Environmental Modelling & Software最新文献

{"title":"Identification of pedestrian submerged parts in urban flooding based on images and deep learning","authors":"Jingchao Jiang ,&nbsp;Xinle Feng ,&nbsp;Jingzhou Huang ,&nbsp;Jiaqi Chen ,&nbsp;Min Liu ,&nbsp;Changxiu Cheng ,&nbsp;Junzhi Liu ,&nbsp;Anke Xue","doi":"10.1016/j.envsoft.2024.106252","DOIUrl":"10.1016/j.envsoft.2024.106252","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106252"},"PeriodicalIF":4.8,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529816","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}

{"title":"Avoid backtracking and burn your inputs: CONUS-scale watershed delineation using OpenMP","authors":"Huidae Cho","doi":"10.1016/j.envsoft.2024.106244","DOIUrl":"10.1016/j.envsoft.2024.106244","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106244"},"PeriodicalIF":4.8,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529743","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}

{"title":"A conceptual data modeling framework with four levels of abstraction for environmental information","authors":"David Martínez ,&nbsp;Laura Po ,&nbsp;Raquel Trillo-Lado ,&nbsp;José R.R. Viqueira","doi":"10.1016/j.envsoft.2024.106248","DOIUrl":"10.1016/j.envsoft.2024.106248","url":null,"abstract":"<div><div>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&amp;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&amp;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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106248"},"PeriodicalIF":4.8,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529742","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}

{"title":"A Machine Learning-based framework and open-source software for Non Intrusive Water Monitoring","authors":"Marie-Philine Gross ,&nbsp;Riccardo Taormina ,&nbsp;Andrea Cominola","doi":"10.1016/j.envsoft.2024.106247","DOIUrl":"10.1016/j.envsoft.2024.106247","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106247"},"PeriodicalIF":4.8,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571524","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}

{"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}

{"title":"Gated recurrent units for modelling time series of soil temperature and moisture: An assessment of performance and process reflectivity","authors":"Maiken Baumberger ,&nbsp;Bettina Haas ,&nbsp;Walter Tewes ,&nbsp;Benjamin Risse ,&nbsp;Nele Meyer ,&nbsp;Hanna Meyer","doi":"10.1016/j.envsoft.2024.106245","DOIUrl":"10.1016/j.envsoft.2024.106245","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106245"},"PeriodicalIF":4.8,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529817","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}

{"title":"Toward reproducible and interoperable environmental modeling: Integration of HydroShare with server-side methods for exposing large-extent spatial datasets to models","authors":"Young-Don Choi ,&nbsp;Iman Maghami ,&nbsp;Jonathan L. Goodall ,&nbsp;Lawrence Band ,&nbsp;Ayman Nassar ,&nbsp;Laurence Lin ,&nbsp;Linnea Saby ,&nbsp;Zhiyu Li ,&nbsp;Shaowen Wang ,&nbsp;Chris Calloway ,&nbsp;Hong Yi ,&nbsp;Martin Seul ,&nbsp;Daniel P. Ames ,&nbsp;David G. Tarboton","doi":"10.1016/j.envsoft.2024.106239","DOIUrl":"10.1016/j.envsoft.2024.106239","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106239"},"PeriodicalIF":4.8,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529741","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}

{"title":"Distribution-agnostic landslide hazard modelling via Graph Transformers","authors":"Gabriele Belvederesi ,&nbsp;Hakan Tanyas ,&nbsp;Aldo Lipani ,&nbsp;Ashok Dahal ,&nbsp;Luigi Lombardo","doi":"10.1016/j.envsoft.2024.106231","DOIUrl":"10.1016/j.envsoft.2024.106231","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106231"},"PeriodicalIF":4.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445081","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}

{"title":"Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management","authors":"Thanh Quang Dang ,&nbsp;Ba Hoang Tran ,&nbsp;Quyen Ngoc Le ,&nbsp;Ahad Hasan Tanim ,&nbsp;Van Hieu Bui ,&nbsp;Son T. Mai ,&nbsp;Phong Nguyen Thanh ,&nbsp;Duong Tran Anh","doi":"10.1016/j.envsoft.2024.106246","DOIUrl":"10.1016/j.envsoft.2024.106246","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106246"},"PeriodicalIF":4.8,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432620","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}

{"title":"Adapting OGC’s SensorThings API and Data Model to Support Data Management and Sharing for Environmental Sensors","authors":"Jeffery S. Horsburgh ,&nbsp;Kenneth Lippold ,&nbsp;Daniel L. Slaugh","doi":"10.1016/j.envsoft.2024.106241","DOIUrl":"10.1016/j.envsoft.2024.106241","url":null,"abstract":"<div><div>Software is critical in managing environmental sensor data. The Open Geospatial Consortium (OGC) developed the “OGC SensorThings API” (STA) standard to address variability across sensors, observed variables, platforms, and protocols, facilitating development of sensing and Internet of Things applications. This paper details a Python/Django implementation of the STA application programming interface (API) and a PostgreSQL/Timescale implementation of the STA data model, enhancing availability of robust software for management and sharing of environmental sensor data. STA offers a RESTful interface with JSON data encoding, aligning with modern development patterns and facilitating interoperability. Integration of metadata from the Observations Data Model ensures data can be adequately described and interpreted. STA’s flexibility allows lightweight query responses or comprehensive metadata inclusion, and a complementary data management API enhances use of STA within multi-user systems. Open-source code and deployment instructions in GitHub enable standalone or cloud deployments, enhancing accessibility and usability for researchers and practitioners.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"183 ","pages":"Article 106241"},"PeriodicalIF":4.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427017","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}