traditional methods for simulating reservoir scheduling rule face challenges in reducing spatiotemporal errors and improving Pareto frontier simulation quality for multi-objective optimization. This study proposes a spatiotemporal correction of decision variables technique using XGBoost (SC-XGB) to extract intelligent multi-objective scheduling rules. A two-stage scheduling rule framework is designed to reduce model complexity, and a spatiotemporal correction loss function is introduced to mitigate cumulative water balance constraint violation errors. Bayesian optimization with cross-validation is employed for hyperparameter tuning, and a multi-metric evaluation system is established. Case study results from the Chaohu Basin, China, show that the SC-XGB improves the average NSE of outflow prediction by 1.89 %, reduces the Water Balance Mean Error range of Chaohu Lake by 27.93 %, and decreases the Relative Hypervolume Error by 21.51 % compared to the XGB model. These findings demonstrate that the SC-XGB model enhances both accuracy and generalization, thereby supporting intelligent scheduling in flood management systems.
{"title":"Spatiotemporal correction of decision variables using XGBoost for multi-objective intelligent scheduling rule extraction model in reservoir-lake flood control systems","authors":"Huili Wang , Bin Xu , Xinman Qin , Xinrong Wang , Jianyun Zhang , Guoqing Wang , Fubao Yang , Ping-an Zhong , Ran Mo , Xuesong Yang","doi":"10.1016/j.envsoft.2025.106795","DOIUrl":"10.1016/j.envsoft.2025.106795","url":null,"abstract":"<div><div>Traditional methods for simulating reservoir scheduling rule face challenges in reducing spatiotemporal errors and improving Pareto frontier simulation quality for multi-objective optimization. This study proposes a spatiotemporal correction of decision variables technique using XGBoost (SC-XGB) to extract intelligent multi-objective scheduling rules. A two-stage scheduling rule framework is designed to reduce model complexity, and a spatiotemporal correction loss function is introduced to mitigate cumulative water balance constraint violation errors. Bayesian optimization with cross-validation is employed for hyperparameter tuning, and a multi-metric evaluation system is established. Case study results from the Chaohu Basin, China, show that the SC-XGB improves the average NSE of outflow prediction by 1.89 %, reduces the Water Balance Mean Error range of Chaohu Lake by 27.93 %, and decreases the Relative Hypervolume Error by 21.51 % compared to the XGB model. These findings demonstrate that the SC-XGB model enhances both accuracy and generalization, thereby supporting intelligent scheduling in flood management systems.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106795"},"PeriodicalIF":4.6,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560061","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 : 2025-11-19DOI: 10.1016/j.envsoft.2025.106790
Chengxin Qin , Fu Sun , Yi Rong , Wanbin Wang , Xingzi Zhang , Yihui Chen , Yi Liu
Model evaluation is crucial for verifying model credibility, especially in decision-making. Successful environmental modelling requires not only self-proved credibility from model developers/users and peer-appraised credibility from technical experts, but also decision-maker and public confidence in model credibility. We propose a participatory model evaluation approach for environmental decisions, combining the standard evaluation procedure, data-augmented peer review and multi-stakeholder engagement. To facilitate this approach, we developed DPMODE (Decision Procedure Management of surface water mODel Evaluation), a web-based system with supporting tools and database. DPMODE evaluates surface water models and recommends credible models and customized test datasets for watershed management. A case study on the Soil and Water Assessment Tool (SWAT) for the Chishui River watershed management demonstrated the effectiveness of this approach. This participatory evaluation would be an adaptive, iterative process to improve stakeholder acceptance, enhance model-based outcomes, and foster better decision pathways.
{"title":"Developing a web-based participatory approach to model evaluation for environmental decision-making","authors":"Chengxin Qin , Fu Sun , Yi Rong , Wanbin Wang , Xingzi Zhang , Yihui Chen , Yi Liu","doi":"10.1016/j.envsoft.2025.106790","DOIUrl":"10.1016/j.envsoft.2025.106790","url":null,"abstract":"<div><div>Model evaluation is crucial for verifying model credibility, especially in decision-making. Successful environmental modelling requires not only self-proved credibility from model developers/users and peer-appraised credibility from technical experts, but also decision-maker and public confidence in model credibility. We propose a participatory model evaluation approach for environmental decisions, combining the standard evaluation procedure, data-augmented peer review and multi-stakeholder engagement. To facilitate this approach, we developed DPMODE (Decision Procedure Management of surface water mODel Evaluation), a web-based system with supporting tools and database. DPMODE evaluates surface water models and recommends credible models and customized test datasets for watershed management. A case study on the Soil and Water Assessment Tool (SWAT) for the Chishui River watershed management demonstrated the effectiveness of this approach. This participatory evaluation would be an adaptive, iterative process to improve stakeholder acceptance, enhance model-based outcomes, and foster better decision pathways.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106790"},"PeriodicalIF":4.6,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145553968","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 : 2025-11-19DOI: 10.1016/j.envsoft.2025.106788
Fletcher T. Chapin , Yin-Li Liu , Meagan S. Mauter
Digital twins and other digital solutions are transforming the planning, design, operation, and maintenance of water assets. Implementing these solutions is often slowed by data management activities including cleaning, storage, and querying. We identify three limitations of existing data management platforms: data inaccessibility, inadequate integration of data and metadata, and the absence of embedded data analysis capabilities. We introduce Python for Process Engineering Schema (PyPES), an object-oriented, open-source schema for water data management, to address these shortcomings. Next, we demonstrate PyPES implementation across three distinct water asset classes (water distribution, reverse osmosis, and wastewater treatment) and applications (leakage detection, optimal sensor placement, and automated fault detection). In each case study, we highlight how novel features of PyPES increase the value and portability of these models relative to state-of-the-art approaches. Finally, we describe opportunities for integrating PyPES with a data ontology to enhance the power of this software.
数字孪生和其他数字解决方案正在改变水资产的规划、设计、运营和维护。数据管理活动（包括清理、存储和查询）通常会减慢实现这些解决方案的速度。我们确定了现有数据管理平台的三个局限性：数据不可访问、数据和元数据集成不足以及缺乏嵌入式数据分析功能。我们介绍了Python for Process Engineering Schema (PyPES)，这是一种面向对象的、用于水数据管理的开源模式，以解决这些缺点。接下来，我们将演示在三种不同的水资产类别（配水、反渗透和废水处理）和应用（泄漏检测、最佳传感器放置和自动故障检测）中实现PyPES。在每个案例研究中，我们强调了PyPES的新特性如何提高这些模型相对于最先进方法的价值和可移植性。最后，我们描述了将PyPES与数据本体集成以增强该软件功能的机会。
{"title":"PyPES: A data and metadata schema for portable water system models","authors":"Fletcher T. Chapin , Yin-Li Liu , Meagan S. Mauter","doi":"10.1016/j.envsoft.2025.106788","DOIUrl":"10.1016/j.envsoft.2025.106788","url":null,"abstract":"<div><div>Digital twins and other digital solutions are transforming the planning, design, operation, and maintenance of water assets. Implementing these solutions is often slowed by data management activities including cleaning, storage, and querying. We identify three limitations of existing data management platforms: data inaccessibility, inadequate integration of data and metadata, and the absence of embedded data analysis capabilities. We introduce Python for Process Engineering Schema (PyPES), an object-oriented, open-source schema for water data management, to address these shortcomings. Next, we demonstrate PyPES implementation across three distinct water asset classes (water distribution, reverse osmosis, and wastewater treatment) and applications (leakage detection, optimal sensor placement, and automated fault detection). In each case study, we highlight how novel features of PyPES increase the value and portability of these models relative to state-of-the-art approaches. Finally, we describe opportunities for integrating PyPES with a data ontology to enhance the power of this software.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"196 ","pages":"Article 106788"},"PeriodicalIF":4.6,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145553963","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}
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