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Dynamics of real-time forecasting failure and recovery due to data gaps: A study using EnKF-based assimilation with the Lorenz model 数据缺口导致的实时预报失败和恢复动态:基于 EnKF 的洛伦兹模式同化研究
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-22 DOI: 10.1016/j.envsoft.2024.106250
Sicheng Wu, Ruo-Qian Wang
Data assimilation-based real-time forecasting is widely used in meteorological and hydrological applications, where continuous data streams are employed to update forecasts and maintain accuracy. However, the reliability of the data source can be compromised due to sensor and communication failures or physical or cyber-attacks, and the impact of data stream failures on the accuracy of the forecasting system is not well understood. This study aims to systematically investigate the process of data stream failure and recovery for the first time. To achieve this, data gaps with varying lengths and timings are introduced to EnKF-based data assimilation system on the Lorenz model operating in both chaotic and periodic modes. Results show that the forecasting error grows exponentially in the chaotic mode but was limited in the periodic mode from the start of the data gap. For chaotic mode, the recovery of the system depends on the length of the data gap if the model error is not saturated; after saturation, the timing of the data stream recovery is important. Moreover, even long after restarting the data assimilation in the chaotic mode, the forecasting system cannot fully restore the original accuracy, while the periodic mode is generally resilient to disruption. This research introduces new metrics for quantifying system resilience and provides crucial insights into the long-term implications of data gaps, advancing our understanding of forecasting system behavior and reliability.
基于数据同化的实时预报广泛应用于气象和水文领域,利用连续数据流更新预报并保持准确性。然而,由于传感器和通信故障、物理或网络攻击等原因,数据源的可靠性可能会受到影响,而数据流故障对预报系统准确性的影响尚不十分清楚。本研究旨在首次系统地研究数据流故障和恢复过程。为此,在基于 EnKF 的数据同化系统中引入了不同长度和时间的数据间隙,这些数据间隙是以混沌和周期模式运行的洛伦兹模型。结果表明,在混沌模式下,预报误差呈指数增长,但在周期模式下,从数据间隙开始,预报误差就受到了限制。在混沌模式下,如果模型误差未达到饱和,系统的恢复取决于数据间隙的长度;饱和之后,数据流恢复的时间非常重要。此外,即使在混沌模式下重启数据同化很长时间后,预报系统也无法完全恢复原来的精度,而周期模式一般对中断具有很强的恢复能力。这项研究引入了量化系统恢复能力的新指标,并对数据缺口的长期影响提出了重要见解,从而推进了我们对预报系统行为和可靠性的理解。
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引用次数: 0
Identification of pedestrian submerged parts in urban flooding based on images and deep learning 基于图像和深度学习识别城市洪水中的行人淹没部分
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-19 DOI: 10.1016/j.envsoft.2024.106252
Jingchao Jiang , Xinle Feng , Jingzhou Huang , Jiaqi Chen , Min Liu , Changxiu Cheng , Junzhi Liu , Anke Xue
During urban flooding, pedestrians are often trapped in floodwater, and some pedestrians even fall or drown. The pedestrian submerged part (i.e., the human body part that water surface reaches) is an important reference indicator for judging dangerous situation of pedestrians. Flood images usually contain the information about pedestrian submerged parts. We proposed an automated method for identifying pedestrian submerged parts from images. This method utilizes relevant deep learning technologies to segment water surfaces, detect the pedestrians in floodwater, and detect the human keypoints of the pedestrians from images, and then identify submerged parts of the pedestrians according to the relationship between the human keypoints and the water surfaces. This method achieves an accuracy of 90.71% in identifying pedestrian submerged parts on an image dataset constructed from Internet images. The result shows that this method could effectively identify pedestrian submerged parts from images with high accuracy.
城市洪水泛滥时,行人往往会被困在洪水中,有些行人甚至会摔倒或溺水。行人的淹没部位(即水面到达的人体部位)是判断行人危险状况的重要参考指标。洪水图像通常包含行人淹没部位的信息。我们提出了一种从图像中自动识别行人淹没部位的方法。该方法利用相关的深度学习技术对水面进行分割,检测洪水中的行人,并从图像中检测行人的人体关键点,然后根据人体关键点与水面的关系识别行人的淹没部位。在由互联网图像构建的图像数据集上,该方法识别行人淹没部分的准确率达到 90.71%。结果表明,该方法能有效、高精度地识别图像中的行人淹没部分。
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引用次数: 0
Avoid backtracking and burn your inputs: CONUS-scale watershed delineation using OpenMP 避免回溯和烧毁输入:使用 OpenMP 进行 CONUS 规模流域划分
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-18 DOI: 10.1016/j.envsoft.2024.106244
Huidae Cho
The Memory-Efficient Watershed Delineation (MESHED) parallel algorithm is introduced for Contiguous United States (CONUS)-scale hydrologic modeling. Delineating tens of thousands of watersheds for a continental-scale study can not only be computationally intensive, but also be memory-consuming. Existing algorithms require separate input and output data stores. However, as the number of watersheds to delineate and the resolution of input data grow significantly, the amount of memory required for an algorithm also quickly increases. MESHED uses one data store for both input and output by destructing input data as processed and a node-skipping depth-first search to further reduce required memory. For 1000 watersheds in Texas, MESHED performed 95 % faster than the Central Processing Unit (CPU) benchmark algorithm using 33 % less memory. In a scaling experiment, it delineated 100,000 watersheds across the CONUS in 13.64 s. Given the same amount of memory, MESHED can solve 50 % larger problems than the CPU benchmark algorithm can.
针对美国毗连区(CONUS)尺度的水文建模,介绍了内存效率流域划分(MESHED)并行算法。为一项大陆尺度的研究划分数以万计的流域不仅计算量大,而且耗费内存。现有算法需要单独的输入和输出数据存储。然而,随着需要划定的流域数量和输入数据分辨率的大幅增加,算法所需的内存量也会迅速增加。MESHED 在处理输入数据时将其销毁,并采用节点跳转深度优先搜索,从而将输入和输出数据存储在同一个数据存储区,进一步减少了所需内存。对于德克萨斯州的 1000 个流域,MESHED 的运行速度比中央处理器(CPU)基准算法快 95%,内存占用率却低 33%。在一次扩展实验中,它在 13.64 秒内就划定了整个美国的 100,000 个流域。在内存容量相同的情况下,MESHED 可以解决的问题比 CPU 基准算法大 50%。
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引用次数: 0
A conceptual data modeling framework with four levels of abstraction for environmental information 环境信息四级抽象概念数据模型框架
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-18 DOI: 10.1016/j.envsoft.2024.106248
David Martínez , Laura Po , Raquel Trillo-Lado , José R.R. Viqueira
Environmental data generated by observation infrastructures and models is widely heterogeneous in both structure and semantics. The design and implementation of an ad hoc data model for each new dataset is costly and creates barriers for data integration. On the other hand, designing a single data model that supports any kind of environmental data has shown to be a complex task, and the resulting tools do not provide the required efficiency. In this paper, a new data modeling framework is proposed that enables the reuse of generic structures among different application domains and specific applications. The framework considers four levels of abstraction for the data models. Levels 1 and 2 provide general data model structures for environmental data, based on those defined by the Observations and Measurements (O&M) standard of the Open Geospatial Consortium (OGC). Level 3 incorporates generic data models for different application areas, whereas specific application models are designed at Level 4, reusing structures of the previous levels. Various use cases were implemented to illustrate the capabilities of the framework. A performance evaluation using six datasets of three different use cases has shown that the query response times achieved over the structures of Level 4 are very good compared to both ad hoc models and to a direct implementation of O&M in a Sensor Observation Service (SOS) tool. A qualitative evaluation shows that the framework fulfills a collection of general requirements not supported by any other existing solution.
观测基础设施和模型产生的环境数据在结构和语义上都存在很大的差异。为每一个新数据集设计和实施一个临时数据模型的成本很高,而且会给数据集成造成障碍。另一方面,设计一个支持各种环境数据的单一数据模型已被证明是一项复杂的任务,而且由此产生的工具无法提供所需的效率。本文提出了一种新的数据建模框架,可在不同应用领域和特定应用之间重用通用结构。该框架考虑了数据模型的四个抽象层次。第 1 层和第 2 层以开放地理空间联盟(OGC)的观测与测量(O&M)标准为基础,提供了环境数据的通用数据模型结构。第 3 级包含不同应用领域的通用数据模型,而具体的应用模型则在第 4 级设计,重复使用前几级的结构。为说明该框架的能力,我们实施了各种用例。使用三个不同用例的六个数据集进行的性能评估表明,与临时模型和在传感器观测服务(SOS)工具中直接实施 O&M 相比,第 4 层结构的查询响应时间非常出色。定性评估显示,该框架满足了一系列其他现有解决方案无法支持的一般要求。
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引用次数: 0
A Machine Learning-based framework and open-source software for Non Intrusive Water Monitoring 基于机器学习的非侵入式水监测框架和开源软件
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-18 DOI: 10.1016/j.envsoft.2024.106247
Marie-Philine Gross , Riccardo Taormina , Andrea Cominola
Recent research highlights the potential of consumption-based feedback for water conservation, emphasizing the need for Non Intrusive Water Monitoring (NIWM). However, existing NIWM studies often rely on small datasets, a pre-selected class of models, and inaccessible software. Here, we introduce PyNIWM, a machine learning-based open-source Python framework for NIWM. PyNIWM enables water end-use classification via (i) data characterization and feature engineering, (ii) water end-use event classification with four machine learning classifiers, and (iii) performance assessment. We demonstrate PyNIWM on a real-world dataset containing around 800,000 labeled end-use events from 762 homes across the USA and Canada. The four PyNIWM classifiers achieve F1 scores above 0.85, indicating high suitability for water end-use classification. However, a tradeoff between accuracy and computational cost exists. Finally, data balancing through oversampling enhances classification of low-represented end-use classes, but does not improve overall classification. We release PyNIWM as an open-source software, aiming for collaborative and reproducible research.
最近的研究强调了基于消耗量的节水反馈的潜力,强调了非侵入式水资源监测(NIWM)的必要性。然而,现有的非侵入式水监测研究通常依赖于小型数据集、预选的模型类别和不可访问的软件。在此,我们介绍 PyNIWM,这是一个基于机器学习的开源 Python 框架,适用于 NIWM。PyNIWM 可通过 (i) 数据特征描述和特征工程,(ii) 使用四个机器学习分类器进行水终端使用事件分类,以及 (iii) 性能评估来实现水终端使用分类。我们在一个真实世界的数据集上演示了 PyNIWM,该数据集包含来自美国和加拿大 762 个家庭的约 800,000 个标记的终端使用事件。四个 PyNIWM 分类器的 F1 分数都超过了 0.85,这表明它们非常适合水的终端使用分类。不过,在准确性和计算成本之间存在权衡。最后,通过超采样来平衡数据可以增强对低代表性最终用途类别的分类,但并不能改善整体分类效果。我们将 PyNIWM 作为开源软件发布,旨在促进合作和可复制的研究。
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引用次数: 0
Hydrogeological modelling of a coastal karst aquifer using an integrated SWAT-MODFLOW approach 利用 SWAT-MODFLOW 综合方法建立沿海岩溶含水层水文地质模型
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-17 DOI: 10.1016/j.envsoft.2024.106249
Gaetano Daniele Fiorese , Gabriella Balacco , Giovanni Bruno , Nikolaos Nikolaidis
The complexity of modelling in karst environments necessitates substantial adjustments to existing hydrogeological models, with particular emphasis on accurately representing surface and deep processes.
This study proposes an advanced methodology for modelling regional coastal karst aquifers using an integrated SWAT-MODFLOW approach. The focus is on the regional coastal karst aquifer of Salento (Italy), which is characterised by significant heterogeneity, anisotropy and data scarcity, such as limited discharge measurements and water levels over time.
The integrated SWAT - MODFLOW approach allows an accurate description of both surface and subsurface hydrological processes specific to karst environments and demonstrates the adaptability of the models to karst-specific features such as sinkholes, dolines and fault permeability. The study successfully addresses the challenges posed by the distinctive characteristics of karst systems through the integration of SWAT-MODFLOW. Additionally, incorporating of satellite data enhances the precision and dependability of the model by augmenting the traditional datasets.
The entire simulation period, which included both the calibration and validation phases, extended from 2008 to 2018. The calibration phase occurred between 2008 and 2011, followed by the validation phase between 2015 and 2018. The temporal choices were exclusively based on the availability of meteorological and hydrogeological data. During calibration, satellite data, previous study results, and groundwater level measurements were used to optimize the SWAT and MODFLOW models. Validation subsequently confirmed model accuracy by comparing simulated groundwater levels with observed data, demonstrating a satisfactory root mean square error (RMSE) of 0.22 m. Modelling results indicate that evapotranspiration is the predominant hydrological process, and excessive withdrawals could lead to a water deficit. Simulated piezometric maps provide crucial information on recharge areas and hydraulic compartments delineated by faults. The study not only advances the understanding of the hydrogeology of the specific case study but also provides a valuable reference for future modelling of karst aquifers. Additionally, it highlights the crucial need for ongoing enhancement in the management and monitoring of coastal karst aquifers.
岩溶环境建模的复杂性要求对现有的水文地质模型进行重大调整,尤其要强调准确表 现地表和深层过程。本研究提出了一种先进的方法,利用 SWAT-MODFLOW 综合方法对区域性 沿海岩溶含水层进行建模。该研究的重点是意大利萨兰托的区域沿海岩溶含水层,该含水层具有显著的异质性、各向异性和数据稀缺性,如有限的排泄测量数据和随时间变化的水位。SWAT-MODFLOW 集成方法可准确描述岩溶环境特有的地表和地下水文过程,并证明模型可适应岩溶特有的特征,如天坑、溶洞和断层渗透性。这项研究通过集成 SWAT-MODFLOW 成功地应对了岩溶系统的独特特征所带来的挑战。此外,卫星数据的加入还增强了传统数据集,从而提高了模型的精确度和可靠性。校准阶段发生在 2008 年至 2011 年,验证阶段发生在 2015 年至 2018 年。时间选择完全基于气象和水文地质数据的可用性。在校准过程中,利用卫星数据、先前的研究结果和地下水位测量结果来优化 SWAT 和 MODFLOW 模型。随后,通过比较模拟地下水位与观测数据,验证了模型的准确性,结果表明均方根误差 (RMSE) 为 0.22 米,令人满意。模拟压强图提供了有关断层所划定的补给区和水力分区的重要信息。这项研究不仅加深了人们对具体案例研究的水文地质的理解,还为今后岩溶含水层建模提供了宝贵的参考。此外,它还强调了不断加强沿海岩溶含水层管理和监测的重要必要性。
{"title":"Hydrogeological modelling of a coastal karst aquifer using an integrated SWAT-MODFLOW approach","authors":"Gaetano Daniele Fiorese ,&nbsp;Gabriella Balacco ,&nbsp;Giovanni Bruno ,&nbsp;Nikolaos Nikolaidis","doi":"10.1016/j.envsoft.2024.106249","DOIUrl":"10.1016/j.envsoft.2024.106249","url":null,"abstract":"<div><div>The complexity of modelling in karst environments necessitates substantial adjustments to existing hydrogeological models, with particular emphasis on accurately representing surface and deep processes.</div><div>This study proposes an advanced methodology for modelling regional coastal karst aquifers using an integrated SWAT-MODFLOW approach. The focus is on the regional coastal karst aquifer of Salento (Italy), which is characterised by significant heterogeneity, anisotropy and data scarcity, such as limited discharge measurements and water levels over time.</div><div>The integrated SWAT - MODFLOW approach allows an accurate description of both surface and subsurface hydrological processes specific to karst environments and demonstrates the adaptability of the models to karst-specific features such as sinkholes, dolines and fault permeability. The study successfully addresses the challenges posed by the distinctive characteristics of karst systems through the integration of SWAT-MODFLOW. Additionally, incorporating of satellite data enhances the precision and dependability of the model by augmenting the traditional datasets.</div><div>The entire simulation period, which included both the calibration and validation phases, extended from 2008 to 2018. The calibration phase occurred between 2008 and 2011, followed by the validation phase between 2015 and 2018. The temporal choices were exclusively based on the availability of meteorological and hydrogeological data. During calibration, satellite data, previous study results, and groundwater level measurements were used to optimize the SWAT and MODFLOW models. Validation subsequently confirmed model accuracy by comparing simulated groundwater levels with observed data, demonstrating a satisfactory root mean square error (RMSE) of 0.22 m. Modelling results indicate that evapotranspiration is the predominant hydrological process, and excessive withdrawals could lead to a water deficit. Simulated piezometric maps provide crucial information on recharge areas and hydraulic compartments delineated by faults. The study not only advances the understanding of the hydrogeology of the specific case study but also provides a valuable reference for future modelling of karst aquifers. Additionally, it highlights the crucial need for ongoing enhancement in the management and monitoring of coastal karst aquifers.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106249"},"PeriodicalIF":4.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Gated recurrent units for modelling time series of soil temperature and moisture: An assessment of performance and process reflectivity 用于模拟土壤温度和湿度时间序列的门控循环单元:性能和过程反映评估
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-15 DOI: 10.1016/j.envsoft.2024.106245
Maiken Baumberger , Bettina Haas , Walter Tewes , Benjamin Risse , Nele Meyer , Hanna Meyer
Soil temperature and moisture are important variables controlling ecological processes, but continuous high-resolution data are rarely available. Therefore, we used the correlation with widely accessible meteorological variables, including air temperature and precipitation, to develop models that predict time series of soil temperature and moisture. To model high-resolution time series, predictor and target variables had a temporal resolution of 1 h. We tested the applicability of Gated Recurrent Units with time series from one exemplary site. The models showed a high predictability on the four years test set with a mean absolute error of 0.87°C for soil temperature and 3.20% volumetric water content for soil moisture. We further investigated the plausibility of the models by passing simplified synthetic data to the trained models and thereby proved their ability to reflect known processes. Finally, we showed the potential to apply the models to other sites and soil depths using transfer learning.
土壤温度和湿度是控制生态过程的重要变量,但很少有连续的高分辨率数据。因此,我们利用与气温和降水等可广泛获取的气象变量的相关性,开发了预测土壤温度和水分时间序列的模型。为了建立高分辨率时间序列模型,预测变量和目标变量的时间分辨率为 1 小时。在四年的测试集中,模型显示出较高的预测能力,土壤温度的平均绝对误差为 0.87°C,土壤水分的体积含水量误差为 3.20%。通过将简化的合成数据传递给训练有素的模型,我们进一步研究了模型的可信度,从而证明了模型反映已知过程的能力。最后,我们展示了利用迁移学习将模型应用于其他地点和土壤深度的潜力。
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引用次数: 0
Toward reproducible and interoperable environmental modeling: Integration of HydroShare with server-side methods for exposing large-extent spatial datasets to models 实现可复制和可互操作的环境建模:将 HydroShare 与服务器端方法相结合,为模型提供大范围空间数据集
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-12 DOI: 10.1016/j.envsoft.2024.106239
Young-Don Choi , Iman Maghami , Jonathan L. Goodall , Lawrence Band , Ayman Nassar , Laurence Lin , Linnea Saby , Zhiyu Li , Shaowen Wang , Chris Calloway , Hong Yi , Martin Seul , Daniel P. Ames , David G. Tarboton
Reproducible environmental modelling often relies on spatial datasets as inputs, typically manually subset for specific areas. Yet, models can benefit from a data distribution approach facilitated by online repositories, and automating processes to foster reproducibility. This study introduces a method leveraging diverse state-scale spatial datasets to create cohesive packages for GIS-based environmental modelling. These datasets were generated and shared via GeoServer and THREDDS Data Server connected to HydroShare, contrasting with conventional distribution methods. Using the Regional Hydro-Ecologic Simulation System (RHESSys) across three U.S. catchment-scale watersheds, we demonstrate minimal errors in spatial inputs and model streamflow outputs compared to traditional approaches. This spatial data-sharing method facilitates consistent model creation, fostering reproducibility. Its broader impact allows scientists to tailor the method to various use cases, such as exploring different scales beyond state-scale or applying it to other online repositories using existing data distribution systems, eliminating the need to develop their own.
可重现的环境建模通常依赖空间数据集作为输入,这些数据集通常是针对特定区域的人工子集。然而,通过在线资源库促进数据分布的方法,以及促进可重复性的自动化流程,可使模型受益匪浅。本研究介绍了一种利用不同州级空间数据集创建基于地理信息系统的环境建模内聚数据包的方法。这些数据集是通过连接到 HydroShare 的 GeoServer 和 THREDDS 数据服务器生成和共享的,与传统的分发方法形成鲜明对比。与传统方法相比,我们使用区域水文生态模拟系统(RHESSys)横跨美国三个集水尺度流域,证明空间输入和模型流输出的误差极小。这种空间数据共享方法有助于创建一致的模型,从而提高可重复性。它的影响范围更广,科学家们可以根据不同的使用情况调整该方法,例如探索国家尺度以外的不同尺度,或利用现有的数据分发系统将其应用于其他在线资源库,而无需开发自己的系统。
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引用次数: 0
Distribution-agnostic landslide hazard modelling via Graph Transformers 通过图形变换器建立与分布无关的滑坡灾害模型
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-11 DOI: 10.1016/j.envsoft.2024.106231
Gabriele Belvederesi , Hakan Tanyas , Aldo Lipani , Ashok Dahal , Luigi Lombardo
In statistical applications, choosing a suitable data distribution or likelihood that matches the nature of the response variable is required. To spatially predict the planimetric area of a landslide population, the most tested likelihood corresponds to the Log-Gaussian case. This causes a limitation that hinders the ability to accurately model both very small and very large landslides, with the latter potentially leading to a dangerous underestimation of the hazard. Here, we test a distribution-agnostic solution via a Graph Transformer Neural Network (GTNN) and implement a loss function capable of forcing the model to capture both the bulk and the right tail of the landslide area distribution. An additional problem with this type of data-driven hazard assessment is that one often excludes slopes with landslide areas equal to zero from the regression procedure, as this may bias the prediction towards small values. Due to the nature of GTNNs, we present a solution where all the landslide area information is passed to the model, as one would expect for architectures built for image analysis. The results are promising, with the landslide area distribution generated by the Wenchuan earthquake being suitably estimated, including both zeros, the bulk and the extremely large cases. We consider this a step forward in the landslide hazard modelling literature, with implications for what the scientific community could achieve in light of a future space–time and/or risk assessment extension of the current protocol.
在统计应用中,需要选择与响应变量性质相匹配的合适数据分布或可能性。要从空间上预测滑坡群的平面面积,最常用的似然法是对数高斯分布。这就造成了一种限制,妨碍了对极小和极大滑坡进行精确建模的能力,后者可能会导致危险的低估危害。在此,我们通过图形变换器神经网络(GTNN)测试了一种与分布无关的解决方案,并实施了一个损失函数,该函数能够强制模型捕捉滑坡面积分布的大部分和右尾部。这种以数据为导向的危险评估方法的另一个问题是,人们通常会将滑坡面积等于零的斜坡排除在回归程序之外,因为这可能会使预测值偏小。鉴于 GTNN 的特性,我们提出了一种解决方案,即把所有滑坡面积信息都传递给模型,这也是为图像分析而构建的架构所期望的。结果很有希望,汶川地震产生的滑坡面积分布得到了适当的估计,包括零、大块和超大的情况。我们认为这是在滑坡灾害建模文献方面向前迈出的一步,对科学界未来根据当前协议进行时空和/或风险评估扩展所能取得的成果具有重要意义。
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引用次数: 0
Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management 将智能水文信息学纳入有效的早期预警系统,促进风险知情的城市洪水管理
IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-10-10 DOI: 10.1016/j.envsoft.2024.106246
Thanh Quang Dang , Ba Hoang Tran , Quyen Ngoc Le , Ahad Hasan Tanim , Van Hieu Bui , Son T. Mai , Phong Nguyen Thanh , Duong Tran Anh
The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. This underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.
城市排水系统一直面临着沿海和城市地区的洪水问题。稳健而准确的城市洪水管理,尤其是考虑到快速移动的复合洪水,对于最大限度地减少洪水灾害对沿海城市的影响至关重要。迄今为止,胡志明市(HCMC)还缺乏有效的城市洪水管理手段,原因是居民之间的洪水风险沟通。现有的洪水风险交流工具依赖于灾后洪水模型结果和数据。因此,本研究提出了一个实时城市洪水预警系统(EUFWS),该系统集成了用户友好的网络和应用程序界面。该系统的骨干包括利用机器学习(ML)算法开发的洪水模型,结合大数据和 Web-GIS 可视化,以 ML 作为构建 EUFWS 的核心。EUFWS 具有几个主要优势:随时可用、随时随地访问,并提供了一个实时、多用户的工作平台。此外,该系统还具有灵活性,可轻松添加组件和服务,并可根据工作量需求进行扩展。作为案例研究,EUFWS 已在越南 Thu Duc 市成功部署并有效运行。作为案例研究,EUFWS 已在越南 Thu Duc 市成功部署并有效运行。研究结果表明,EUFWS 支持决策者在城市洪水紧急情况下有效了解风险并做出明智决策。这突出表明,在全球易受洪水侵袭的城市中,集成 ML 和信息技术以加强智能城市排水系统管理的潜力巨大。
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引用次数: 0
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Environmental Modelling & Software
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