Pub Date : 2024-05-20DOI: 10.1016/j.envsoft.2024.106083
Rajani Pandey , G R Jayanth , M.S Mohan Kumar
Linear control-oriented models are important to represent canal dynamics for designing controllers. This study focuses on hydraulic control structure (gate) modelling to address the complex interdependent behavior inherent in irrigation canals. A comprehensive mathematical model that incorporates the water level with gate-opening to model discharge is introduced for single and multiple canal pool scenarios. The proposed model captures the hydraulic coupling within and among canal pools, a key finding. The model is evaluated extensively under uniform and non-uniform flows across three distinct canals, highlighting the model's applicability to various systems. The uncertainty inherent within the nominal model is also assessed for varying operating conditions and hydraulic parameters. The proposed model is compared with the existing and the Saint-Venant (SV) model, showing improved accuracy in water-level predictions. This advancement in hydraulic modelling contributes to adaptable canal models essential in developing robust controllers to enhance water management in irrigation canals.
{"title":"Comprehensive mathematical model for efficient and robust control of irrigation canals","authors":"Rajani Pandey , G R Jayanth , M.S Mohan Kumar","doi":"10.1016/j.envsoft.2024.106083","DOIUrl":"10.1016/j.envsoft.2024.106083","url":null,"abstract":"<div><p>Linear control-oriented models are important to represent canal dynamics for designing controllers. This study focuses on hydraulic control structure (gate) modelling to address the complex interdependent behavior inherent in irrigation canals. A comprehensive mathematical model that incorporates the water level with gate-opening to model discharge is introduced for single and multiple canal pool scenarios. The proposed model captures the hydraulic coupling within and among canal pools, a key finding. The model is evaluated extensively under uniform and non-uniform flows across three distinct canals, highlighting the model's applicability to various systems. The uncertainty inherent within the nominal model is also assessed for varying operating conditions and hydraulic parameters. The proposed model is compared with the existing and the Saint-Venant (SV) model, showing improved accuracy in water-level predictions. This advancement in hydraulic modelling contributes to adaptable canal models essential in developing robust controllers to enhance water management in irrigation canals.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141137809","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 : 2024-05-19DOI: 10.1016/j.envsoft.2024.106072
Eric Chen , Martin S. Andersen , Rohitash Chandra
Although traditional physical models have been used to analyse groundwater systems, the emergence of novel machine learning models can improve the accuracy of the predictions. Deep learning has been prominent in environmental and climate change problems. In this paper, we present a framework for utilising deep learning models to predict groundwater levels based on nearby streamflow and rainfall data. We address the missing data problem using a Bayesian linear regression model within the deep learning framework. Our deep learning framework utilises models such as long-short term memory (LSTM) networks and convolutional neural networks (CNN) for multi-step ahead time series prediction. We examine the fluctuations in groundwater levels at various boreholes located near Middle Creek in New South Wales, Australia. We use the National Collaborative Research Infrastructure Strategy (NCRIS) groundwater database and utilise Bayesian linear regression to impute missing data. We investigate the accuracy of the selected models for individual and regional basins and univariate and multivariate strategies. Our results show that the LSTM-based regional model with multivariate strategy using rainfall data provided the best accuracy.
尽管传统的物理模型一直被用于分析地下水系统,但新型机器学习模型的出现可以提高预测的准确性。深度学习在环境和气候变化问题上表现突出。在本文中,我们提出了一个利用深度学习模型的框架,以根据附近的溪流和降雨数据预测地下水位。我们在深度学习框架内使用贝叶斯线性回归模型来解决数据缺失问题。我们的深度学习框架利用长短期记忆(LSTM)网络和卷积神经网络(CNN)等模型进行多步超前时间序列预测。我们研究了澳大利亚新南威尔士州 Middle Creek 附近多个钻孔的地下水位波动情况。我们使用了国家合作研究基础设施战略(NCRIS)地下水数据库,并利用贝叶斯线性回归来弥补缺失数据。我们研究了单个流域和区域流域所选模型的准确性,以及单变量和多变量策略。结果表明,基于 LSTM 的区域模型使用降雨数据的多变量策略提供了最佳精度。
{"title":"Deep learning framework with Bayesian data imputation for modelling and forecasting groundwater levels","authors":"Eric Chen , Martin S. Andersen , Rohitash Chandra","doi":"10.1016/j.envsoft.2024.106072","DOIUrl":"10.1016/j.envsoft.2024.106072","url":null,"abstract":"<div><p>Although traditional physical models have been used to analyse groundwater systems, the emergence of novel machine learning models can improve the accuracy of the predictions. Deep learning has been prominent in environmental and climate change problems. In this paper, we present a framework for utilising deep learning models to predict groundwater levels based on nearby streamflow and rainfall data. We address the missing data problem using a Bayesian linear regression model within the deep learning framework. Our deep learning framework utilises models such as <em>long-short term memory</em> (LSTM) networks and <em>convolutional neural networks</em> (CNN) for multi-step ahead time series prediction. We examine the fluctuations in groundwater levels at various boreholes located near Middle Creek in New South Wales, Australia. We use the National Collaborative Research Infrastructure Strategy (NCRIS) groundwater database and utilise Bayesian linear regression to impute missing data. We investigate the accuracy of the selected models for individual and regional basins and univariate and multivariate strategies. Our results show that the LSTM-based regional model with multivariate strategy using rainfall data provided the best accuracy.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141132076","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 : 2024-05-18DOI: 10.1016/j.envsoft.2024.106076
Cappelli Francesco , Simon Michael Papalexiou , Yannis Markonis , Salvatore Grimaldi
Simulation models are a fundamental tool for investigating hydrological processes and for water resource management. In this study, we introduce PyCoSMoS, a Python toolbox that enables researchers to simulate observed univariate time series mimicking hydroclimatic processes. This toolbox preserves arbitrary marginal distribution and autocorrelation functions, while significantly reducing computational burden. PyCoSMoS is built upon the mixed-Uniform CoSMoS method recently proposed by Papalexiou et al. (2023). The toolbox is designed to minimize the user’s input, requiring only observed time series, marginal distribution, correlation function, and the number of lags. The output provides both visual and quantitative comparisons between the observed and simulated time series. We evaluate the performance of the package using various synthetic case studies and the results demonstrate satisfactory accuracy. Furthermore, we apply the toolbox to three real case studies: precipitation, temperature, and relative humidity, for which the toolbox can successfully simulate the observed time series in each case.
{"title":"PyCoSMoS: An advanced toolbox for simulating real-world hydroclimatic data","authors":"Cappelli Francesco , Simon Michael Papalexiou , Yannis Markonis , Salvatore Grimaldi","doi":"10.1016/j.envsoft.2024.106076","DOIUrl":"10.1016/j.envsoft.2024.106076","url":null,"abstract":"<div><p>Simulation models are a fundamental tool for investigating hydrological processes and for water resource management. In this study, we introduce PyCoSMoS, a Python toolbox that enables researchers to simulate observed univariate time series mimicking hydroclimatic processes. This toolbox preserves arbitrary marginal distribution and autocorrelation functions, while significantly reducing computational burden. PyCoSMoS is built upon the mixed-Uniform CoSMoS method recently proposed by Papalexiou et al. (2023). The toolbox is designed to minimize the user’s input, requiring only observed time series, marginal distribution, correlation function, and the number of lags. The output provides both visual and quantitative comparisons between the observed and simulated time series. We evaluate the performance of the package using various synthetic case studies and the results demonstrate satisfactory accuracy. Furthermore, we apply the toolbox to three real case studies: precipitation, temperature, and relative humidity, for which the toolbox can successfully simulate the observed time series in each case.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815224001373/pdfft?md5=ef4bbfe85137e87765ba1d82ee40c107&pid=1-s2.0-S1364815224001373-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141141503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-15DOI: 10.1016/j.envsoft.2024.106074
Huiyuan Luo , Qiang Xu , Yan Cheng , Wanzhang Chen , Linfeng Zheng , Chuanhao Pu
Geological bodies prone to disasters, such as glaciers, landslides, and land subsidence, undergo three-dimensional (3-D) movement. Spaceborne Synthetic Aperture Radar (SAR) satellites commonly capture relative directional motion for Earth observation. However, this begs the question of how to track the 3-D movement of geological bodies. Presented here, the 3-D Deformation Inversion toolbox MATLAB-based concurrently processes ascending and descending SAR-derived datasets acquired from either Pixel Offset Tracking (POT) or Differential Interferometric SAR (DInSAR) methodology, in addition, generates long-term 3-D deformation and interactive point time series and line section information, also dynamic map visualizations. It is the ability to calculate the least squares solution using truncated or multi-order Tikhonov regularized Singular Value Decomposition (SVD). Three various scenarios are employed to assess processing capabilities. The L-curve method finds the optimal calculation parameters tailored to various objects. The toolbox's effectiveness and applicability enhance the potential for evolutionary dynamic analysis in geoscience.
{"title":"3-D deformation inversion: A MATLAB toolbox for automatically calculating SAR-derived 3-D deformation maps of glacier, landslide, and land subsidence","authors":"Huiyuan Luo , Qiang Xu , Yan Cheng , Wanzhang Chen , Linfeng Zheng , Chuanhao Pu","doi":"10.1016/j.envsoft.2024.106074","DOIUrl":"10.1016/j.envsoft.2024.106074","url":null,"abstract":"<div><p>Geological bodies prone to disasters, such as glaciers, landslides, and land subsidence, undergo three-dimensional (3-D) movement. Spaceborne Synthetic Aperture Radar (SAR) satellites commonly capture relative directional motion for Earth observation. However, this begs the question of how to track the 3-D movement of geological bodies. Presented here, the 3-D Deformation Inversion toolbox MATLAB-based concurrently processes ascending and descending SAR-derived datasets acquired from either Pixel Offset Tracking (POT) or Differential Interferometric SAR (DInSAR) methodology, in addition, generates long-term 3-D deformation and interactive point time series and line section information, also dynamic map visualizations. It is the ability to calculate the least squares solution using truncated or multi-order Tikhonov regularized Singular Value Decomposition (SVD). Three various scenarios are employed to assess processing capabilities. The L-curve method finds the optimal calculation parameters tailored to various objects. The toolbox's effectiveness and applicability enhance the potential for evolutionary dynamic analysis in geoscience.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141042085","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 : 2024-05-14DOI: 10.1016/j.envsoft.2024.106075
Amira Zaki , Ling Chang , Irene Manzella , Mark van der Meijde , Serkan Girgin , Hakan Tanyas , Islam Fadel
Detecting and monitoring surface deformation using radar satellite data is vital in geohazard assessment. Sentinel-1 has provided unprecedented spatial and temporal resolution, but data processing is complicated and poses computational challenges. Although software and tools exist, each with its own limitations. SNAP-ESA is notable for its user-friendly interface and stable performance in Interferometric Synthetic Aperture Radar (InSAR). However, SNAP-ESA lacks a flexible approach for generating interferometric time series stacks for Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques and faces computational challenges over large areas. Here, we present an automated Python workflow, SNAPWF, using SNAP-ESA to enable efficient PSI and SBAS interferometric time series stacks generation using flexible network graphs. SNAPWF has been implemented on a dedicated geospatial computing platform, enabling efficient performance over large areas. Results confirm its ability to generate PSI and SBAS interferometric stacks using full Sentinel-1 scenes and achieve results comparable to existing software.
{"title":"Automated Python workflow for generating Sentinel-1 PSI and SBAS interferometric stacks using SNAP on Geospatial Computing Platform","authors":"Amira Zaki , Ling Chang , Irene Manzella , Mark van der Meijde , Serkan Girgin , Hakan Tanyas , Islam Fadel","doi":"10.1016/j.envsoft.2024.106075","DOIUrl":"10.1016/j.envsoft.2024.106075","url":null,"abstract":"<div><p>Detecting and monitoring surface deformation using radar satellite data is vital in geohazard assessment. Sentinel-1 has provided unprecedented spatial and temporal resolution, but data processing is complicated and poses computational challenges. Although software and tools exist, each with its own limitations. SNAP-ESA is notable for its user-friendly interface and stable performance in Interferometric Synthetic Aperture Radar (InSAR). However, SNAP-ESA lacks a flexible approach for generating interferometric time series stacks for Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques and faces computational challenges over large areas. Here, we present an automated Python workflow, SNAPWF, using SNAP-ESA to enable efficient PSI and SBAS interferometric time series stacks generation using flexible network graphs. SNAPWF has been implemented on a dedicated geospatial computing platform, enabling efficient performance over large areas. Results confirm its ability to generate PSI and SBAS interferometric stacks using full Sentinel-1 scenes and achieve results comparable to existing software.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815224001361/pdfft?md5=f7fe0b3deabd74bccb47d4a3564ffa8d&pid=1-s2.0-S1364815224001361-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141053956","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}
Extreme events have the potential to significantly impact transportation infrastructure performance. For example, in the case of bridges, climate change impacts the river discharge, hence scouring patterns, which in turn, affects the bridge foundation stability. Therefore, extreme events (river flow) forecasting is mandatory in bridge reliability analysis. This paper approaches this river flow forecasting problem by developing a Markov-Switching Autoregressive model coupled with a conditional hidden seasonal Markov component. In addition, the proposed model is also combined with the deep machine learning neural networks method to forecast river flow from a dataset or from simulations. The proposed method is illustrated by using realistic data: historic river flow values of the Thames River. The results indicate that the proposed model well represented the extreme events within the dataset. In terms of river flow forecasting, the results indicate that the forecasts improve when the training period changes from 20 years to 40 years.
{"title":"Conditional seasonal markov-switching autoregressive model to simulate extreme events: Application to river flow","authors":"Bassel Habeeb , Emilio Bastidas-Arteaga , Mauricio Sánchez-Silva , You Dong","doi":"10.1016/j.envsoft.2024.106066","DOIUrl":"https://doi.org/10.1016/j.envsoft.2024.106066","url":null,"abstract":"<div><p>Extreme events have the potential to significantly impact transportation infrastructure performance. For example, in the case of bridges, climate change impacts the river discharge, hence scouring patterns, which in turn, affects the bridge foundation stability. Therefore, extreme events (river flow) forecasting is mandatory in bridge reliability analysis. This paper approaches this river flow forecasting problem by developing a Markov-Switching Autoregressive model coupled with a conditional hidden seasonal Markov component. In addition, the proposed model is also combined with the deep machine learning neural networks method to forecast river flow from a dataset or from simulations. The proposed method is illustrated by using realistic data: historic river flow values of the Thames River. The results indicate that the proposed model well represented the extreme events within the dataset. In terms of river flow forecasting, the results indicate that the forecasts improve when the training period changes from 20 years to 40 years.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140951440","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 : 2024-05-13DOI: 10.1016/j.envsoft.2024.106073
Camilla Negri , Per-Erik Mellander , Nicholas Schurch , Andrew J. Wade , Zisis Gagkas , Douglas H. Wardell-Johnson , Kerr Adams , Miriam Glendell
A Bayesian Belief Network was developed to simulate phosphorus (P) loss in an Irish agricultural catchment. Septic tanks and farmyards were included to represent all P sources and assess their effect on model performance. Bayesian priors were defined using daily discharge and turbidity, high-resolution soil P data, expert opinion, and literature. Calibration was done against seven years of daily Total Reactive P concentrations. Model performance was assessed using percentage bias, summary statistics, and visually comparing distributions. Bias was within acceptable ranges, the model predicted mean and median P concentrations within the data error, with simulated distributions more variable than the observations. Considering the risk of exceeding regulatory standards, predictions showed lower P losses than observations, likely due to simulated distributions being left-skewed. We discuss model advantages and limitations, the benefits of explicitly representing uncertainty, and priorities for data collection to fill knowledge gaps present even in a highly monitored catchment.
开发了贝叶斯信念网络来模拟爱尔兰农业集水区的磷(P)损失。其中包括化粪池和农田,以代表所有磷源并评估其对模型性能的影响。利用日排放量和浊度、高分辨率土壤磷数据、专家意见和文献资料定义了贝叶斯先验。根据七年的每日总活性 P 浓度进行校准。使用偏差百分比、汇总统计和直观比较分布来评估模型性能。偏差在可接受范围内,模型预测的 P 浓度平均值和中位数在数据误差范围内,模拟分布比观测值更多变。考虑到超过监管标准的风险,预测结果显示钾损失低于观测结果,这可能是由于模拟分布呈左偏型。我们讨论了模型的优势和局限性、明确表示不确定性的好处以及数据收集的优先次序,以填补即使在高度监测的流域中也存在的知识空白。
{"title":"Bayesian network modelling of phosphorus pollution in agricultural catchments with high-resolution data","authors":"Camilla Negri , Per-Erik Mellander , Nicholas Schurch , Andrew J. Wade , Zisis Gagkas , Douglas H. Wardell-Johnson , Kerr Adams , Miriam Glendell","doi":"10.1016/j.envsoft.2024.106073","DOIUrl":"10.1016/j.envsoft.2024.106073","url":null,"abstract":"<div><p>A Bayesian Belief Network was developed to simulate phosphorus (P) loss in an Irish agricultural catchment. Septic tanks and farmyards were included to represent all P sources and assess their effect on model performance. Bayesian priors were defined using daily discharge and turbidity, high-resolution soil P data, expert opinion, and literature. Calibration was done against seven years of daily Total Reactive P concentrations. Model performance was assessed using percentage bias, summary statistics, and visually comparing distributions. Bias was within acceptable ranges, the model predicted mean and median P concentrations within the data error, with simulated distributions more variable than the observations. Considering the risk of exceeding regulatory standards, predictions showed lower P losses than observations, likely due to simulated distributions being left-skewed. We discuss model advantages and limitations, the benefits of explicitly representing uncertainty, and priorities for data collection to fill knowledge gaps present even in a highly monitored catchment.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815224001348/pdfft?md5=825bb852ddc27f8d108f3fcb4a07a3a0&pid=1-s2.0-S1364815224001348-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141025264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-11DOI: 10.1016/j.envsoft.2024.106059
Jiachen Geng , Changxiu Cheng , Shi Shen , Kaixuan Dai , Tianyuan Zhang
Cellular automata (CA) based models are practical tools to simulate the spatiotemporal landscape evolution induced by the land use/-cover change (LUCC). Existing models are struggling to comprehensively handle the spatiotemporal driving relationships amid the nonlinear LUCC process. Besides, the landscape patterns are not considered in most models, making them struggled to support the development strategies. Aiming at overcoming these obstacles, a novel land use/-cover change model concerning spatiotemporal dependency and properties related to landscape evolution (STAPLE) is proposed in this paper. A potential generating module establishing the nonlinear spatiotemporal driving relationship and a spatial allocating module employing a landscape-based CA are integrated for realistic LUCC simulations. As a case study, the proposed model is applied in Zhengzhou, China to assess its performance. It is indicated that the STAPLE model achieves a higher simulation accuracy, and the landscape properties are effectively manipulated. It provides a reproducible tool for policy-makers to explore a low-ecological-risk landscape under different future scenarios and achieve sustainable developments.
基于细胞自动机(CA)的模型是模拟土地利用/覆盖变化(LUCC)引起的时空景观演变的实用工具。现有模型难以全面处理非线性 LUCC 过程中的时空驱动关系。此外,大多数模型都没有考虑景观模式,因此难以为发展战略提供支持。为了克服这些障碍,本文提出了一种新型土地利用/覆盖变化模型(STAPLE),该模型涉及时空依赖性和景观演变相关属性。建立非线性时空驱动关系的潜力生成模块和采用基于景观的 CA 的空间分配模块相结合,可用于现实的土地利用/覆被变化模拟。以中国郑州为例,对所提出的模型进行了性能评估。结果表明,STAPLE 模型实现了更高的模拟精度,景观属性也得到了有效控制。它为政策制定者提供了一个可重复的工具,以探索不同未来情景下的低生态风险景观,实现可持续发展。
{"title":"STAPLE: A land use/-cover change model concerning spatiotemporal dependency and properties related to landscape evolution","authors":"Jiachen Geng , Changxiu Cheng , Shi Shen , Kaixuan Dai , Tianyuan Zhang","doi":"10.1016/j.envsoft.2024.106059","DOIUrl":"10.1016/j.envsoft.2024.106059","url":null,"abstract":"<div><p>Cellular automata (CA) based models are practical tools to simulate the spatiotemporal landscape evolution induced by the land use/-cover change (LUCC). Existing models are struggling to comprehensively handle the spatiotemporal driving relationships amid the nonlinear LUCC process. Besides, the landscape patterns are not considered in most models, making them struggled to support the development strategies. Aiming at overcoming these obstacles, a novel land use/-cover change model concerning spatiotemporal dependency and properties related to landscape evolution (STAPLE) is proposed in this paper. A potential generating module establishing the nonlinear spatiotemporal driving relationship and a spatial allocating module employing a landscape-based CA are integrated for realistic LUCC simulations. As a case study, the proposed model is applied in Zhengzhou, China to assess its performance. It is indicated that the STAPLE model achieves a higher simulation accuracy, and the landscape properties are effectively manipulated. It provides a reproducible tool for policy-makers to explore a low-ecological-risk landscape under different future scenarios and achieve sustainable developments.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141050221","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 : 2024-05-10DOI: 10.1016/j.envsoft.2024.106070
Qinjun Qiu , Jiandong Liu , Mengqi Hao , Weijie Li , Yang Wang , Zhong Xie , Liufeng Tao
Multi-source heterogeneous, multi-modal, and multi-type open scientific data (e.g., thematic sharing sites, metadata, journal articles, etc.) on earth surface systems (EES) provide important data sources for knowledge mining, discovery, and accurate recommendations, and also pose increasing challenges, resulting in the need to develop appropriate tools to address these challenges and support decision-making. This paper constructs an interoperable software system to store, visualize, and publish open science data of ESS. Utilizing an open scientific data catalogue repository encompassing EES information as foundational input and employing an integrated modeling methodology, this system endeavors to synthesize heterogeneous surface data of diverse linguistic, sourced, and typological origins. The objective is to facilitate multidimensional data retrieval and precise data auto-recommendation, thereby fostering the dissemination of scientific data and facilitating value-added services within EES domain. The tool may be used by stakeholders including researchers, data analysts, policymakers and national authorities to support decision-making on questions ranging from locating the location of open data related to the topic, to discovering high-quality data, selecting the data with the better overall evaluation. Along with a description of the system/platform design process, its structure, and the constituent models, key results are presented relating to the user interface, and several application examples. Software systems can help modelers to use the best features of a single software tool to answer open scientific data-related questions that seek to discovery, use, comparison or synthesis within or across topics of ESS.
地球表面系统(EES)的多源异构、多模式和多类型开放科学数据(如专题共享网站、元数据、期刊论文等)为知识挖掘、发现和准确推荐提供了重要的数据源,同时也带来了越来越多的挑战,因此需要开发适当的工具来应对这些挑战并支持决策。本文构建了一个可互操作的软件系统,用于存储、可视化和发布 ESS 的开放科学数据。该系统利用包含 EES 信息的开放式科学数据目录库作为基础输入,并采用综合建模方法,致力于综合不同语言、来源和类型的异构地表数据。其目标是促进多维数据检索和精确的数据自动推荐,从而促进科学数据的传播和 EES 领域的增值服务。该工具可供研究人员、数据分析师、政策制定者和国家当局等利益攸关方使用,以支持有关问题的决策,包括查找与主题相关的开放数据的位置、发现高质量数据、选择综合评价较好的数据等。在介绍系统/平台设计过程、结构和组成模型的同时,还介绍了与用户界面有关的主要成果和几个应用实例。软件系统可以帮助建模人员利用单一软件工具的最佳功能来回答与开放科学数据有关的问题,这些问题旨在发现、使用、比较或综合 ESS 中或 ESS 跨主题的数据。
{"title":"An interoperable software system to store, associate, visualize, and publish global open science data of earth surface system","authors":"Qinjun Qiu , Jiandong Liu , Mengqi Hao , Weijie Li , Yang Wang , Zhong Xie , Liufeng Tao","doi":"10.1016/j.envsoft.2024.106070","DOIUrl":"10.1016/j.envsoft.2024.106070","url":null,"abstract":"<div><p>Multi-source heterogeneous, multi-modal, and multi-type open scientific data (e.g., thematic sharing sites, metadata, journal articles, etc.) on earth surface systems (EES) provide important data sources for knowledge mining, discovery, and accurate recommendations, and also pose increasing challenges, resulting in the need to develop appropriate tools to address these challenges and support decision-making. This paper constructs an interoperable software system to store, visualize, and publish open science data of ESS. Utilizing an open scientific data catalogue repository encompassing EES information as foundational input and employing an integrated modeling methodology, this system endeavors to synthesize heterogeneous surface data of diverse linguistic, sourced, and typological origins. The objective is to facilitate multidimensional data retrieval and precise data auto-recommendation, thereby fostering the dissemination of scientific data and facilitating value-added services within EES domain. The tool may be used by stakeholders including researchers, data analysts, policymakers and national authorities to support decision-making on questions ranging from locating the location of open data related to the topic, to discovering high-quality data, selecting the data with the better overall evaluation. Along with a description of the system/platform design process, its structure, and the constituent models, key results are presented relating to the user interface, and several application examples. Software systems can help modelers to use the best features of a single software tool to answer open scientific data-related questions that seek to discovery, use, comparison or synthesis within or across topics of ESS.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141031590","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 : 2024-05-10DOI: 10.1016/j.envsoft.2024.106071
Xuejian Li , Huaqiang Du , Fangjie Mao , Yanxin Xu , Zihao Huang , Jie Xuan , Yongxia Zhou , Mengchen Hu
Forest biomass is an essential indicator of forest ecosystem carbon cycle and global climate change research, and traditional machine learning cannot explain the mechanism of feature variable impact on forest aboveground biomass (AGB). Therefore, we proposed an interpretable bamboo forest AGB prediction method based on Shaply Additive exPlanation (SHAP) and XGBoost model to explain the impact mechanism of feature variables on AGB. The bamboo forest AGB is estimated using the monthly and annual scale leaf area index (LAI), enhanced vegetation index (EVI), ratio vegetation index (RVI), precipitation (Pre), maximum temperature (Tmax), minimum temperature (Tmin) and solar radiation (Rad) data. The results showed that the method could be effectively predict AGB, and precipitation more important than temperature. The framework revealed the threshold effect, exceeded the threshold value, the impacts of LAI_Ann, EVI_Ann, and Pre_11 on AGB were stable. The SHAP interaction value between LAI_Ann and EVI_Ann decreased with increasing EVI_Ann and LAI_Ann. By contrast, when Pre_11 increased, the SHAP interaction value between LAI_Ann and Pre_11 increased with increasing LAI_Ann. The framework could also be easily implemented, providing an interpretable machine learning model of forest AGB.
{"title":"Estimation aboveground biomass in subtropical bamboo forests based on an interpretable machine learning framework","authors":"Xuejian Li , Huaqiang Du , Fangjie Mao , Yanxin Xu , Zihao Huang , Jie Xuan , Yongxia Zhou , Mengchen Hu","doi":"10.1016/j.envsoft.2024.106071","DOIUrl":"10.1016/j.envsoft.2024.106071","url":null,"abstract":"<div><p>Forest biomass is an essential indicator of forest ecosystem carbon cycle and global climate change research, and traditional machine learning cannot explain the mechanism of feature variable impact on forest aboveground biomass (AGB). Therefore, we proposed an interpretable bamboo forest AGB prediction method based on Shaply Additive exPlanation (SHAP) and XGBoost model to explain the impact mechanism of feature variables on AGB. The bamboo forest AGB is estimated using the monthly and annual scale leaf area index (LAI), enhanced vegetation index (EVI), ratio vegetation index (RVI), precipitation (Pre), maximum temperature (Tmax), minimum temperature (Tmin) and solar radiation (Rad) data. The results showed that the method could be effectively predict AGB, and precipitation more important than temperature. The framework revealed the threshold effect, exceeded the threshold value, the impacts of LAI_Ann, EVI_Ann, and Pre_11 on AGB were stable. The SHAP interaction value between LAI_Ann and EVI_Ann decreased with increasing EVI_Ann and LAI_Ann. By contrast, when Pre_11 increased, the SHAP interaction value between LAI_Ann and Pre_11 increased with increasing LAI_Ann. The framework could also be easily implemented, providing an interpretable machine learning model of forest AGB.</p></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1364815224001324/pdfft?md5=ab46147be2322388548d568e47222815&pid=1-s2.0-S1364815224001324-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141024759","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}